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Category: Digital Transformation

Align, Assemble, Assure: A Framework for AI Adoption

Align, Assemble
Assure

Executive Summary 

In the rapidly evolving landscape of artificial intelligence (AI), enterprises face significant challenges in differentiating their AI capabilities to achieve strategic objectives. This article provides a comprehensive approach to organizing internal structures across business, technology, and governance/risk/compliance domains to build a robust and differentiated AI capability. 

The first part of the article emphasizes the importance of Stakeholder Alignment. To ensure success, AI initiatives must align closely with the organization’s strategic objectives and core values. This alignment ensures that AI projects not only drive innovation but also resonate with the organizational ethos and mission. By integrating AI principles and strategies, enterprises can foster a culture that supports and accelerates AI adoption. 

The second section delves into the Operating Model, which is crucial for driving AI innovation and efficiency. This involves defining the ideal team structures and identifying the essential capabilities required across people, processes, and technology. A robust operating model includes a well-defined AI platform that supports strategic objectives and maximizes returns. It also emphasizes the importance of ideation, experimentation, and the productionalization of AI projects to ensure they deliver tangible business value. Effective resource allocation and management are key to maximizing the returns on AI investments. 

The third critical aspect covered is Governance, Risk, and Compliance. As AI technologies advance, so do the associated risks and regulatory requirements. It is imperative for enterprises to identify and manage potential risks linked to AI projects proactively. Establishing comprehensive compliance mechanisms ensures that AI applications adhere to all regulatory and ethical standards, thereby safeguarding the organization against legal and ethical pitfalls. 

To address these multifaceted challenges, this article introduces the Align | Assemble | Assure (AAA) Framework for AI adoption within enterprises. 

  1. Align: This phase focuses on ensuring that AI initiatives are in sync with the strategic objectives and core values of the enterprise. By developing a clear AI strategy and set of principles, organizations can provide a coherent direction for AI adoption that supports broader business goals. 
  2. Assemble: This phase is about building the necessary capabilities across the organization. It involves organizing teams effectively and ensuring the right mix of talent, processes, and technology. By developing an AI use case portfolio, roadmap, business case, and budget, enterprises can prioritize AI investments and drive projects that offer the highest return on investment (ROI). 
  3. Assure: The final phase focuses on risk management and compliance. This involves assessing AI risks comprehensively and ensuring compliance with internal policies and external regulations. Implementing an AI risk management framework and compliance measures ensures that AI initiatives are secure, ethical, and compliant with all necessary standards.

In the next section, we will explore in depth how the Blanc Labs AAA Framework for AI Adoption can be implemented using actionable and measurable steps.

Align

First, ensure that all AI initiatives are aligned with the company’s strategic objectives and core business values. This involves understanding the business landscape and determining how AI can solve existing problems or create new opportunities. By focusing on alignment, the enterprise ensures that every AI project drives meaningful impact and contributes positively to the overarching goals of the organization. 

Here’s how you can effectively execute this alignment:

Identify Strategic Objectives

Business Goals: Begin by clearly understanding the core goals of the organization. What are the key performance indicators (KPIs) or business outcomes that matter most? How can AI contribute to these areas? Thinking through this at the beginning of the process will help you make the right choices pertaining to resources, time and effort, resulting in the long-term success of your AI strategy.  

Value Alignment: Ensure that the planned AI initiatives resonate with the company’s values and culture. People are a consequential pillar in AI adoption. Aligning your goals and vision will establish a clarity of purpose, which will in turn drive employee motivation and participation. 

Stakeholder Engagement

Collaboration: Engage with stakeholders across various departments to gather insights and identify needs that AI can address. This includes executives, operational staff, and IT teams. AI can improve processes across the mortgage value chain, starting with lead generation and pre-approval at the loan origination stage, going all the way up to default management. Examples of how AI can add value include automating routine tasks to allow employees to concentrate on strategic activities, helping underwriters make complex decisions faster by analyzing research and data; using data to tailor customer experiences, which can boost sales, customer retention, and engagement; and developing comprehensive AI-driven services like chatbots or specialized products for small businesses, which can increase both new and existing revenue streams. 

Feedback Loops: Establish continuous communication channels to keep all stakeholders informed and involved in the AI integration process. This helps in adjusting strategies as needed based on real-world feedback and evolving business needs.

Market and Competitive Analysis

Benchmarking: Analyze competitors and industry standards to understand where AI can provide a competitive edge or is necessary to meet industry benchmarks. 

Innovative Opportunities: Identify gaps in the current market that AI could fill, potentially opening new business avenues or improving competitive positioning.

Risk Assessment and Mitigation

Identifying Risks: Part of alignment involves understanding the potential risks associated with AI deployments, such as data privacy issues, biases in AI models, or unintended operational impacts. 

Mitigation Strategies: Develop strategies to mitigate these risks upfront, ensuring that the AI initiatives proceed smoothly and with minimal disruption.

Scalability and Sustainability

Future-proofing: Consider how the AI initiatives align with long-term business strategies and technological advancements. Ensure that the solutions are scalable and adaptable to future business changes and technological evolution.

Regulatory Compliance

Legal and Ethical Considerations: Ensure that all AI deployments follow relevant laws and ethical guidelines, which is particularly important in industries like healthcare, finance, and public services. 

Assemble 

Next, assemble a dedicated cross-functional AI Center of Excellence (CoE). This centralized team should consist of experts in AI, data science, ethics, compliance, and business operations. The CoE acts as the hub for AI expertise and collaboration within the company, enabling the standardization of tools, techniques, and methodologies. It also facilitates the pooling of resources and knowledge, ensuring that AI projects across the organization benefit from a consistent approach and high levels of technical and ethical oversight. 

To delve deeper into the “Assemble” part of the “Align, Assemble, Assure” framework using the people, process, technology, and organization structure components, we need to consider how each of these elements supports the creation of a robust AI capability within an enterprise. 

People 

Assembling the right talent is critical. This includes hiring and nurturing: 

  • AI Specialists: Data scientists, machine learning engineers, and AI researchers who can develop and optimize AI models. 
  • Technology Experts: Cloud architects, cyber security engineers, solution architects, software developers and testers who can build and support AI applications 
  • Business Domain Experts: Project managers and business analysts who understand the specific challenges and opportunities within the industry and can ensure that AI solutions are relevant and impactful.
  • Support Roles: Risk, legal and compliance officers to oversee AI projects and ensure they align with business and regulatory requirements. 

 

Process 

Establishing clear processes ensures AI projects are executed efficiently and effectively: 

Development Lifecycle:  

Define a standard AI project lifecycle, from ideation and data collection to model training and deployment. Here is a detailed breakdown of the various stages of the project lifecycle, including challenges, solutions and how to measure impact: 

Evaluation and Planning: Define critical performance indicators to measure success. Detail both functional and non-functional requirements, outline the necessary output formats, develop a comprehensive monitoring strategy, and define AI literacy requirements for the system. Additionally, determine the necessary data inputs and set clear criteria for explanations. Select the appropriate Generative AI technology, determine how it will be customized, and outline a high-level support and accountability framework. Evaluate and address the risks associated with these activities proportionally, documenting significant effects and strategies for risk mitigation. 

Pro Tip: When setting up your evaluation framework, maintain a balance between technical precision and flexibility. This allows your team to adapt quickly to new insights or changes in technology without compromising the system’s integrity or performance. Regularly revisit and refine your performance metrics and requirements to ensure they remain aligned with your strategic goals and the evolving landscape of AI technology. 

Data Set Up: Access, clean, and transform data to ensure it is high-quality, well-understood, and relevant for the specific use case. Address data privacy, security, legal, and ethical concerns by putting in place robust safeguards and compliance measures for both the input and output of data, making sure that data owners have approved its use. Set up strict guardrails for the input of information to third-party generative tools and the output from the solution. This includes establishing processes for refining or filtering the output before it reaches the end user, and setting clear restrictions on how the output can be utilized. 

Pro Tip: Emphasize the importance of automation in cleansing and transforming your data. This not only saves time but also reduces human error, ensuring consistent data quality. Additionally, continually update and refine your data governance policies and generative AI guardrails to keep pace with technological advancements and evolving regulatory landscapes. This proactive approach will help maintain the integrity and security of your data, enhancing overall trust in your AI solutions. 

Development: Start by selecting the optimal tool that matches your requirements in terms of size, language capabilities, and pre-trained features, along with the most appropriate integration method, whether it’s an API or an on-premises solution. Customize your tool using various advanced techniques such as fine-tuning, which involves teaching the tool to perform new or improved tasks like refining output formats, and Retrieval Augmented Generation (RAG), which enhances prompt responses and outputs by incorporating external knowledge sources. Develop and refine a systematic approach to prompt engineering to create, manage, and continuously improve prompts. Focus on refining the solution to boost performance, accuracy, and efficiency through methods like adjusting parameters, tuning hyperparameters, improving data quality, conducting feature engineering, and making architectural adjustments. 

Pro Tip: Prioritize the scalability and adaptability of your tools and methods. As you refine and expand your AI applications, ensure that the tools you select can evolve with your needs and can integrate new features or data sources seamlessly. Regularly revisit your prompt engineering and customization strategies to keep them aligned with the latest advancements in AI technology, thus maintaining your competitive edge and maximizing the effectiveness of your solutions. 

Implementation and Management: Integrate the AI solution into the operational applications, ensuring seamless transition into production environments. Assign clear ownership to oversee the solution’s lifecycle. Develop a support structure that includes performance-based Service Level Agreements (SLAs) and operational guidelines aimed at fulfilling the established non-functional requirements, and introduce practices for consistent data management. Enhance organizational understanding and capability through targeted AI training programs. Ensure diligent registration of any new use of AI. Complete all the necessary documentation to provide clear explanations and transparency regarding the AI solution’s functionalities and decisions. 

Pro Tip: Establish a feedback loop between the operational performance and the development teams. This ensures that any insights gained from real-world application can be swiftly acted upon to refine the solution. Regularly updating your integration practices and operational protocols in response to these insights will help you maintain high standards of performance and reliability, ensuring that your AI system remains robust and effective in ever-changing environments. 

Monitoring and Maintenance: Implement continuous monitoring of the deployed AI solution to assess its performance, accuracy, overall impact (including any unintended biases), usage, costs, and data management practices. Promptly investigate and resolve any issues or anomalies that arise during operation. Utilize the insights gathered from the monitoring process to regularly update, maintain, and enhance the solution. Maintain vigilant oversight of any updates made to the AI system. Ensure all new applications of the AI are properly registered. Occasionally, updates pushed by the supplier might require a comprehensive review due to potential significant changes. Provide ongoing reporting to highlight the benefits and value added by the AI solution. 

Pro Tip: Consider implementing automated tools that can alert you to anomalies in real-time. Regularly scheduled reviews of the system’s outputs and operations can help preempt problems before they escalate, ensuring that the AI continues to operate efficiently and effectively. Moreover, documenting every adjustment and update not only aids in compliance and governance but also provides valuable historical data that can inform future enhancements and deployments. 

The Role of AI Quality Assurance and Testing

AI development demands rigorous and continuous testing. The role of Quality Assurance (QA) and Testing is to assess the relevance and effectiveness of the training data, ensuring it performs as intended. This process starts with basic validation techniques, where QA engineers select portions of the training data for the validation phase. They test this data in specific scenarios to evaluate not only the algorithm’s performance on familiar data but also its ability to generalize to new, unseen data. Evaluation metrics such as accuracy, precision, recall, and the F1 score are defined based on the specific use case, recognizing that not all metrics are appropriate for every scenario.

If significant errors are detected during validation, the AI must undergo modifications, similar to traditional software development cycles. After adjustments, the AI is retested by the QA team until it meets the expected standards. However, unlike other software, AI testing by the QA team does not conclude after one cycle. QA engineers must repeatedly test the AI with various datasets for an indefinite period, depending on the desired thoroughness or available resources, all before the AI model goes into production.

During this repetitive testing phase, also known as the “training phase,” developers should test the algorithm on various fronts. Notably, QA teams will need to focus not on the code or algorithm itself but on whether the AI fulfills its intended function. QA engineers for AI testing will primarily work with hyperparameter configuration and training data, using cross-validation to ensure correct settings.

The final focus is on the training data itself, evaluating its quality, completeness, potential biases, or blindspots that might affect real-world performance. To effectively address these issues, QA teams need access to representative real-world data samples and a deep understanding of AI bias and ethics. This comprehensive approach will help them pose critical questions about the AI’s design and its ability to realistically model the scenarios it aims to predict, ensuring robustness and reliability across various applications.

 

 

Technology

To deliver comprehensive and effective AI solutions, enterprises need to establish robust technology and tooling capabilities within their AI platforms. These capabilities span several domains, each critical for developing, deploying, and managing Gen AI, Cognitive AI and machine learning (ML) models.

Gen AI Development is foundational for creating full-featured generative AI applications. Enterprises must have the capability to customize and deploy models to meet specific business needs. This includes fine-tuning model inputs and engineering prompts to enhance performance and relevance. Additionally, managing the lifecycle of models during development is crucial, ensuring that models are continuously improved and updated. Orchestrating various AI models allows enterprises to integrate multiple AI systems seamlessly, optimizing the overall functionality and efficiency of the AI platform.

Gen AI and ML Operations focus on the day-to-day management and fine-tuning of AI models once they are in production. This involves managing the lifecycle of models and their versions to maintain consistency and reliability. Effective access management to models ensures that only authorized personnel can modify or utilize these models, enhancing security. Fine-tuning ML models and their training is essential to adapt to new data and evolving business requirements. Furthermore, managing data sources efficiently ensures that the models are fed with accurate and relevant data, which is vital for generating reliable outputs.

Gen AI and ML Governance is essential for maintaining the integrity and compliance of AI systems. Enterprises need to monitor models continuously to ensure they produce accurate and appropriate responses. Safeguarding models from inappropriate or malicious inputs is critical to prevent misuse and potential harm. Compliance with legal and regulatory frameworks is another significant aspect, ensuring that AI operations adhere to the necessary standards and regulations. Managing the data leveraged by models helps maintain data privacy and security, which is paramount in today’s regulatory environment.

Cognitive AI Capabilities are designed to mimic human cognitive functions, providing advanced interactions through APIs and SDKs. These capabilities include understanding and translating languages, recognizing images and sounds, and extracting and summarizing data. By integrating these cognitive functions, enterprises can enhance their AI applications to provide more natural and intuitive interactions, thereby improving user experience and engagement.

Establishing these comprehensive technology and tooling capabilities within an enterprise AI platform is crucial for developing robust, secure, and compliant AI solutions. By focusing on these areas, enterprises can harness the full potential of AI, driving innovation and achieving strategic business objectives.

Organization Structure 

Effective organization structure facilitates AI adoption and integration: 

  • Centralized AI Unit: An AI Center or hub, possibly under a Chief AI Officer, to centralize expertise and provide leadership. 
  • Cross-functional Teams: Integration of AI teams with other business units to promote collaboration and ensure AI solutions meet business needs.  
  • Change Management: Structures to support change management processes, helping the workforce adapt to new technologies and methods introduced by AI. 

Assure 

Finally, implement robust governance to assure the safety, compliance, and ethical integrity of AI deployments. This includes setting up frameworks for ongoing monitoring and evaluation of AI systems, ensuring they adhere to regulatory requirements and ethical standards. The governance process should also involve stakeholder engagement to maintain transparency and address any concerns related to AI projects. 

Responsible AI 

Responsible use of AI will require a commitment to ethical guidelines that prioritize: 

Transparency & Explainability: Create AI systems that are both transparent and clear in their functioning. As an organization, you should be able to pinpoint and clarify AI-driven decisions and outcomes. This will be especially important in the context of customers and stakeholders.  

Compliance: Designate responsible individuals for AI systems and ensure that the systems comply with regulatory standards. Conduct frequent audits to ensure the accuracy of your AI solutions, handle unforeseen outcomes, and check that the solutions fulfill legal and regulatory requirements. 

Equity and Diversity: Design, develop and deploy AI in an ethical, fair and inclusive manner. Proactively strive to identify and address biases that can emerge from training data or decision-making algorithms.  

Data Privacy: It’s imperative to safeguard personal data and prioritize individuals’ well-being, preventing any potential harm. Commit to respecting privacy rights, secure personal data, and mitigate potential risks and negative impacts on customers by obtaining consent and implementing responsible data practices.  

Security: Create AI solutions that can withstand attacks, malfunctions and manipulation. Implement safeguards to secure AI data, services, networks and infrastructure to prevent unauthorized access, breaches or data manipulation.  

Examples of Prohibited Use Cases: 

Using a risk assessment framework guided by the ethical AI principles mentioned above, here is an example of use cases that will not pass:  

  • Assessing, categorizing, or evaluating individuals based on their biometric information, personal attributes, or social behavior. 
  • Using subliminal, manipulative, or misleading techniques to sway an individual’s behavior. 
  • Taking advantage of people’s vulnerabilities (e.g., their age, disability, or social/economic status). 
  • Emotion recognition, as defined by your legal department.  
  • Collecting biometric data indiscriminately. 
  • Generating false content that seems to represent a particular person.

 

See Also: Artificial Intelligence and Data Act Canada

The proposed Artificial Intelligence and Data Act (AIDA) aims to establish standards for the responsible design, development, and deployment of AI systems, ensuring they are safe and non-discriminatory. This legislation will require businesses to identify and mitigate the risks of their AI systems, and to provide transparent information to users.

Under AIDA, the level of safety obligations for AI systems will depend on the associated risks, and businesses will need to adhere to new regulations across the design, development, and deployment stages. The government is working to create regulations that align with existing standards, aiming to facilitate compliance for businesses. A new AI and Data Commissioner will monitor compliance to ensure AI systems are fair and non-discriminatory. By introducing this law, Canada is among the first countries to propose AI regulation, aiming to balance innovation with safety, and ensuring international competitiveness while considering the needs of all stakeholders.

For more information, view the full act.

 

The diagram above shows how you can create your own AI risk assessment framework. Risk can be measured along four factors: personal information, decision-making, bias and external access.

In Conclusion

With the principle of “Align, Assemble, Assure,” the enterprise can methodically approach AI integration, ensuring that the efforts are strategically sound, well-supported by a specialized team, and maintained under stringent ethical and regulatory standards. This principle not only fosters innovation and efficiency but also builds trust and reliability in AI applications across the business.

Take the first step towards transforming your business with AI

Prithvi Srinivasan
Managing Director – Advisory Services

Prithvi Srinivasan, the Managing Director for Advisory Services, brings extensive expertise in technology strategy and digital transformation to drive mission-critical programs and advance financial institutions with AI and automation. His ability to transform complex strategies into successful transformations underscores his commitment to innovation and client value.

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Digital Transformation Vs. IT Modernization: What’s the Difference?

Technology | Digital Transformation | IT Management

Digital Transformation Vs. IT Modernization: What’s the Difference?

July 11, 2023
Digital Transformation

In today’s dynamic business landscape, organizations face numerous challenges such as economic volatility, supply chain complexities, evolving work styles, competing for talent, fickle consumer trends, market commoditization, sustainability concerns, and more. As stated in Forrester Research’s 2021 report, “In the 2020s, the one constant will be disruption.” CIOs play a crucial role in navigating these challenges and driving business transformation. The ability to embrace disruption will set leaders apart from laggards in this ever-changing landscape.

In this context, it is important for technology executives to make the choice between modernizing legacy processes or instituting a complete transformation of their systems. Yet, many organizations struggle to understand the difference.

In this article, we explore both digital transformation and modernization and why transformation is mission-critical for modern businesses.

Digital Transformation Vs. IT Modernization

Digital transformation has a broader scope and touches  almost every facet of a business. IT modernization’s scope is limited to upgrading existing systems.

Don’t get us wrong. Modernizing your tech stack  is just as critical to your business as transformation. The 2023 CEO Priorities survey shows that 51% of CEOs think low digital maturity/technical capabilities are barriers to creating business value with AI. As companies try to achieve digital maturity, they’ll go through multiple IT modernization iterations.

Let’s dig into the differences between digital transformation and IT modernization.

Digital Transformation in Practice

A digital transformation is meant to change or evolve some of the DNA of an organization such that a company is better equipped to compete in a constantly evolving  environment where customer expectations are changing at breakneck speed.  Technology can and should play a central role in a digital transformation but technology alone will not equip a company to operate effectively in this “new normal”, this requires a careful choreography of people, processes, and technology.

In addition to being better equipped to meet evolving customer needs and expectations, digital transformation can be your doorway to scaling your business by entering new markets, eliminating or minimizing process inefficiencies, and becoming more agile.

Domino’s is an excellent example of how digital transformation can change your company’s growth trajectory. Back in the day, the pizza chain was a brick-and-mortar restaurant that promised to deliver pizzas in under 30 minutes.

Competitors started offering a similar service. So Patrick Doyle, the CEO of Domino’s in 2011, decided to create an app that allowed customers to order pizza within seconds. Currently, Domino’s is testing delivery via autonomous vehicles to stay ahead of delivery services like DoorDash and Uber Eats.

Essentially, digital transformation involves a fundamental shift in various business aspects, like internal processes and your business model. The goal is to achieve sustained growth and become a more competitive business.

Modernization in Practice

Modernization is often confused with digital transformation, but they are not the same. Modernization focuses on building on top of existing systems. For example, you could implement cloud computing and AI without changing legacy processes.

Suppose you’re a bank that is looking to improve its mortgage origination process. You currently require staff to enter customer details manually. Over the next quarter, you want to implement an automated loan origination system that eliminates almost all the mundane tasks in the origination process.

For this, you’ll use technologies like AI, natural language processing (NLP), optical character recognition (OCR), and robotic process automation (RPA) through third-party integration or by building a new solution. These technologies will enable you to automate tasks like document processing and customer onboarding.

These technologies can help you re-engineer the origination process to make it more efficient—without disturbing your underlying mortgage process or the business model.

Why Digital Transformation Matters

Digital transformation is right for you if you’re in a rapidly evolving market and want to stay ahead of the competition.

Since digital transformation involves a more comprehensive change in your business’s model and operational processes, it can be resource and time-intensive, especially in the absence of first-hand experience on the multi-faceted nature of transforming an organization.  Let’s go back to our mortgage example to understand the difference between transformation and modernization.  Consider if the mortgage lender wanted to move from being a traditional, broker-based lender to a digital direct lender and the relative time, cost and complexity required to stand up a whole new business model.

IT modernization is relatively less resource and time-intensive. Modernization is an excellent place to start if you’re struggling to meet customer expectations or efficiency targets because of outdated technology.

Revamping your technological systems will help you reduce costs, operate more efficiently, and scale your business.

Digital transformation matters to two types of businesses:

  • Businesses that want to stay ahead of the curve by implementing the latest technologies in a fast-evolving market.
  • Businesses that need to survive in a market where other businesses failed to implement new technologies on time.

Digital transformation isn’t easy for either of these businesses to implement without an experienced team—only about 30% of companies navigate digital transformation successfully.

However, once you’ve successfully completed digital transformation, your business stands to benefit on multiple fronts.

The pandemic caused the most recent surge in digital transformation. Businesses were forced to adapt to an environment where employees prefer remote or hybrid work models, but they benefited greatly from the changes that followed.

That’s exactly why more businesses are excited about digital transformation. In fact, according to the World Economic Forum, the combined value of digital transformation to society and industry will exceed $100 trillion by 2025.

Here are some benefits of digital transformation that highlight why it can be a game changer for your business this year:

Improves Customer Experience

Customer expectations have risen significantly over the past decade. They prefer businesses that offer a great experience in all customer-facing aspects.

Enable  Data-Driven Decision Making

Data analytics is one of the most critical capabilities you gain from digital transformation. With modern tech, it is typically much easier to securely access and manipulate data that can be used to unlock valuable insights about customers, competitors, and internal processes.

Better Resource Management

Digitally-powered businesses use hundreds of apps and store data in multiple places. The lack of interoperability and data silos can make business hard. Digital transformation involves integrating these digital tools, allowing you to consolidate dispersed data sets and apps.

Improves Team Collaboration

Adding the right digital collaboration tools to your toolkit can be critical to building a collaborative environment in the workplace. They allow teams to collaborate outside the office space or country. If your team is spread across time zones, you can use async communication tools for better collaboration.

Increases Scalability

Digital transformation gives you the flexibility to grow faster. It can help reduce time to market and make product or service improvements faster. You can also serve clients in a larger geographic area and communicate with your clientele more effectively.

What Does it Take to Create Digital Transformation?

Digital transformation needs more than just technical expertise. It’s a massive undertaking where multiple factors contribute to your success.

Here are things to focus on when transforming your business digitally:

Digital First Culture

Transforming your business requires agility and an attitude to pursue the latest technologies proactively. Building a digital-first culture, where employees proactively look for opportunities to use digital solutions to help the business achieve its goals, is critical to successfully transforming your business.

Culture is a critical part of Gartner’s ContinuousNEXT approach to digital transformation. The Gartner experts who coined the term cite culture as a major barrier — 46% of CIOs identify culture as the most significant barrier to digital transformation.

A report by MIT and Capgemini explains that focusing on the following seven cultural attributes can be vital to building a digital-first culture:

  • Innovation: The prevalence of behaviors that support taking risks, thinking disruptively, and exploring new ideas.
  • Digital-first mindset: A mindset where everyone, by default, thinks of digital solutions first.
  • Customer-centricity: The use of digital tools to improve customer experience, expand the customer base, and co-create new products.
  • Open culture: The extent of partnership with third parties, including vendors and customers.
  • Agility and flexibility: Decision-making speed, dynamism, and your company’s ability to adapt to evolving market demands and technologies.
  • Data-driven decision-making: The use of data to make better decisions.
  • Collaboration: The strategic creation of teams to make the best use of the company’s human resources.

Change Management

Often, the most overlooked aspect of a digital transformation is the amount of change management and capability development required to enable team members to excel in their new or evolved roles. One takeaway that we’ve learned from our experience is the need to have a strong leadership commitment and a high level of conviction and alignment across the senior leadership team on the transformation objectives, priorities, and plan to undertake the transformation effort at an organization. .

When communicating a change, remember that the benefits of change may be evident to you. Yet, your team might see change as a signal to step out of a workflow they’re comfortable with and learn new skills.

Resistance will snowball when one employee who feels that way communicates with colleagues. Change management can help prevent or at least minimize this resistance to change.

Here are three critical elements of managing change:

  • Plan before you start: Change management should start before digital transformation. Risk analysis and assessing the organization’s readiness are great ways to lay the foundation for your change management strategy. They involve identifying organizational attributes like culture and characteristics of change.
  • Change management: This is where you go from planning to execution. You keep communicating your digital transformation plans, address concerns regarding the plan, and train employees.
  • Feedback: Change management doesn’t end after executing your change management strategy. Seek feedback from employees about the change. If you see reluctance, take corrective actions to ensure resistance doesn’t snowball.

Data-Driven Organization

Digital transformation is highly data-driven. Managing data — storing and processing data and ensuring data integrity — are one of the first things you’ll need to work on when you start the digital transformation process.

Data provides a unique insight into your operations. Once you’ve achieved a higher level of operational transparency and gained a deeper understanding of core business processes using data, you’ll be able to identify barriers to digital transformation. Data will also enable you to improve and optimize your transformation strategy continuously.

Companies like Airbnb and Amazon hold massive volumes of data. These companies are great examples of data-driven organizations. This data allows them to personalize customer experiences and stay one step ahead of the competition.

Emphasis on Quality

When you’re adamant about delivering quality, you’ll need to ask yourself — is the business prepared to ensure a high level of quality during the digital transformation journey?

Going all in at once is a bad idea. Digital transformation requires an incremental approach where you plan, test, and validate ideas every step of the way. This will allow you to stay prepared for potential pitfalls and optimize your strategy as you go. You’ll avoid complete disasters, which can result in loss of money, trust, and reputation.

Taking an incremental approach makes your digital transformation journey smoother and ensures you maintain high quality throughout the transformation process.

Begin Your Digital Transformation Journey

Digital transformation can be challenging to navigate without an experienced partner.

At Blanc Labs, we understand each organization has unique needs. We offer advisory and consulting services to gain a complete understanding of your most pressing challenges and help you start your digital transformation journey.

Book a discovery call with us today to learn more.

Navigating the Healthcare Interoperability Journey

Healthcare | Digital Transformation | Interoperability | IT Management

Navigating the Healthcare Interoperability Journey

May 18, 2023
Interoperability journey

The journey of navigating healthcare interoperability is a critical one, and an incredibly complex endeavor. Healthcare organizations must tackle big tasks like accessing exchange networks, mapping messages across systems, and integrating with multiple data sources while also operating within tight compliance rules. It can be especially daunting for executives tasked with ensuring successful implementation. If this describes you or someone on your team, don’t worry—there are ways to ensure success as you undertake the process of achieving healthcare interoperability.

In this article we explain what it means to embark on an interoperability journey and how best to implement it throughout an enterprise organization.

Stage 1: Strategy and Roadmap

Beginning the healthcare interoperability journey involves addressing key pain points, such as:

  1. A lack of common standards and communication protocols between existing health systems like Electronic Medical Record (EMR), Laboratory Information Systems (LIS), etc.
  2. Limited IT budgets and minimal underlying infrastructure
  3. A shortage of interoperability-focused resources

To tackle these challenges, the first step is to create a well-defined strategy, followed by a comprehensive gap analysis and a dynamic roadmap aimed at ensuring regulatory compliance and achieving seamless integration of healthcare data. A crucial aspect of this journey is addressing the CMS (Centers for Medicare and Medicaid Services) mandate that requires healthcare organizations to adopt and implement interoperability standards.

At this stage of the interoperability journey, organizations move from having disconnected data systems to making data organized and manageable. Applications of interoperability at this stage include patient-centered care.

The importance of securing curated and standardized health data

Securing curated and standardized data is crucial in ensuring that information is both organized and meaningful, particularly in the context of patient-centered care. By utilizing a data curation process, healthcare providers can effectively gather, annotate, and maintain relevant datasets that accurately represent patients’ medical histories, conditions, and preferences. This process often involves removing inconsistencies or inaccuracies, as well as integrating data from various sources into one unified platform. Standardizing this data in accordance with industry regulations or established protocols, such as the International Classification of Diseases (ICD) or Systematized Nomenclature of Medicine – Clinical Terms (SNOMED CT), enables seamless communication and the exchange of information.

Furthermore, the implementation of advanced security measures, including encryption and robust access controls, helps protect sensitive patient information from unauthorized access or potential breaches, adhering to privacy standards like the Health Insurance Portability and Accountability Act (HIPAA). Altogether, the rigorous curation, standardization, and security of data serve as foundational elements in the journey to interoperability.

Stage 2: Validating Strategy

The journey towards interoperability maturity is a complex and ongoing process, requiring healthcare organizations to regularly validate their strategies and roadmaps, align budgets with evolving business needs and technological advancements, and comply with CMS mandates. Achieving interoperability maturity involves focusing on core aspects such as:

  • Enabling seamless communication and coordination between disparate systems
  • Instituting a robust data management plan with accurate data mapping
  • Leveraging cutting-edge API technologies to address diverse use cases efficiently

Organizations must stay ahead of the curve by continuously assessing and refining their interoperability efforts with industry best practices and regulatory requirements as benchmarks. This iterative approach ensures that organizations can consistently drive improvements in care delivery, patient satisfaction, and long-term healthcare outcomes.

By this stage in the interoperability journey, companies graduate from simply having organized data systems to making them data analytics ready. This enables organizations to track patients over a longer period and participate in integrated healthcare.

Interoperability journey

Stage 3: Developing and end-to-end ecosystem for healthcare interoperability

The last stage in the healthcare interoperability journey is about addressing the alignment between business and technology objectives. A fundamental aspect of this stage is obtaining leadership buy-in, thereby empowering organizations to extend their interoperability initiatives beyond what is mandated by regulatory and industry requirements.

By focusing on enabling a comprehensive end-to-end solution for interoperability, organizations can leverage the potential of emerging standards, such FHIR, to facilitate seamless intra- and cross-organizational data exchange. Additionally, investing in the development of an Application Programming Interface (API)-driven ecosystem allows organizations to foster a highly connected, flexible, and scalable technology infrastructure that promotes innovative and improved patient-centric care services.

In the final stage of the healthcare interoperability journey, organizations are in the position to develop data driven applications (e.g predictive analytics, advanced reporting, population health management, etc.) using the latest technologies like AI to improve patient outcomes, enhance operational efficiency & increase profitability.

Get Started on your Healthcare Interoperability Journey with Blanc Labs

Interoperability can seem daunting, especially when trying to make sense of the entire journey. However, if approached methodically and incrementally with a well-thought-out strategy and roadmap, the end result can be a more secure, efficient, and user-centric system. Starting with developing the vision for an ecosystem that works for both you and your customers should give you confidence for making investments in interoperability technologies.

To understand how the interoperability journey will apply to your organization, simply speak to an expert from Blanc Labs today.

10 Tips to Successfully Implement RPA in Finance

Financial Services | AI | Banking Automation | Digital Transformation | Enterprise Automation

10 Tips to Successfully Implement RPA in Finance

April 14, 2023
RPA in Finance

Automation has taken the finance industry by storm, and for all good reasons. Banking automation technologies like Robotic Process Automation (RPA) come with the promise of streamlining processes and increasing efficiency.

According to Forbes, RPA has the potential to transform the way finance functions, from reducing manual errors to freeing up valuable resources. To help you maximize the benefits of RPA in finance, we’ve put together a list of 10 tips for successful implementation. But first, let’s take a step back and explore what RPA is.

What is Robotic Process Automation (RPA)?

RPA is the use of software robots to automate routine and repetitive tasks like document processing, freeing up employees to focus on strategic activities that can lead to better customer service or innovations that could meet customer expectations.

The bots are programmed to follow specific rules and procedures to complete a task, just like an employee. They can interact with various software applications and systems, such as spreadsheets and databases, to collect and process data. The bots can also make decisions, trigger responses, and communicate with other systems and software.

When a task is triggered, the bot performs it automatically, eradicating the scope of errors that may be produced through manual processes. The process is monitored and managed by a central control system, allowing adjustments and updates to be made as needed.

Think of RPA as a digital workforce, working tirelessly in the background to complete tasks that would otherwise take hours to complete. With RPA in finance, your financial institution can increase efficiency, reduce costs, and improve the accuracy of its processes.

The Use of RPA in Banking Automation

There are many ways in which RPA can be used in banking automation. Here are some of them:

Customer Service Automation

RPA is capable of automating routine customer service tasks such as account opening, balance inquiries, and transaction processing, allowing bank employees to focus on more complex customer needs.

Loan Processing

RPA in banking and finance can streamline the loan processing workflow by automating repetitive tasks such as document collection and verification, credit score analysis, and loan decision-making.

Fraud Detection and Prevention

In 2022 alone, banks and financial institutions lost $500K to fraud. Technologies like RPA can analyze vast amounts of data to detect and prevent fraud in real-time, improving the accuracy and speed of fraud detection. It may also report any suspected fraud to the banking authorities as soon as it is discovered.

KYC and AML Compliance

Verification and compliance document processing can eat up a major share of your institution’s resources. RPA can automate the process of collecting, verifying, and analyzing customer information, helping banks to comply with KYC (Know Your Customer) and AML (Anti-Money Laundering) regulations.

Back Office Operations

RPA can automate various back-office tasks such as data entry, reconciliation, report generation, monthly closing, and management reports, freeing up employees to focus on more strategic initiatives.

Payment Processing

Manual data entry in payment processing can lead to manual errors and longer processing time. RPA can automate the payment processing workflow, including payment initiation, authorization, and settlement, reducing the risk of errors and improving efficiency.

Risk Management

Banks and financial institutions are constantly at risk from various sources. RPA in finance can help institutions identify, assess, and manage risks by automating data collection, analysis, and reporting, improving the accuracy and speed of risk assessments.

Internal Compliance Monitoring

RPA can automate the process of monitoring and reporting on compliance with internal policies and regulations, reducing the risk of non-compliance and improving overall compliance management.

The potential of RPA in banking and finance is unlimited. When combined with Intelligent Automation technologies, RPA can leverage Artificial Intelligence (AI) and Machine Learning (ML) to provide more intelligent process automation. While the technology in itself is efficient, financial institutions must know how to implement it for maximized benefit.

10 Tips for Successfully Implementing RPA in Finance

According to the Deloitte Global RPA survey, 53% of organizations who took part in the survey have already begun their RPA implementation. The number is expected to rise to 72% over the next year. Entering the RPA race can be quick, but managing and scaling it is a different ball game. Before getting started with automation initiatives, it is important to consider the following tips.

Start Small

While RPA might seem useful to rejuvenate all of your systems and processing, it is important to consider the business impact and start small. Beginning with a smaller project, for example, a single process or department can help build momentum and demonstrate the benefits of RPA. It also allows the organization to gain experience and develop a better understanding of the technology before scaling up.

Define Clear Goals

The key to successful RPA implementation rests in your goals. Decide what you want to achieve with RPA in consultation with your IT department. Having a clear understanding of the goals and objectives of the RPA implementation will ensure that resources are allocated appropriately and that the project stays on track. Aim for specific, measurable, achievable, relevant, and time-bound (SMART) goals.

Involve Stakeholders

Engaging stakeholders, such as the management, finance employees, and IT, can help build better design and smoother change management for the RPA implementation. It also ensures that the RPA system is well-integrated across departments and addresses the needs and concerns of all stakeholders.

Assess Processes

When a financial institution leverages too many bots to automate processes, it results in a pile of data. They may be tempted to use ML on the data and create a chatbot to make customer queries easier. However, it can lead to  a poorly planned ML project. Thus, a thorough assessment of the processes is critical to ensure that the RPA implementation does not get sidetracked. This assessment should include an analysis of the tasks, inputs, outputs, and stakeholders involved in the process.

10 steps

Choose the Right Tools

Selecting the right RPA tools and technology is critical to the success of the implementation. Decide the mix of automation technologies your institution requires before reaching out to a vendor. Factors to consider include the cost, scalability, and ease of use , as well as its ability to integrate with other systems and applications.

Define Roles and Responsibilities

Clearly defining the roles and responsibilities of the RPA team, including project governance, testers, and users, is important to ensure that everyone knows what is expected of them. Remember that automation is a gradual process, and your employees will still need to interfere if it is not properly automated.

Ensure Data Security

Protecting sensitive data and customer information is a key concern in finance. RPA needs to be implemented in such a way that data security remains unaffected. Discuss with your vendor about strong security measures to ensure that data is protected and that the confidentiality of customer information is maintained.

Plan for Scalability

There are thousands of processes in banks and financial institutions that can use automation. Thus, the RPA implementation should be planned with scalability in mind so that the technology and processes can be scaled up as needed. This helps to ensure that the RPA can be combined with AI and ML technologies as necessary and the implementation remains relevant in the long term.

Monitor and Evaluate Performance

Continuously monitoring and evaluating the performance of the RPA bots is critical to ensure that they are operating effectively and efficiently. Also, do not forget to keep HR in the loop so that employees are informed and trained about the changing processes and how to use them in a timely manner. Regular evaluations should be conducted to identify areas for improvement and to make adjustments as needed.

Foster a Culture of Innovation

Encouraging a culture of innovation and experimentation can help to ensure that the organization is prepared for the future. Consult your IT department and automate your entire development lifecycle to protect your bots from disappearing after a major update. Invest in a center of excellence that can create business cases, measure ROI and cost optimization and track progress against goals.

Implementation Automation with Blanc Labs

At Blanc Labs, we understand that every financial and banking institution has unique needs and challenges when it comes to RPA implementation. That’s why we offer tailor-made RPA solutions to ensure seamless implementation for our clients.

Our RPA systems are designed to be flexible and scalable, allowing our clients to start small and grow as needed. We provide end-to-end support, from process assessment and design to implementation and ongoing assistance, to ensure that our clients realize the full benefits of RPA.

Booking a free consultation with us is the first step toward successful RPA implementation. Our team of experts work closely with each client to understand their specific requirements and goals to build a customized RPA solution to meet their needs.

What is API Management?

Financial Services | API Management | Digital Banking | Digital Transformation | IT Management

What is API Management?

March 15, 2023
What is API

Application Programming Interface (API) management has become an increasingly important aspect of modern business operations. With the advent of cloud computing and the need for digital transformation, enterprises are using APIs to enhance their existing applications, develop new applications, and drive innovation.

According to a study by Forbes, firms that used APIs saw 12.7% growth in their market capitalization over a period of four years. But using APIs is one thing and having an API strategy in place that can enable your business goals is another.

Proper management of APIs is imperative to support smooth business functioning. From startups to large enterprises, API management has become a critical component for businesses to remain competitive and meet the changing needs of their customers.

Whether you are a financial services provider looking to securely integrate third-party services, a retail giant seeking to improve your e-commerce platform, or a healthcare organization seeking to securely exchange medical data, API management can help you achieve your business objectives.

API Management Components

API management components are the building blocks that make up a comprehensive API management solution. These components work together to enable organizations to effectively manage their APIs and deliver value to their customers and partners. The primary components of API management include:

API Gateway

The API gateway is the component that sits at the front end of the API management architecture, acting as a traffic cop for incoming API requests. The API gateway is responsible for routing API requests to the appropriate backend services, applying security and access controls, and transforming data between different formats. The gateway also provides features such as caching, rate limiting, and request and response transformations.

Developer Portal

The developer portal is a  platform that provides developers with the information and tools they need to consume and build applications using your APIs. A good developer portal includes detailed documentation, code samples, forums, and tools for testing and debugging. The developer portal is a key component of API management as it helps to foster a community of developers who can help you drive adoption and engagement with your APIs.

Reporting and Continuous Improvement

Reporting and continuous improvement are essential components of API management as they help organizations understand how their APIs are used, identify improvement areas, and make data-driven decisions about their API strategies. With the help of real-time analytics and usage reports, organizations can track key metrics such as API request volumes, response times, and error rates. This information can then be used to continuously improve the API management process and deliver a better experience to developers and end-users.

API Lifecycle Management

API lifecycle management is the process of managing the entire life cycle of an API, from design and development to retirement and deprecation. This includes tasks such as versioning, testing, and publishing APIs, as well as managing security and access controls. API lifecycle management helps to ensure that APIs are managed in a consistent and organized manner, enabling organizations to respond quickly to changing business requirements and deliver value to their customers and partners.

Benefits of an API Management Platform

API management platforms provide a number of benefits to organizations that are looking to leverage APIs to drive innovation and growth. Some of the key benefits include:

Improved Security

APIs provide businesses with various benefits such as accessing enterprise services from different devices, promoting innovation, and creating new revenue streams. However, using APIs can also pose risks to data security, which is why it is crucial to protect them with an API manager. API management platforms are essential to ensure the security of APIs as they monitor their usage and implement security protocols such as JWT, OpenID and OAuth. Additionally, API management platforms can provide extra security benefits by controlling access to applications.

Increased Agility

API management platforms allow organizations to quickly and easily expose their existing systems and services as APIs. This enables organizations to respond quickly to changing business requirements and create new opportunities for growth and innovation. With the ability to easily manage and scale APIs, organizations can quickly and easily adapt to changing business needs.

A good example of this is the Emirates NBD Bank. In an interview with McKinsey, senior bank executives explained how they were able to achieve effectiveness and efficiency by shifting to APIs. “We have enabled several strategic business initiatives as a result. One example is our digital onboarding, which is available on mobile phones for self-service and via tablet for assistance in our branches. “We have onboarded more than 100,000 customers with our new process, doing up to 85 percent with straight-through processing in less than ten minutes,” said Neeraj Makin, group head of international and group strategy. Today, the bank offers more than 800 microservices and have seen over a million interactions in the last two years.

Improved Developer Experience

API management platforms provide a centralized location for developers to access and use APIs. With features such as detailed documentation, code samples, and testing tools, API management platforms make it easy for developers to consume and build applications using your APIs. This helps to drive adoption and engagement with your APIs, which can lead to increased revenue and more opportunities for innovation.

Better Monitoring and Analytics

API management platforms provide real-time monitoring and analytics capabilities, allowing organizations to track the usage and performance of their APIs. This information can be used to identify areas for improvement, optimize performance, and make data-driven decisions about your API strategy. With a better understanding of how your APIs are being used, you can make informed decisions about how to optimize your API offerings and deliver a better experience to your customers and partners.

Monetization Opportunities

API management platforms provide organizations with the tools and capabilities to monetize their APIs. With features such as billing, usage tracking, and rate limiting, organizations can set pricing and usage policies for their APIs, creating new revenue streams and driving growth.

Top Use Cases for API Management

The global API management market is expected to grow at a CAGR of 34.5% and reach $41.5 billion by 2031. API management has a wide range of use cases across various industries and sectors. A few of the major use cases are:

Digital Transformation Initiatives

API management is an essential component of digital transformation initiatives as it allows organizations to expose their existing systems and services as APIs. This enables organizations to quickly and easily create new applications and services, and drive innovation in a fast-changing digital landscape. With the ability to manage and scale APIs, organizations can respond quickly to changing business requirements and drive growth.

Open Banking

Open banking is an emerging trend that is transforming the financial services industry. With open banking, financial institutions can securely share their customer data with third-party providers, enabling new financial products and services to be created. API management is a critical component of open banking as it provides a secure and controlled environment for exchanging financial data, helping to ensure that customer data is protected and that transactions are compliant with regulatory requirements.

Read more: What is Open Banking and is it available in Canada?

Data Security

Data security is a critical concern for organizations in a wide range of industries. With API management, organizations can secure their APIs and the sensitive data they carry with features such as authentication, authorization, and encryption. This helps to protect sensitive information and ensures that data is transmitted securely, reducing the risk of data breaches and protecting the reputation of your organization.

Compliance

Compliance is an important consideration for organizations in regulated industries such as healthcare and finance. With API management, organizations can ensure that their APIs are compliant with regulatory requirements, such as the EU’s General Data Protection Regulation (GDPR) and the Payment Card Industry Data Security Standard (PCI DSS). This helps organizations minimize their risk of non-compliance and reduces the risk of costly penalties.

Custom API Management Solutions from Blanc Labs

At Blanc Labs, we understand the unique needs and requirements of our clients, and we offer custom API management solutions that are tailored to meet your specific needs. Our API management solutions are designed to provide enterprise organizations with the tools and capabilities they need to drive their digital transformation initiatives, secure their data, and ensure compliance with regulatory requirements.

If you are interested in learning more about the benefits of API management and how Blanc Labs can help you achieve your goals, we encourage you to book a discovery call with our team. Our experienced consultants will work with you to understand your needs and provide you with a customized solution that is tailored to meet your specific requirements.


Make the most of your API Strategy. Talk to an Expert.

What is Open Banking and Is It Available in Canada? 

Financial Services | Digital Banking | Digital Transformation | Lending Technology | Open Banking

What is Open Banking and Is It Available in Canada? 

February 9, 2023
Open Banking in Canada

If you’re a bank or financial institution, you should know what open banking is. Open banking consolidates your customers’ financial information. It allows them to access their financial data via a single banking or third-party app, allowing them to make smarter and faster financial decisions.

This guide explains open banking, how it works, and its benefits.

What Does Open Banking Mean?

Open banking is a secure framework that facilitates the exchange of financial data between financial institutions and third parties through APIs (application programing interface).

Think about the last time you wanted to check your investment portfolio. You probably had to log into multiple online portals and bank accounts to get financial information.

Open banking (also known as consumer-directed or consumer-led banking) can shorten this process to a few minutes by bringing all the information to a single dashboard.

When you try to access financial information via a fintech app with open banking, your bank transmits data via a secure online channel to the app. More importantly, you don’t need to provide login credentials when using open banking.

As you can imagine, being able to pull financial data securely from banks and other institutions will allow fintechs and the banks themselves to develop innovative products that enable Canadians to manage finances more effectively.

How Open Banking Works

Here is a typical scenario for how open banking works:

  1. A bank’s customer downloads a fintech app to manage finances and wants to start using it.
  2. The app needs to access financial data, so it prompts the customer to link their bank accounts.
  3. The customer authorizes the bank to securely share financial data with the app.
  4. The bank transmits customer data through a secured online channel.
  5. The app provides financial insights and recommendations.

Isn’t that how apps operate anyway?

Well, yes, except for one key difference. Traditionally, when a person links an app to their bank account, it uses screen scraping and the person’s login credentials to log into it and pull financial data.

On the other hand, open banking uses APIs (application programming interfaces). In simple terms, APIs allow two systems (the banking system and the third-party app, in this case) to communicate and exchange information securely.

Banks or financial institutions are responsible for building, implementing and managing the APIs that will allow customers to connect their accounts with new and innovative apps.

Banks need to find the right API management platform and be mindful of some common open banking API challenges. Alternatively, you can book a discovery call with us, and we’ll take care of the technical aspect of implementing open banking.

Screen scraping is prone to privacy and security risks since you can’t control how the fintech stores or uses your data. The practice can also violate your bank’s electronic access agreement (EAA), which frees the bank of any liability in case of an unauthorized transaction in your account.

Screen scraping will likely become obsolete once open banking becomes available in Canada.

What is Screen Scraping?

Screen scraping is the process of capturing data present on a screen — from a webpage, document, or app — for using it in another system or app.

It’s generally used by apps that need to extract data from legacy systems that lack an API management system or any other source of data extraction.

Data accessed by apps through screen scraping isn’t regulated. Without a standardized system, all third parties use their own level of security and approach to handling data.

Screen scraping platforms also store login credentials as text strings. The lack of encryption leaves your data vulnerable to attacks from hackers.

Unfortunately, an estimated four million Canadians are accessing banking-style services via screen scraping. The growing popularity of financial planning apps strongly calls for a more secure, regulated framework like open banking.

However, open banking still has its limitations. For example, the bank might securely transmit the information to a third party, but if someone hacks the app itself, they might steal your data. So even though open banking is safer than screen scraping, it’s not fully secure.

Benefits of Open Banking

The implementation of open banking in Canada will benefit both you and your customers.

Here are four ways open banking will benefit your customers:

  • Gives an overview: Open banking provides a secure framework to pull information from your bank accounts, credit cards, investment partners, and other financial institutions. An open banking app can consolidate all your financial data and provide a complete overview in one place without switching between websites and manually extracting information.
  • Allows shopping around for the best deal: Comparing deals for personal loans, credit cards, or mortgages requires careful research. A comparison app using open banking can speed up the process and help you find the best deal. Apps can also help you understand how likely you are to qualify if you apply for a loan based on your financial information.
  • Speeds up the application process: Applying for loans or credit requires submitting information, including your financial statements and KYC documents. Instead of manually submitting these documents, an app can store them for you and submit them as necessary when applying for a loan or opening an investment account.
  • Helps make smarter financial decisions: Fintech apps can use artificial intelligence (AI) and machine learning (ML) to create financial roadmaps based on your financial data. You can use these apps to create a budget, understand your spending habits, and find the best investment options based on your risk appetite. Apps may also project cash flows based on your budget and financial obligations so that you can estimate the available balance in your account at the end of each month.

Here are four ways open banking benefits you as a bank or financial institution:

  • Collaborate with third parties: Collaborating with third-party apps can help you explore data-sharing agreements and identify new opportunities. You can streamline processes and offer more related services to stay ahead of the competition.
  • Prepare for the future: Open banking isn’t available in Canada, but it soon will be. Over time, your customers will likely demand the privacy and security that open banking offers. As data privacy laws evolve, open banking will ensure you’re in an excellent position to adapt to changes. Moreover, quickly becoming compliant with evolving rules without interrupting service improves customer experience.
  • Increase market share: Your customers crave convenience. Allowing them to consolidate financial information securely ensures excellent customer experience. Offering open banking is critical to fulfilling your customer’s demands. Over time, you might even lose market share by not offering open banking.
  • Lower operating costs: Open banking ensures banks’ data lives in a centralized, digitally accessible location. This minimizes data silos and facilitates automation. Automating banking processes like mortgage underwriting allows you to reduce operating costs.

Read more: Open Banking in Canada: How Banks and Customers Can Benefit

Open Banking in Canada

While open banking is currently unavailable in Canada, it’s available in countries like the UK and Australia.

The Canadian government is working on safely implementing open banking in Canada. The government appointed Abraham Tachjian to lead Canada’s open banking framework development initiative in March 2022.

According to the Final Report of the Advisory Committee on Open Banking, the government had set a target to make open banking operational by January 2023, but implementation is still under process.

However, the government is committed to implementing open banking at the earliest and realizes the benefits it can offer to Canadians.

For example, when asked about how open banking can address the challenges facing BIPOC Canadians, small businesses, and rural/remote communities, Tachijan explained:

“While Canada’s banking framework aims to ensure all Canadians have access to basic bank accounts, some Canadians may be underbanked. Open Banking creates the opportunity for consumer-led banking, which gives consumers and businesses greater control and protection over their financial data, as well as more transparency on how it’s used.”

While the government lays the groundwork to implement open banking, you should ensure you have everything set up to offer customers open banking soon after it becomes available in Canada.

Is Your Financial Institution Ready for Open Banking?

Open banking is about to transform the financial services industry. Your customers will have the flexibility to choose how they interact with your bank, and your competitors will have the option to offer innovative solutions.

Implementing open banking can feel daunting, but partnering with the right team can simplify the process.

Blanc Labs helps banks implement open banking from scratch. Book a discovery call  to learn more about our open banking solutions.

Top Use Cases for Banking Automation

Financial Services | AI | Digital Banking | Digital Transformation | Enterprise Automation

Top Use Cases for Banking Automation

January 16, 2023
Top Use Cases for Banking Automation

Did you know that the World Bank uses Banking Automation including robotic process automation (RPA) for many of its functions? That’s how powerful it is.

Modern businesses rely on automation to reduce costs and improve efficiency, but how can banks use automation? In this article, we explain the most common use cases of banking automation.

What is Banking Automation?

Banking automation involves handing over repetitive business processes in financial institutions like banks and credit unions to technologies like robotic process automation (RPA), artificial intelligence (AI), and machine learning (ML).

Why Banks Need Intelligent Automation

As a banking professional, you know that a good chunk of your daily tasks is repetitive and mundane. Banking automation eliminates the need for manual work, freeing up your time for tasks that require critical thinking.

Here are seven reasons why banks needs intelligent automation:

  1. Better Customer Experience
  2. Automated KYC
  3. Faster Mortgage Application Processing
  4. Accurate Report Generation
  5. Anti-Money Laundering Prevention
  6. Audits and Compliance
  7. Faster Decision Making

Below we dive deeper into banking automation use cases so you can get a closer look at how automation can transform your workflow.

1. Customer Experience 

A robust Customer Experience has never been more important. As the world moves online, you’ll need to re-engineer your Customer Experience to make it friction free, faster and more efficient.

Consider a customer’s first experience with the customer onboarding process. If this isn’t a painless experience, you risk turning away a customer in the first interaction.

Sure, you might need to invest some money to improve the customer experience and make it seamless and efficient, but the potential ROI is excellent. Think about it. Automation will eliminate much of the manual and low-value in-person interaction, saving your sales reps plenty of time to focus on running effective sales campaigns.

Automating processes reduce the potential for errors, allowing you to onboard customers faster. Automation also reduces costs because it eliminates the manual labor and paperwork associated with customer onboarding.

Moreover, your customers will be able to use their accounts faster, which improves customer experience. As a McKinsey report explains:

“Automation and artificial intelligence, already an important part of consumer banking, will penetrate operations far more deeply in the coming years, delivering benefits not only for a bank’s cost structure, but for its customers.”

2. KYC

Most banks perform KYC (Know Your Customer) by manually verifying customer details. The problem? Manual verification can take plenty of hours.

The KYC process doesn’t end at verification. You must manage KYC documents for a long time to comply with regulatory requirements. Using automation in banking operations can help free up the hours you spend on manual verification.

Moreover, automation also eliminates the risk of human error. By eliminating room for error, automation ensures improved customer experience, increased quality assurance, and the number of cases processed each month, according to a McKinsey study.

3. Mortgage Application Processing

Mortgage application processing involves plenty of paperwork. Manually checking details on each document is time-consuming and leaves room for error. On the other hand, intelligent document processing (IDP) helps streamline document management.

Remember those desks full of paperwork? That’s a thing of the past. Modern banks use IDP to manage documents digitally. IDP helps automate the generation of customer risk profiles and mortgage document processing, reducing processing time to a few days.

An Accenture study found that 47% of customers prefer opening a new bank account online using a computer, while 37% prefer using the bank’s app or website.

The shifting consumer preferences point to a future where loan requests and processing are online and automated. Now is a great time to prepare your bank for that future.

4. Report Generation

All financial institutions need to generate reports for various purposes. For example, you might need to generate a report to show quarterly performance or transaction reports for a major client.

RPA can help generate reports automatically. The system can auto-fill details into a report and prepare an error-free report within seconds. An automated system can perform various other operations as well, such as extracting data from internal or external systems and fact-checking the reports.

5. Anti-Money Laundering (AML) Prevention

In Canada, banks need to ensure they are complying with the statutes of the Proceeds of Crime (Money Laundering) and Terrorist Financing Act, 2000. Depending on your location, compliance requirements might include ongoing risk-based assessment, customer due diligence, and educating staff and customers about AML laws.

A single AML investigation can take 30 minutes or more when assigned to an employee. However, automation can complete the same investigation much faster and minimize errors.

Using automation ensures you don’t spend too much money on AML investigations and stay compliant, so you don’t have to pay hefty fines.

6. Audits and Compliance

The cost of maintaining compliance can total up to $10,000 on average for large firms according to the Competitive Enterprise Institute.

Maintaining compliance is expensive but less so than being non-compliant. For example, banks must ensure data accuracy when producing loan facility letters. However, instead of requiring employees to spend time meticulously verifying customer data, you can use intelligent document processing to save time and guarantee data accuracy.

Banking automation can help you save a good amount of money you currently spend on maintaining compliance. With automation, you can create workflows that satisfy compliance requirements without much manual intervention. These workflows are designed to automatically create audit trails so you can track the effectiveness of automated workflows and have compliance data to show when needed.

For example, you can set up a system to auto-freeze compromised accounts. Once the account is frozen, RPA can automatically complete the steps in your fraud investigation process. The system also creates an audit trail in the process.

Unlike humans, RPA can be active 24/7. Using RPA in banking can help ensure the accuracy of compliance processes, ensuring you’re compliant at all times without investing a lot of human resources towards compliance.

7. Decision Making

Managers at financial institutions need to make decisions about marketing, operations, and sales, but relying on raw data or external research doesn’t provide full context. RPA can help compile and analyze internal data to track client spending patterns and preferences.

AI and RPA-powered automation can help make decisions about timing marketing campaigns, redesigning workflows, and tailor-making products for your target audience. As a result, you improve the campaign’s effectiveness, process efficiency, and customer experience.

For example, when introducing a mortgage loan product, you can use RPA’s data analysis capabilities to identify location-specific and borrower-specific risks. RPA can compile a summary of the risks on a document, allowing the credit manager to study risk profiles without the associated manual work.

Blanc Labs’ Banking Automation Solutions

Blanc Labs works with financial organizations like banks, credit unions, and Fintechs to automate their processes.

We can create tailor-made automation software solutions based on your banks’ needs to minimize manual work and improve process efficiency. Our team can help you automate one or multiple parts of your workflow using technologies like RPA, AI, and ML.

Book a discovery call with us to see first-hand how automation can transform your bank’s core operations. We’ll create an automation solution specifically for your organization that works in tandem with your current internal systems.

Top Use Cases of Intelligent Document Processing

Financial Services | Digital Transformation | Enterprise Automation | IDP | Lending Technology

Top Use Cases of Intelligent Document Processing

November 22, 2022
IDP

Currently, most enterprises have a workflow rampant with manual document-heavy processing.

However, businesses are quickly digitizing their document-processing workflows. 50% of B2B invoices across the globe will be processed without manual intervention according to a Gartner study. The reason? Manual document processing is more expensive than the cost of the documents themselves.

For example, the average cost of processing a single invoice was $10.89 in 2021. Manual document processing is also prone to human errors like fat finger errors. In a world where 90% of the data is unstructured, you need a tool that can automatically convert unstructured data into structured data to supercharge your productivity.

This guide explains how you can use intelligent document processing to save your business plenty of money, time, and resources.

What is Intelligent Document Processing?

Intelligent Document Processing (IDP) is a technology that automatically extracts unstructured data from multiple document sources, including images, online forms, and PDFs. IDP is also known as Cognitive Document Processing (CDP).

IDP converts this unstructured data into structured data using multiple technologies, including natural language processing (NLP), machine learning (ML), optical character recognition (OCR), and intelligent character recognition (ICR). Together, these technologies make IDP intelligent.

OCR is often used interchangeably with IDP. However, that’s not true. IDP uses OCR as one of the technologies to extract data.

How Does Intelligent Document Processing Work?

Here’s how a document is processed using IDP:

  • Conversion: An IDP platform starts by capturing your document through a scanning device. Once it converts a physical document into a digital one, it starts ingesting data.
  • Document image processing: The document’s image is processed for optimal OCR and archival.
  • Reading text using OCR: OCR helps the machine accurately read the scanned document’s text.
  • Identify language elements with NLP: IDP platforms use NLP to find language elements using methods like feature-based tagging and sentiment analysis.
  • Machine learning algorithm classifies information: A combination of machine learning and other techniques is used to classify the information in the document.
  • Extracting elements using AI: IDP uses AI to extract information elements like contact numbers, addresses, and names.
  • Validation: IDP platforms validate information using third-party databases and lexicons for data validation. Data points are flagged when the platform can’t validate them so someone from the team can review them manually.

Top Intelligent Data Processing Use Cases in Banking

Reading and writing financial documents make up a large portion of a bank’s workflow. As a bank, you need to process data fast to offer best-in-class services to your customers without making errors.

IDP helps banks guarantee accuracy and efficiency to their clients. In addition to data extraction’s key role in a bank’s workflow, banks can also use IDP platforms for fraud detection.

Here are some of the most common use cases of IDP for banks.

Mortgage Underwriting

Customer satisfaction with mortgage originators reduced by five points on a 1,000-point scale in 2021 according to a study by J.D. Power driven by record mortgage origination volume. Banks need to automate their mortgage workflow to scale as the demand grows. After all, customer satisfaction is one of the most significant differentiators in the mortgage industry.

The mortgage workflow involves collecting various documents. Extracting data from these documents is one of the major factors slowing down the workflow. This is where an IDP tool can help streamline your mortgage workflow.

An IDP tool helps you speed up the underwriting process with automation. It automatically reads and extracts relevant data and relays it to your bank’s credit evaluation system.

Claims Processing

The P&C Customer Satisfaction Survey reveals that the claim filing process is the biggest driver of customer satisfaction.

However, the claims processing workflow can be complex. Claims data comes in various formats—customers might send data as word files, PDFs, and images. Plus, you might receive the data via multiple channels—you might receive it via email, chat, or over a call.

Unifying this data without manual effort is a massive challenge. Traditionally, banks used OCR to process physical documents. However, the lack of accuracy required manual review.

An IDP tool is a great alternative to OCR for claims processing. Thanks to technologies like NLP, computer vision, and deep learning, it provides greater accuracy than traditional OCR.

Customer Onboarding

Customer onboarding is one of the most resource-intensive processes for a bank. Banks spend an average of $280 to onboard a single client according to Backbase—the cost can add up when you’re onboarding hundreds or thousands of customers every month.

Many of these expenses go towards processing documents, including the bank’s forms, credit reports, or tax returns. Sure, you can try automating this workflow. However, the automation will break down as soon as a new document type is introduced or you change your form’s template.

An IDP tool can help tame your customer onboarding costs. Your customers will appreciate a fast onboarding experience, and you’ll save money, increase productivity, and make an excellent first impression.

Financial Document Analysis

Banks handle thousands of financial documents every day. From financial statements to tax returns, carefully studying financial documents is critical to a bank’s operations.

Financial analysis is a cognitively heavy task. Why make your team spend time on mundane tasks like manipulating data when you can use an IDP tool to automate this process and enable your team to concentrate on their more complex deliverables.

Using an IDP tool helps analysts automatically structure and populate relevant financial data into their system. You’ll do your analyst team a favor by eliminating a lot of their manual work, allowing them to focus on analysis.

KYC Process Automation

KYC (Know Your Customer), Re-KYC, and C-KYC are critical for compliance. Banks might need to refer to a customer’s KYC details at various stages during a customer’s journey.

However, handling hand-written KYC forms is a hassle. Migrating a customer’s KYC data comes with challenges like human error and work overload. Committing errors when underwriting a mortgage or onboarding a customer costs money, but failing to comply with KYC requirements may increase the legal, compliance and regulatory risks.

Using IDP ensures accuracy, so you never have to lose your reputation and pay a fine for failing to comply with KYC norms.  The McKinsey KYC Benchmark Survey found that by increasing end-to-end KYC-process automation by 20%, an organization could enjoy the following positive outcomes:

  • Increased quality assurance by 13%
  • Improved customer experience (by reducing customer outreach frequency) by 18%
  • Increased the number of cases processed per month by 48%

The Bottom Line

Banks process a colossal amountnumber of documents and data each day. Getting new customer data into the system, processing claims, and analyzing financial statements are heavily data-driven tasks that involve dozens of documents from hundreds of customers.

The probability of committing errors is high. Banks also need a large team just to process documents and structure the data in those documents.

Banks need an IDP tool to automate this process and remove the risk of error from the process. It also integrates with applications to make migrating the data easier. An IDP tool also validates data and alerts team members in exception cases, when it requires a human to review accuracy.

It is important to select an IDP tool that offers the right solutions for your industry. Better yet, find a partner who can create a custom IDP solution tailor-made for you.

Why Choose Blanc Labs Intelligent Document Processing?

Blanc Labs partners with financial organizations like banks, credit unions, and fintechs to automate operations.

We can help you create robust automation solutions that minimize manual effort, reduce errors, and improve productivity. Our team helps you use the most advanced technologies including AI and ML to automate complex, resource-heavy processes like document processing.

Book a discovery call with us if your financial organization deals with plenty of documents daily. We’ll come up with a tailor-made solution to minimize the friction in your document processing workflow.

Innovation Is The Key To Digitally Transform Financial Services

Financial Services | Digital Transformation | Innovation | Leadership

Innovation Is The Key To Digitally Transform Financial Services

November 8, 2022

In a world where customers are becoming increasingly demanding, we need to find new ways of meeting their needs. The only way to do this is through innovation.

In this Business Ninjas podcast, our CEO, Hamid Akbari, talks about how Blanc Labs helps financial services organizations evolve their technology to meet new customer needs and make the most of market opportunities.

For more information on how Blanc Labs can help with digital transformation, get in touch.

Blanc Labs CEO Hamid Akbari Speaks On What It Takes To Scale Up Successfully

Technology | Digital Transformation | Leadership | Podcast

Blanc Labs CEO Hamid Akbari Speaks On What It Takes To Scale Up Successfully

October 7, 2022

You don’t have to be a #Fortune500 company to start thinking strategically about the future of your business. Technology services firms that commit to strategic methods and sustainable growth mechanisms early on can experience exponential profitability.

The way forward is not always clear, but technology services firms can take it one step at a time. Hiring great people, adapting technological breakthroughs, and staying connected with the needs of their customers all contribute to success.

In this podcast hosted by Collective 54, our CEO, Hamid Akbari, talks about what it takes to scale up a company in today’s competitive landscape.