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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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These are not your grandmother’s models: the impact of LLM’s on Document Processing

Insight | AI | Enterprise Automation | IDP

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January 22, 2024
These are not your grandmother’s models: the impact of Large Language Models on Document Processing

Document Processing before LLMs

Document processing primarily relies on rule-based systems and keyword matching, which can be effective for structured or even semi-structured documents with predictable formats. However, this approach often struggles with unstructured data, where variability and complexity are high. In contrast, Large Language Models (LLMs) bring a transformative approach to document understanding. They leverage advanced natural language processing (NLP) techniques, enabling them to comprehend context, semantics, and nuanced language variations in documents.

In the ever-evolving world of data science and enterprise automation, the explosive growth of unstructured data generated by companies has been a major challenge for data scientists. To give you a sense of scale, recent studies show we’re likely to witness a surge from 33 zettabytes in 2018 to a predicted 175 zettabytes by 2025. Furthermore, according to Gartner, unstructured data currently represents an estimated 80 to 90 percent of all new enterprise data.  Unstructured data can include conversations through e-mail or text messages, but also social media posts, blogs, video, audio, call logs, reviews, customer feedback, and replies in questionnaires. This trend spotlights an urgent need for more sophisticated tools to create value from this burgeoning data deluge.

Our team has over 5 years working with various OCR and NLP technologies, including having developed and training models in-house.  Don’t get me wrong, IDP  tech has come an extremely long way and the tools have gotten tremendously powerful. Libraries such as Amazon Textract (among many others) provide ML engineers a powerful suite of tools to accelerate the speed and quality of applying intelligent document processing to automation scenarios.

However, there are still limitations to how IDP can be adopted to a range of automation scenarios that we encounter in enterprise environments.

Think of traditional models document processing tech as a diligent yet somewhat myopic librarian, meticulously following rules but often missing the bigger picture. In contrast, Large Language Models (LLMs) are like Sherlock Holmes — insightful, context-aware, omnipresent, and adept at deciphering the most cryptic of texts.

This results in several key benefits and improvements:

Enhanced Comprehension

Traditional Method: Typically relies on keyword spotting and pattern recognition. For example, extracting dates or specific terms from structured forms.

LLMs Approach: Goes beyond mere pattern recognition. It interprets language nuances and intent, essential in contexts like financial and legal document analysis where the meaning of clauses and data can be complex.

Flexibility with Unstructured Data

Traditional Method: Struggles with documents like unstructured emails or reports, often leading to high error rates or the need for manual intervention.

LLMs Approach: Excel in handling unstructured formats. For instance, in customer service, LLMs can analyze and respond to diverse customer queries that vary in structure and content, easily extract information from employment letters or mortgage commitment statements.

Dealing with unstructured data, which includes everything from casual emails to social media chatter, videos, and customer feedback, is not a trivial matter. This kind of data resists neat categorization and defies traditional database structures, posing significant challenges in analysis and comprehension. Here’s where Large Language Models show their mettle, adeptly navigating this complex, non-uniform data and unlocking valuable insights that conventional methods might miss.

Adaptive Learning

Traditional Method: Updating rule-based systems for new formats or languages is time-consuming and resource-intensive.

LLMs Approach: Can continuously learn from new data, adapting to changes in language usage or document formats without extensive manual reprogramming.

Error Reduction

Traditional Method: Prone to errors in cases of ambiguous or context-heavy information, resulting in lower reliability.

LLMs Approach: Their deep contextual understanding leads to more accurate data extraction and interpretation, crucial in high-stakes industries like legal, financial and healthcare.

A Practical Example

ChatGPT 4, without any specific fine tuning or pre-training is able to easily extract information from a document it has never seen before. It understands the context and you can simply query in a natural way for data points that you are interested in:

*Chat gpt extracts information_Blanc Labs

* Note: we take data privacy and PII seriously (see below) and created “spoof” documents for the purposes of this demonstration.

For instance, by asking direct questions such as ‘What is the policy end date?’ or ‘By what margin has the insured amount varied?’, it promptly delivers precise information with perfect accuracy.

This scenario offers the opportunity to further explore solutions that leverage the unique capabilities of LLM’s for the purposes of intelligent document processing and automation.

Generative AI-Powered Extraction and Comparison of Insurance Policy Documents

In the previous section, we delved into the capabilities of Language Learning Models (LLMs) in streamlining the extraction of key information from various document types. To demonstrate this further, we now present a practical application of our system.

The first part of the demonstration involves uploading the initial insurance policy, which acts as our benchmark document. Watch how the system seamlessly processes this document, effortlessly extracting critical details such as the policy number, coverage specifics, the insured party’s information, and other essential data.

Next, we upload a second document representing a modification in the policy. It not only extracts pertinent information from the new document but also conducts an intelligent comparison with the original policy. Notice how the system highlights the changes in the date and insurance limit. This comparative analysis is vital to ensure comprehensive and accurate updates of all modifications and their implications.

To enhance the efficiency of such systems, integration with existing databases and cloud storage services is key. Utilizing APIs, these systems can automatically retrieve documents from various sources such as cloud storage (like AWS S3, Google Cloud Storage), internal databases, or even directly from email attachments. This integration enables real-time processing and updates, ensuring that the latest documents are always analyzed and compared.

The Role of Retrieval-Augmented Generation (RAG) in LLMs

For more context specific answers and solutions, Retrieval-Augmented Generation represents a significant advancement in the capabilities of LLMs. It’s another step forward on this never-ending roller coaster!

  • Enhanced Accuracy and Relevance: RAG combines the generative power of LLMs with information retrieval, pulling in relevant data or documents to provide contextually accurate responses. This is particularly beneficial for financial analysis and reporting, where accuracy is paramount.
  • Dynamic Data Integration: Unlike traditional LLMs, RAG can integrate real-time data, offering dynamic responses to financial queries. This is essential in finance, where market conditions and regulatory environments are constantly evolving.
  • Customized Financial Advice: RAG’s ability to retrieve and process vast amounts of data allows for highly personalized financial advice, tailored to individual customer profiles and market conditions.
  • Improved Compliance and Risk Management: In the regulatory-heavy landscape of financial services and healthcare industries, RAG can efficiently process and cross-reference internal and external data sources including detailed regulations and requirements. We believe that there is a huge opportunity to automate regulatory, risk, and compliance checklists to reduce complex manual efforts that exist in regulated industries.

Potential limitations of deploying LLM’s for document processing (at scale)

While LLMs are highly likely to revolutionize document processing with,  it is important to consider potential limitations as well. LLMs are like a double-edged sword, powerful in processing vast amounts of data but requiring careful handling to address privacy concerns and manage computing resources.

  • Privacy and Personal Identifiable Information (PII): LLMs are capable of processing vast amounts of data, including confidential or proprietary information as well as PII data.  Organizations must work within data security frameworks and engineer solutions that have data security at the core of how they are designed and deployed.  As a SOC2 Certified company, this is an area of key focus for our teams and we have implemented robust data handling and processing protocols to ensure that all PII is managed securely and in compliance with privacy regulations.
  • Computing Power and Cost: The potential impact offered by powerful LLMs to read and extract data from large volumes of unstructured data is reliant on the on substantial computing power required to run these models.  We are still in the early stages of enterprise adoption of generative AI technologies and the economics of leveraging these toolsets at an enterprise scale are fairly dynamic.  We expect a lot to change over the next few years but in the meantime, we are actively working with clients to understand the business drivers of using LLM’s to automate processes.  With our deep background in intelligent document processing we’ve become experts at crafting solutions that optimize model efficiency without compromising performance. We employ techniques like model pruning, efficient data processing pipelines, and cloud-based solutions that balance computational demands with cost-effectiveness.

Conclusion

LLMs and RAG are the vibrant threads bringing new patterns of efficiency, accuracy, and innovation. We’ve journeyed from the meticulous yet narrow pathways of traditional methods to the expansive highways of AI-driven solutions. This evolution isn’t just a step forward; it’s a quantum leap into a future where data isn’t just processed but understood, where advice isn’t just given but tailored, and where compliance isn’t just followed but mastered.

The advancements in unstructured data analytics signal a critical shift in our approach to data. It’s not just about the volume; it’s about the untapped potential that lies within. This raises a compelling question: how can we leverage unstructured data to gain a deeper understanding of our customers, societal trends, and the world at large? The key lies in harmonizing cutting-edge AI tools like Large Language Models with human insight, transforming this wave of data into insightful and actionable knowledge.

Harness the power of AI

At Blanc Labs, we specialize in tailoring AI solutions to the specific needs of the Canadian financial and healthcare sector. Our expertise in AI, automation & digital product development positions us to assist in harnessing the power of LLMs and other AI technologies. We provide customized solutions for intelligent document processing, intelligent automation, and enhancing customer experiences, ensuring compliance with industry standards and regulations.

Explore how Blanc Labs can assist your organization in navigating and succeeding in the digital era.

Author

Luciano Lera Bossi
BPI & Intelligent Automation Lead

Luciano Lera Bossi is a skilled Engineer with 15+ years of success in tech, specializing in Intelligent Automation, Low Code/No Code and Agile Project Management. He enables effective communication between technical and business stakeholders, resulting in seamless project outcomes.

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The Keys to the Secret Garden: Unlocking the Potential of AI in the Enterprise

Financial Services | AI | Enterprise Tech | Generative AI

The Keys to the Secret Garden: Unlocking the Potential of AI in the Enterprise

November 27, 2023
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At this point, enterprise generative AI applications for  software development, marketing, customer/employee service , and product design are proliferating as the standout use-cases early adopters are focusing on. From our position working with clients to develop and enable enterprise data + AI solutions, we’ve gained perspective on several of the foundational characteristics enterprises will need to develop to realize on the promise of this 3rd wave of the human-computer revolution.

Even though it is still early days we are very enthusiastic about the prospects of LLMs and powerful ML capabilities to unlock vast economic value and growth for progressive enterprises. Some are estimating an impact of $1 trillion, or 4% of GDP, in the U.S. alone and the generative AI market is expected to see rapid growth, rising from 11.3 billion USD in 2023 to 51.8 billion by 2028.

But First, a Disclaimer

Blanc Labs focuses on the financial services and healthcare industries. We see both sectors as having massive potential in terms of the benefits offered through the application of these transformative technologies.  Both healthcare and financial services are hugely complex, highly regulated, and depending on who you speak to, resistant to change. We believe the stage is set for a broader divergence of winners and losers: organizations that build capability and competency in adopting new tools and leverage the power of their enterprise data will stand to reap the benefits of their investments.

So with all the hype surrounding generative AI and the rapid advancement of a field that feels to moving forward at the speed of light, it is important to understand why some organizations will be better suited to create value in this field than others, and what organizations can do to ensure success.

⛔ Cloud Infrastructure Only ⛔

AWS Enterprise Strategist and Evangelist, Phil Lebrun is a pretty smart guy. In one of his posts about the topic, his flagship advice is “Don’t try this without the cloud.” You want your teams focused on problem-solving and innovation, not on managing the underlying complexity and cost of enabling infrastructure and licenses. The cloud is the enabler for adopting AI, making available cost-effective data lakes, sustainably provisioned GPUs and compute, high-speed networking, and consumption-based costing.

Enterprise Platforms are a Lynchpin

If we consider that having completed a cloud migration or cloud native approach is table stakes for AI  enablement within an organization, the next big question will be where your enterprise data resides. The organization’s technology infrastructure and enterprise platform ecosystem will in many ways determine what toolsets may be available to facilitate experimentation and development of AI enabled use cases.

If you are a CIO, CTO or CDO, start by taking stock of the platforms that house your enterprise data and evaluate the toolsets offered by these power technology-ecosystem juggernauts. Microsoft and AWS are emerging as clear leaders in terms of providing their customers powerful tools to apply ML and AI to their enterprise data, but Salesforce, SAP, Pega and OpenText – amongst many others will offer tooling to access, manage and apply AI to enterprise data.

It is impossible to predict who the big winners and losers of this space will be, partially because they are such massive and well entrenched players but keep an eye on this space over the next few years.

The 👿 is in the Data [Taxonomy]

Building an enterprise data model across a complicated operating environment like a bank is no small feat. Standardizing data definitions between different lines of business in a bank involves tackling the complexity of enterprise (+legacy) platforms used to store and handle data. This complexity extends to accommodating the diverse needs of various stakeholder groups with distinct preferences for accessing, manipulating, and analyzing data.

In many ways, risk is a unifying theme and responsibility within regulated industries like the banking sector. We see conformance to risk measurement and regulatory reporting requirements as a lightning rod for action amongst FI’s to get their “data house” in order but proactive approaches amongst banking and technology executives are few and far between.

In a recent interview with Tech Exec magazine, VP of Data and Adaptive Intelligence at Munich Re Canada Branch (Life), Lovell Hodge encapsulated the shift in how progressive organizations are thinking about their data:

“Over the years, there has been a realization that data has an inherent information value to the organization. As the years go by, we’re rapidly producing more data at an increasingly and somewhat alarming rate. So, what a lot of folks in the industry have realized is that there is not a huge space between theoretical concepts around information from data, and how they can use that information to simplify internal processes and also benefit clients.
So, you’re seeing that interdependence starting to mature. And because of that, many organizations are looking at their data not as just something they have to store, but as an asset they can leverage. And once you start thinking in that sort of way, then you start to formulate methods by which you can gain value from data, and it becomes an important part of your corporate strategy.”

App Design and Development Now Occurs ‘In the Data’

Historically, software design and development occurred in a sandbox that was devoid of enterprise data. In traditional dev environments we piped in dummy data to test parameters and during QA tested connectivity and security. But with the explosion of sophisticated ML and AI tools, application development will very much occur at the nexus of querying, manipulating, analyzing and visualizing proprietary data sets.

There is a tremendous amount of opportunity for these new tools to democratize data for business users through the use of conversational/natural language query tools to conduct analysis and derive insights from enterprise data sets.

Another big change will be the blurring of the lines between the role of developers and data scientists and in many cases, these resources will be working directly alongside each other.  Technical resources will require access to live data sets to facilitate model training and to leverage powerful data analytics toolsets. This is going to require a significant shift (from reactive to proactive) in terms of access management and data security.

Firms that work with external partners will require them to adhere to an extremely high standard of data security protocols i.e., ISO and SOC 2 compliance certification with regular audits.

Specific sector, domain, and knowledge of business context has always counted for a lot but increasingly developers won’t be able to rely on detailed functional, technical and design requirements documentation to define the specs of what they are developing.  It will require a much more experimental, test and learn approach.  Toolsets are only going to get more user friendly to work with but developing high impact apps leveraging advanced AI/ML techniques will require a detailed and nuanced understanding of user needs, workflow, data inputs, command prompts, prompt modelling strategies, and a continuous improvement approach to get to a place of lasting and tangible impact for businesses and teams.

In Summary:

There is much still to be learned through this large-scale adoption of a suite of technologies that are evolving at such a rapid pace. This is by no means a complete list of the things business teams and leaders need to consider as they look to capture competitive advantage from these powerful new tools. At Blanc Labs, we are excited about the future and look forward to working with companies to develop their capabilities to create business value by harnessing the tremendous potential of their enterprise data.

About the Author

David Offierski
VP Partnerships & Marketing

With over 15 years in technology consulting services, I am thrilled at how next-gen technologies will unlock a new wave of innovation and productivity for enterprises.

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Blanc Labs Welcomes Two New Leaders to Advance AI Innovation and Enhance Tech Advisory Services for Financial Institutions Across North America

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Blanc Labs Partners with TCG Process to Integrate their Automation and Orchestration Platform and deliver Advanced Intelligent Workflow Automation to Financial Institutions

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Blanc Labs and TCG Process have partnered to transform lending operations with innovative automation solutions, using the DocProStar platform to enhance efficiency, compliance, and customer satisfaction in the Canadian lending market.

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BPI in Banking and Financial Services in the US & Canada

April 23, 2024
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Business Process Improvement in Financial Services in US & Canada

Banking and financial services are changing fast. Moving from old, paper methods to new, digital ones is key to staying in business. It’s important to think about how business process improvement (BPI) can help.

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What is the role of a Business Process Improvement Specialist? 

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A business process improvement specialist identifies bottlenecks and inefficiencies in your workflows, allowing you to focus efforts on automating the right processes.

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We’ve developed this resource to help technical teams adopt an Open Banking approach by explaining a high-level solution architecture that is organization agnostic.

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These are not your grandmother’s models: the impact of LLM’s on Document Processing

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These are not your grandmother’s models: the impact of Large Language Models on Document Processing

Explore the transformative influence of large language models (LLMs) on document processing in this insightful article. Discover how these cutting-edge models are reshaping traditional approaches, unlocking new possibilities in data analysis, and revolutionizing the way we interact with information.

Healthcare
Enterprise Automation
Low Code Platforms
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Low-Code Tools and Automation Power Minor Ailments Program for Pharmacists

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Blanc Labs Expedites the Development of Mortgage Product for Canada’s Top Brokerage

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Using RPA in Banking

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

Using RPA in Banking

May 8, 2023
Using RPA in Banking

 

All banking or financial institutions can relate to the struggle of managing piles of structured and unstructured data daily. This task requires repetitive and manual effort from your employees that they could otherwise dedicate to high-value work. It can also be time-consuming and prone to errors, ultimately hampering your bank’s customer experience. Fortunately, automation technologies are proving to be a boon for the finance sector.

The finance domain is experiencing a major transformation, with banking automation and digitization at the forefront. According to a study by McKinsey, machines will handle between 10% to 25% of banking functions in the next few years, which can free up valuable time and resources for employees to focus on more strategic initiatives.

What is Robotic Process Automation (RPA)?

RPA is an automation technology governed by structured inputs and business logic. RPA in banking is a powerful tool that can automate repetitive and time-consuming tasks. It allows banks and financial institutions to gain a competitive advantage by automating routine tasks cost-effectively, fast, and without errors.

Banks, credit unions, or other financial institutions can set up robotic applications to handle tasks like capturing and analyzing information from documents, performing transactions, triggering responses, managing data, and coordinating with other digital systems. The possibilities for using RPA in finance are innumerable  and can include a range of functionalities such as generating reports, sending auto emails, and even auto-decisioning.

How RPA works

Robotic Process Automation works by automating repetitive and routine tasks that are currently performed manually. Software robots, also known as ‘bots,’ are designed to mimic human actions and interactions with digital systems. These rule-based bots can be configured to perform specific tasks, such as document processing, data entry, transaction execution, complete keystrokes, and more.

Once a bot is configured, it can be triggered to run automatically or on a schedule, freeing up human resources to focus on customer service or other higher-value or strategic activities. The bot interacts with the relevant systems and applications, capturing and analyzing data, navigating systems, and automating workflows as needed.

One of the key advantages of RPA in finance is that it is non-intrusive, meaning that it operates within existing systems and processes, without requiring any changes to the underlying infrastructure. This means that no changes are made to the underlying applications. RPA bots perform tasks in a similar manner  to employees- by signing into applications, entering data, conducting calculations, and logging out. They do this at the user interface or application surface layer by imitating mouse movements and the keystrokes made by employees.

This makes it easier to implement and reduces the risk of disruption to existing operations. As per Forbes, RPA usage has seen a rise in popularity in the last few years and will continue to see double-digit growth in 2023.Many people use the terms ‘RPA’ and ‘Intelligent Automation’ (IA) interchangeably. Both are banking automation technologies that improve efficiency, but are they the same?

Are RPA and Intelligent Automation the same?

No, RPA is not IA and IA is not RPA. While RPA is a rule-based approach for everyday tasks, intelligent automation uses Artificial Intelligence (AI) and Machine Learning (ML) technologies to automate more complex and strategic processes. IA encompasses a wide range of technologies which includes RPA. IA enables organizations to automate not just manual tasks but also decision-making processes and allows for continuous improvement through self-learning.

A combination of IA and RPA can unlock the true potential of banking automation. When RPA is combined with the powers of AI, ML, and natural language processing, it dramatically increases the software’s skills to execute advanced cognitive processes like understanding speech, carrying out conversations, comprehending semi-structured tasks such as purchase orders, invoices and unstructured documents like emails, text files and images.

Thus, RPA and its combination technologies are fully capable of taking your banking and financial business to new heights.

What are the benefits of RPA in Banking?

The global RPA market is projected to grow at a CAGR of 23.4%, from $10.01 Billion in 2022 to $43.2 Billion in 2029. Evidently, more industries worldwide are realizing the importance of RPA. Here are some benefits of using RPA in banking and financial institutions.

Improved Scalability

Robots can work faster and longer than humans without taking breaks. RPA can also be scaled to meet changing business needs, making it an ideal solution for organizations that are looking to grow and expand their operations and provide additional services.

Enhanced Compliance and Risk Management

RPA can help banks and financial institutions improve their compliance and risk management processes. For example, the software can be configured to monitor transactions for potential fraud and to ensure compliance with regulatory requirements. It can also inform the bank authorities in case any anomaly is found.

Improved Customer Service

RPA can enable faster and more personalized service to customers. For example, the software can be configured to handle routine customer inquiries and transactions, reduce wait times and improving the overall customer experience.

Increased Efficiency

RPA can automate repetitive and manual tasks, redirecting human resources to other higher-value and strategic activities. This can result in faster processing times, improved accuracy, and reduced costs. According to a study by Deloitte, banking institutions could save about $40 million over the first 3 years of using RPA in banking.

Better Data Management

RPA can automate the collection, analysis, and management of data, making it easier for banks and financial institutions to gain insights and make informed decisions. This means faster account opening or closing, loan and document processing, data entry, and retrieval.

Top Use Cases of RPA in Banking

RPA can be applied in several ways in the banking and finance industry. Here are some examples of RPA use cases in banking and finance:

Accounts Payable

RPA can automate the manual, repetitive tasks involved in the accounts payable process, such as vendor invoice processing, field validation, and payment authorization. RPA software in combination with Optical Character Recognition (OCR) can be configured to extract data from invoices, perform data validation, and generate payment requests, reducing the risk of errors and freeing up human resources.  This system can also notify the bank in case of any errors.

Mortgage Processing

Mortgage processing involves hundreds of documents that need to be gathered and assessed. RPA can streamline the mortgage application process by automating tasks such as document verification, credit checks, and loan underwriting. By using RPA to handle routine tasks, banks, and financial institutions can improve processing time, reduce the risk of errors, and enhance the overall customer experience.

Fraud Detection

According to the Federal Trade Comission (FTC), banks face the ultimate risk of forgoing money to fraud, which costs them almost $8.8 billion in revenue in 2022. This figure was 30% more than than what was lost to bank fraud in 2021 .  RPA can assist in detecting potential fraud by automating the monitoring of transactions for unusual patterns and anomalies. Bots can be configured to perform real-time ‘if-then’ analysis of transaction data, flagging potential fraud cases as defined for further investigation by human analysts.

KYC (Know Your Customer)

RPA can automate the KYC onboarding process, including the collection, verification, and analysis of customer data. RPA software can be configured to handle routine tasks such as data entry, document verification, and background checks, reducing the risk of errors and faster account opening, thus resulting in enhanced customer satisfaction.

Thus, using RPA in your bank and financial institution can not only save time and money but also boost productivity. Banking automation gives you a chance to gain a competitive edge by leveraging technology and becoming more efficient.

Blanc Labs Automation Solution for Banks

Blanc Labs helps banks, credit unions, and financial institutions with their digital transformation journey by providing solutions that are RPA-based. Our services include integrating advanced automation technologies into your processes to boost efficiency and reduce the potential for errors caused by manual effort.

We offer a tailored approach that combines RPA, ML, and AI to automate complex tasks, such as mortgage processing and document processing, allowing you to conserve resources, speed up decision-making  and provide quicker and improved financial services to your customers.

If your bank processes a huge amount of data everyday, we can help you. Book a discovery call with us and let us explain how we can increase the efficiency of your bank’s core functions. Our team will analyze your current processes and propose a tailor-made automation solution that can operate seamlessly and in conjunction with your existing systems.

How to Automate Loan Origination Systems

Financial Services | AI | Banking Automation | Digital Banking | Lending Technology

How to Automate Loan Origination Systems

May 1, 2023
Loan Origination Systems

Loan origination automation is critical because the loan origination process is labor-intensive and prone to human error.

Translation?

The process takes expensive human capital that you can dedicate to other, more strategic tasks. It’s also prone to human error, which increases your costs and puts your reputation at risk.

Automating the process takes humans out of the equation, minimizing the cost of human capital and the risk of human error.

In this article, we explain how to automate the process using an automated loan origination system.

What is the Loan Origination Process?

Loan origination is the process of receiving a mortgage application from a borrower, underwriting the application, and releasing the funds to the borrower or rejecting the application.

When a customer applies for a mortgage, the lender initiates (or originates) the process necessary to determine if a borrower should be lent funds according to the institution’s policies.

The process is extensive and takes an average of 35 to 40 days. The origination process involves five steps, as explained below.

Prequalification

Prequalification is a screening stage. This is where lenders look for potential reasons that can adversely impact a borrower’s capability to repay the loan.

Typically, lenders look for things like:

  • Income: Does the borrower make enough money to be able to service the loan payments, and is that income consistent?
  • Assets: Should the borrower’s income stop for some reason; do they have enough assets to remain solvent given their existing liabilities?
  • Debt: Is the borrower overleveraged? Are the debts secured or unsecured?
  • Credit record: Has the buyer made loan payments on time in the past?

These factors help the lender determine if they should spend time processing the application further.

Preparing a Loan Packet

Lenders create a packet (essentially a file of documents) for prequalified borrowers.

The packet i