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.
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
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.
2023 Fall Economic Update on Consumer-Driven Banking 🥳
Blanc Labs teamed up with Daylight Automation (acquired by Quadient) to overhaul the process pharmacists use to assess and treat minor ailments, at a critical inflection point for Canadians to efficiently access healthcare services.
Blanc Labs teamed up with Daylight Automation (acquired by Quadient) to overhaul the process pharmacists use to assess and treat minor ailments, at a critical inflection point for Canadians to efficiently access healthcare services.
Blanc Labs teamed up with Daylight Automation (acquired by Quadient) to overhaul the process pharmacists use to assess and treat minor ailments, at a critical inflection point for Canadians to efficiently access healthcare services.
Financial Services | AI | Banking Automation | Digital Banking | Enterprise Automation
Using RPA in Banking
May 8, 2023
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.
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.
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:
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 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.
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.
Financial Services | AI | Banking Automation | Digital Banking | Lending Technology
How to Automate Loan Origination Systems
May 1, 2023
Loan origination automation is critical because the loan origination process is labor-intensive and prone to human error.
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 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 includes the borrower’s documents, including KYC, financial statements, and other relevant documents that provide an overview of the borrower’s debt servicing capability.
Lenders also include documents that highlight the reasons that make an applicant eligible or ineligible to be considered for the loan.
For example, the lender may include the borrower’s debt-to-income ratio, properties and assets at market value, and income streams to provide an overview of whether the borrower is a good candidate for the loan.
Some lenders take extra steps to double-check the applicant’s claims.
For example, lenders might hire a valuer or research property rates to verify your real estate investment’s current market value. The valuer’s report is added to the packet for the underwriter’s reference.
Many borrowers, especially those with an excellent credit record, browse their options before accepting a lender’s offer.
The borrower might want to negotiate a lower rate or ask for a fixed rate instead of a floating rate.
Term Sheet Disclosure
A term sheet is a summary of the loan. It’s a non-binding document that contains the terms and conditions of the mortgage deal.
The term sheet includes the tenure, interest rate, principal amount, foreclosure charges, processing fees, and other relevant details.
If the negotiations go well and the borrower accepts the offer, the lender closes the loan.
The lender creates various closing documents, including final closing disclosure, titling documents, and a promissory note.
The borrower and lender sign the documents, and the lender disburses the funds as agreed.
3 Ways to Automate Loan Origination
Now that you know the loan origination process, let’s talk about automating parts of this process to make it more efficient.
Digitizing Loan Applications
You can create an online portal where applicants can initiate a loan application and upload their KYC and other documents.
The documents are automatically transferred to your internal systems for prequalifying the applicant.
IDP extracts and relays the applicant’s data from the documents to your system.
Once the data is in the system, robotic process automation (RPA) can be used to determine if an applicant should be prequalified.
You can use a machine learning (ML) algorithm for deeper insights.
ML can help identify characteristics that make a person more or less likely to service the loan until the end of the term, allowing you to make smarter decisions.
Assembling Loan Documentation
Cloud-based RPA can collect documents from the online portal and organize them in one location. Not only are digital copies faster to collect, but they’re easy to store and search.
Think about it. You’ve received an application, but it’s missing the cash flow statement for last year. You’ll need to email or call the applicant to upload the documents, wasting your and the applicant’s time.
Lenders typically have a document checklist. Automating checklists is easy with RPA — when the applicant forgets to upload a document, the RPA can trigger notifications to the applicant.
The system can also notify the loan officer if the applicant becomes non-responsive.
Speeding Up Underwriting
Borrowers want faster access to funds, but lenders must complete their due diligence.
Lending automation can help reduce the time between application and approval.
With technologies like artificial intelligence (AI), ML, and RPA, automation systems can assess an applicant’s creditworthiness within seconds.
RPA can help with basics like checking the minimum credit score and income levels. RPA can also flag any areas that indicate greater risk, such as excessive variability in the applicant’s cash flows.
Moreover, loan origination is a compliance-heavy process. It’s easy to forget a small step when you’re overwhelmed with loan applications.
RPA ensures all compliance steps are taken care of. If they’re not, the system can trigger alerts for the underwriter as well as the superiors.
AI and ML can provide deeper insights. These technologies can use big data to identify patterns that make a borrower more likely to default, allowing you to make smarter decisions fast.
Moreover, AI and ML can help you look beyond credit scores and find people that are more creditworthy than their credit scores suggest.
As Dimuthu Ratnadiwakara, assistant professor of finance at Louisiana State University, explains:
“Traditional models tend to lock anyone with a low credit score—including many young people, college-educated people, low-income people, Black and Hispanic people and anyone who lives in an area where there are more minorities, renters and foreign-born—out of the credit market.”
How Shortening Loan Origination With Automation Helps
Shortening the loan origination process benefits you in multiple ways:
Match customer expectations: You’ll deliver on the customers’ expectations by offering faster loan processing. 40% of mortgage customers are willing to complete the entire process using self-serve digital tools, but 67% still interact with a human representative via phone.
Deliver better experiences: Automation offers various opportunities to improve customer experience. For example, you can set up an AI chatbot that responds to customers’ questions in real time.
Increase efficiency: You can process more applications per month by automating loan origination.
Better use of your staff’s time: Automated workflows allow credit officers to focus on parts of the business that require a human touch, such as building stronger relationships with clients, than on repetitive tasks that automation can perform more accurately.
Loan Origination Automation with Blanc Labs
Selecting the right partner to set up loan origination automation is critical. Partnering with Blanc Labs ensures frictionless implementation of a personalized loan origination system.
Blanc Labs tailor-makes solutions best suited for your needs and workflow. Blanc Labs starts by assessing your needs and creating a strategy to streamline your loan origination processes. Then, Blanc Labs creates an automation system using technologies like RPA, AI, and ML.
Book a demo to learn how Blanc Labs can help you automate your loan origination workflow.
Financial Services | AI | Banking Automation | Digital Transformation | Enterprise Automation
10 Tips to Successfully Implement RPA in Finance
April 14, 2023
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.
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.
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.
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.
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.
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.
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.
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.
Financial Services | AI | Banking Automation | Gen AI | ML
Banking Automation: The Complete Guide
April 6, 2023
Banks are process-driven organizations. Processes ensure accuracy and consistency across the organization. They are also repetitive. Over the past decade, the transition to digital systems has helped speed up and minimize repetitive tasks. But to prepare yourself for your customers’ growing expectations, increase scalability, and stay competitive, you need a complete banking automation solution.
Systems powered by artificial intelligence (AI) and robotic process automation (RPA) can help automate repetitive tasks, minimize human error, detect fraud, and more, at scale. You can deploy these technologies across various functions, from customer service to marketing.
Many, if not all banks and credit unions, have introduced some form of automation into their operations. According to McKinsey, the potential value of AI and analytics for global banking could reach as high as $1 trillion.
If you are curious about how you can become an AI-first bank, this guide explains how you can use banking automation to transform and prepare your processes for the future.
What is Banking Automation?
Banking automation involves automating tasks that previously required manual effort.
For example, banks have conventionally required staff to check KYC documents manually. However, banking automation helps automatically scan and store KYC documents without manual intervention.
Cost saving is generally one of RPA’s biggest advantages.
According to a Gartner report, 80% of finance leaders have implemented or plan to implement RPA initiatives.
The report highlights how RPA can lower your costs considerably in various ways. For example, RPA costs roughly a third of an offshore employee and a fifth of an onshore employee.
You can make automation solutions even more intelligent by using RPA capabilities with technologies like AI, machine learning (ML), and natural language processing (NLP). According to a McKinsey study, AI offers 50% incremental value over other analytics techniques for the banking industry.
With that in mind, let’s look closely at RPA and how it works.
Generative AI and Banking Automation
The latest trend in banking automation is the use of Generative AI.
Retail banking and wealth: Generative AI can create more accurate NLP models and help automated systems process KYC documents and open accounts faster.
SMB banking: Generative AI can help interpret non-numeric data, like business plans, more effectively.
Commercial banking: Generative AI will enable customers to get answers about financial performance in complex scenarios.
Investing banking and capital markets: Banks could use generative AI to stress test balance sheets with complex and illiquid assets.
Banks are already using generative AI for financial reporting analysis & insight generation. According to Deloitte, some emerging banking areas where generative AI will play a key role include fraud simulation & detection and tax and compliance audit & scenario testing.
What is RPA?
Robotic process automation, or RPA, is a technology that performs actions generally performed by humans manually or with digital tools.
Say you have a customer onboarding form in your banking software. You must fill it out each time a customer opens an account. You’re manually performing a task using a digital tool.
RPA can perform this task without human effort. The difference? RPA does it more accurately and tirelessly—software robots don’t need eight hours of sleep or coffee breaks.
You can implement RPA quickly, even on legacy systems that lack APIs or virtual desktop infrastructures (VDIs).
Implementing RPA can help improve employee satisfaction and productivity by eliminating the need to work on repetitive tasks.
You can use RPA in banking operations for various purposes.
For example, Credigy, a multinational financial organization, has an extensive due diligence process for consumer loans.
The process was prone to errors and time-consuming. The company decided to implement RPA and automate the entire process, saving their staff and business partners plenty of time to focus on other, more valuable opportunities.
The Need for Automation in Banking Operations
Banks need automation to:
Deliver better customer experiences
Increase online security
Improve decision making
Below, are more reasons for your bank to automate operations.
To Deliver Faster, Personalized Customer Experiences
New-gen customers want banks that can provide fast financial services online.
Thanks to the pandemic, the shift to digital has picked up pace. A digital portal for banking is almost a non-negotiable requirement for most bank customers.
In fact, 70% of Bank of America clients engage with the bank digitally. The bank’s newsroom reported that a whopping 7 million Bank of America customers used Erica, its chatbot, for the first time during the pandemic.
A chatbot can provide personalized support to your customers. A level 3 AI chatbot can collect the required information from prospects that inquire about your bank’s services and offer personalized solutions.
A chatbot is a great way for customers to get answers, but it’s also an excellent way to minimize traffic for your support desk.
To Improve Cybersecurity
Cybersecurity is expensive but is also the #1 risk for global banks according to EY. The survey found that cyber controls are the top priority for boosting operation resilience according to 65% of Chief Risk Officers (CROs) who responded to the survey.
Using automation to create a cybersecurity framework and identity protection protocols can help differentiate your bank and potentially increase revenue. You can get more business from high-value individual accounts and accounts of large companies that expect banks to have a top-notch security framework.
Automating cybersecurity helps take remedial actions faster. For example, the automated system can freeze compromised accounts in seconds and help fast-track fraud investigations.
Of course, you don’t need to implement that automation system overnight. With cloud computing, you can start cybersecurity automation with a few priority accounts and scale over time.
For Better Decision Making
AI and ML algorithms can use data to provide deep insights into your client’s preferences, needs, and behavior patterns.
These insights can improve decision-making across the board. For example, using these insights in your marketing strategy can help hyper-target marketing campaigns and improve returns.
Moreover, these insights help deliver greater value to customers. By making faster and smarter decisions, you’ll be able to respond to customers’ fast-evolving needs with speed and precision.
As a McKinsey article explains, banks that use ML to decide in real-time the best way to engage with customers can increase value in the following ways:
Stronger customer acquisition: Automation and advanced analytics help improve customer experience. They help personalize marketing across the customer acquisition journey, which can improve conversions.
Higher customer lifetime value: You can increase lifetime value by consistently engaging with customers to strengthen relationships across products and services.
Lower credit risk: Banks can screen customers by analyzing behavior patterns that signal higher default or fraud risk.
To Empower Employees
As you digitize banking processes, you’ll need to train employees. Reskilling employees allows them to use automation technologies effectively, making their job easier.
Your employees will have more time to focus on more strategic tasks by automating the mundane ones. This results in increased employee satisfaction and retention and allows them to focus on things that contribute to your topline — such as building customer relationships, innovating processes, and brainstorming ways to address customers’ most pressing issues.
Challenges Faced by Banks Today
Here are some key challenges that banks face today and how automation can help address them:
Inefficient Manual Processes
Manual processes are time and resource-intensive.
According to the 2021 AML Banking Survey, relying on manual processes hampers a financial organization’s revenue-generating ability and exposes them to unnecessary risk.
The simplest banking processes (like opening a new account) require multiple staff members to invest time. Moreover, the process generates paperwork you’ll need to store for compliance.
While you complete the account opening process, the customer is on standby, waiting to start using their account.
The slow service doesn’t exactly make a great impression. Customers want to be able to start using their accounts faster. If you’re too slow, they’ll find a bank that offers faster service.
Automation helps shorten the time between account application and access. But that’s just one of the processes that automation can speed up.
Technologies like RPA and AI can help fast-track processes across departments, including accounting, customer support, and marketing.
Automation Without Integration
Banks often implement multiple solutions to automate processes. However, often, these systems don’t integrate with other systems.
For end-to-end automation, each process must relay the output to another system so the following process can use it as input.
For example, you can automate KYC verification. But after verification, you also need to store these records in a database and link them with a new customer account. For this, your internal systems need to be integrated.
Connecting banking systems requires APIs. Think of APIs as translators. They help two software solutions communicate with each other. A system can relay output to another system through an API, enabling end-to-end process automation.
Increase in Competition
Canadians want more competition in banking. The competition in banking will become fiercer over the next few years as the regulations become more accommodating of innovative fintech firms and open banking is introduced.
An increase in competition will give customers more power. They’ll demand better service, 24×7 availability, and faster response times.
You’ll need automation to achieve these objectives and make yourself stand out in the crowd.
Benefits of Automation in Banking
Once you invest in automation, you can expect to derive the following benefits:
Improves Operational Efficiency
An error-free automation system can supercharge operational efficiency.
You’ll have to spend little to no time performing or monitoring the process. Moreover, you’ll notice fewer errors since the risk of human error is minimal when you’re using an automated system.
Implementing automation allows you to operate legacy and new systems more resiliently by automating across your system infrastructure. This increases efficiency, consistency, and speed.
“Through the combination of a distinct data element with robotics process automation, it is possible to generate client documentation from management tools and archives at a high frequency. Due to its scalability, high volumes can be managed more efficiently.”
The article provides the example of Swiss banks. During the pandemic, Swiss banks like UBS used credit robots to support the credit processing staff in approving requests. The support from robots helped UBS process over 24,000 applications in 24-hour operating mode.
80% of banks still favor some form of print statements. The cost of paper used for these statements can translate to a significant amount. Automation and digitization can eliminate the need to spend paper and store physical documents.
b. Human error
Human error can require reworks and cause delays in processing customer requests. Errors can result in direct losses (like a lost sale) and indirect losses (like a lost reputation). Minimizing errors can help reduce the cost associated with human error.
c. Increased employee satisfaction
You’ll spend less per unit with more productive employees. Automation can help improve employee satisfaction levels by allowing them to focus on their core duties.
For example, a sales rep might want to grow by exploring new sales techniques and planning campaigns. They can focus on these tasks once you automate processes like preparing quotes and sales reports.
Working on non-value-adding tasks like preparing a quote can make employees feel disengaged. When you automate these tasks, employees find work more fulfilling and are generally happier since they can focus on what they do best.
As Professor Sgroi explains, “The driving force seems to be that happier workers use the time they have more effectively, increasing the pace at which they can work without sacrificing quality.”
Automation can help meet customer expectations in various ways.
Speed is one of the most difficult expectations to meet for banks. You want to offer faster service but must also complete due diligence processes to stay compliant. That’s where automation helps.
61% of customers feel a quick resolution is vital to customer service. As a bank, you need to be able to answer your customers’ questions fast.
How fast? Ideally, in real-time.
A level 3 AI chatbot can help provide real-time, personalized responses to your customers’ questions.
In addition to real-time support, modern customers also demand fast service. For example, customers should be able to open a bank account fast once they submit the documents. You can achieve this by automating document processing and KYC verification.
Better Risk Management
Automation can help minimize operational, compliance, and fraud risk.
Since little to no manual effort is involved in an automated system, your operations will almost always run error-free.
You can also automate compliance processes. For example, you can add validation checkpoints to ensure the system catches any data irregularities before you submit the data to a regulatory authority.
Automation can help minimize fraud risk too. Using AI and ML can help flag suspicious activities and trigger alerts. As this study by Deloitte explains:
“Machine learning can also analyze big data more efficiently, build statistical models quickly, and react to new suspicious behaviors faster.”
Using traditional methods (like RPA) for fraud detection requires creating manual rules. RPA works well in a structured data environment. But given the high volume of complex data in banking, you’ll need ML systems for fraud detection.
Blanc Labs’ Banking Automation Solutions
Blanc Labs helps banks, credit unions, and Fintechs automate their processes. We tailor-make automation tools and systems based on your needs. Our systems take work off your plate and supercharge process efficiency.
Our team deploys technologies like RPA, AI, and ML to automate your processes. We integrate these systems (and your existing systems) to allow frictionless data exchange.
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:
Better Customer Experience
Faster Mortgage Application Processing
Accurate Report Generation
Anti-Money Laundering Prevention
Audits and Compliance
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.”
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.
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.
Extraction: The Next Step in Intelligent Document Processing
Extraction: The Next Step in Intelligent Document Processing
May 31, 2022
By Luciano Lera Bossi, Alejandro Nava and Parsa Morsal
One of the starting points of digital transformation, especially for financial institutions, is intelligent document processing or IDP. We previously explored classification as the first step in IDP. In this article, we will explore the next crucial step, extraction.
What is document extraction?
Document data extraction is a process of extracting data from structured or unstructured documents and converting them into usable data. It is also called intelligent data capture. With the rapid progress in document imaging technology such as the incorporation of natural language processing (NLP) and optical character recognition (OCR) as well as advanced analytics, we can now enable IT systems to understand the data that was thus far only on paper.
Powered by machine learning models and NLP, IDP systems can now bring the benefits of AI to document processing. The intelligence and detail offered by IDP systems today can be used for many functions including compliance and fraud. The level of granularity, accuracy, and speed offered by IDP systems today, can hugely impact the scale of digital transformation for your organization.
Document processing and the banking industry
The financial industry is no stranger to the benefits of IDP. In a recent study of 200 banks in the US, it was found that 66% of the respondents eliminated the need for manual processes for a typically labor-intensive industry thanks to IDP; and 87% cited accuracy and extraction as the key reasons for incorporating IDP into their systems. Given the emphasis on accuracy and extraction, it is important to understand how intelligent document processing coupled with OCR and NLP can give desired results.
OCR vs IDP
OCR as a document processing technology has been around the longest. OCR is used to extract handwritten or typed text in documents which can then be converted into data. While OCR has been synonymous with data extraction for many years, it is not without its challenges. Without intelligent processing of the data in understanding what the data is for, OCR may give inaccurate results. There can be errors in detecting a text block in an image (error in word detection), there may be errors in interpreting words correctly if there are differences in text alignment or spacing (error in word segmentation) or there may be errors in identifying a character bound in a character image (error in character recognition).
However, when combined with intelligent processes such as NLP and machine learning analytics, time-intensive processing tasks can be sped up with minimal errors. The biggest differentiator between OCR and IDP is that IDP can also handle documents that may be structured, semi-structured, or unstructured.
Structured documents generally focus on collecting information in a precise format, guiding the person who is filling them with precise areas where each piece of data needs to be entered. These come in a fixed form and are generally called forms. Examples of structured documents include tax forms and credit reports.
Semi-structured documents are documents that do not follow a strict format the way structured forms do and are not bound to specified data fields. These don’t have a fixed form but follow a common enough format. They may contain paragraphs as well. Example of semi-structured documents could be employment contracts or gift letters.
Unstructured documents are documents in which the information isn’t organized according to a clear, structured model. These files are all easily comprehensible by human beings, yet much more difficult for a robot. Examples of unstructured documents include mortgage commitment statements and municipal tax forms.
Textual and Visual data extraction in IDP
The two main aspects that efficient IDP solutions tackle are textual data extraction and visual data extraction. In textual data extraction, the entity extraction technique is applied to recognize text in a document. This is a machine learning approach where the software is exposed to thousands of documents and the machine “learns” to identify information and segregate it based on certain semantic parameters. Entity extraction can involve a variety of tools and techniques including neural networks to visual layout understanding. By using entity extraction methods, you can avoid going down the template route and thereby use the software on various kinds of documents.
In visual data extraction, IDP solutions can be designed to understand elements such as signatures, tables, checkboxes, logos, etc. Visual data extraction is more complex than textual data extraction as it involves detecting, analyzing, and extracting information from the regions with visual elements accurately while also denoising content that is not relevant. Using machine learning, advanced visual extraction models can also understand the structural relationship of the visual data and its relevance.
Choosing the right IDP solution that can handle both text and visual elements accurately across varying document types will ensure that there is no need for your back office to comb through documents once again.
Explore Kapti, our intelligent document processing software to find out how the power of machine learning and automated document workflows can transform your organization’s document processing experience.
Classification: The First Step in The Art of Speed Reading
Technology | AI | Enterprise Automation | IDP | ML
Classification: The First Step in The Art of Speed Reading
March 15, 2022
The world is building a solid foundation of machine learning (ML) into various sectors, functions, and tasks in our everyday lives. There is a myriad of components that one may delve into when exploring ML but an area that we engage in extensively and is getting increasingly interesting, is deep learning applied to document and data capture or Intelligent Document Processing (IDP).
The ABCs of IDP
Let’s start with the basics: According to Bernard Marr at Forbes, “Deep learning is a subset of machine learning where artificial neural networks, algorithms inspired by the human brain, learn from large amounts of data.” IDP involves the capturing, extracting and processing of data from a variety of document types.
Now what’s so “exciting” about document and data capture? There are two types of documents from which you’d capture data: Structured and Unstructured documents.
Structured: These are standard forms (ex: government forms) that have specific and unaltered fields and patterns of information.
Unstructured: These are documents that don’t have any universal pattern. For this article, letters of employment would be good examples. Companies draft them in different formats and with different lengths (and cadence).
And with that established, lets delve into the two stages of how data is captured:
Classification: Examining and defining a document
Extraction: Capturing and recording key data from a document that has already been classified
There are two ways in which documents may be categorized: image processing and Natural Language Processing (NLP).
The Art of Image Processing
Here is an instance where machine learning models are trained to look at a document and understand the pattern of what it sees. It isn’t ‘reading’ the document but uses Convolutional Neural Networks (CNNs) to identify patterns within an image, which in turn informs its classification. We found this works well for more structured documents where patterns are identical while the content may be varied. However, where we came into a few issues was the ability to scale the process.
When you train a machine to resolve categorisation, through the image approach, you are identifying objects specifically. However, forms don’t possess visual features for image processing. Every time we had to retrain a new document type, it would need to be trained from scratch with new data. This took days and weeks. In addition, we started to work with unstructured documents and found that we needed a new solution to effectively categorize them faster.
Enter: Natural Language Processing
With NLP, Optical Character Recognition (OCR) and LTSM (long term short memory) networks are used to extract words, run analysis (determining the context of the words), and then classify the documents. Unlike image processing, NLP, along with transformer models considers the relationship between all the words in a sentence and determines the weight of each word to interpret its meaning. This reduces the training time for every new document introduced, making the entire process faster.
Where new document types may take a week to train the machine to classify using image processing, NLP does it in mere seconds.
Setting up for success
There are two things to consider when one starts a project with NLP to make the process easier:
Determine goals: What information are you capturing and why. And what type of documents will be required to process?
Organize data collection: Maintain the quality of data by building workflows or processes that support consistent quality of information and documents collected.
The Journey Ahead
When you look at algorithms, sentiment analysis and the capacity of machine learning to evolve its capabilities, the term ‘speed reading’ really is taken to a whole new level. Our story doesn’t end here. It is just the beginning; It’s an evolving journey of discoveries and we look forward to exploring other dimensions of our realizations, including our next chapter exploring extraction, with you very soon!
Technology | AI | Digital Transformation | Enterprise Automation | IDP
The Human Effects of Hyperautomation: Redefining processes and business models
January 31, 2022
Now more than ever the phrase “amid chaos lies opportunity” should be resonating with business leaders. This is not a time to hesitate but rather a time to adapt and move swiftly. The world has changed and those who can adjust quickly will come out on the other side stronger and with a greater market share. Herein lies tremendous opportunities.
According to the 2021 Gartner Board of Directors Survey, “69% of boards accelerated their digital business initiatives in the wake of COVID-19 disruption. Almost half anticipate changing their organizations’ business model as a result of the pandemic.” We live and breathe in a digital world. To stay ahead of the competition, serve our customers better and in the way they want, to grow market share while moving faster and more efficiently, companies must look to technology for assistance.
Robotic Process Automation is dead, long live Hyperautomation
I say this with my tongue firmly planted in my cheek. Robotic Process Automation (RPA) is the automating of simple, rule-based tasks, and is an excellent starting point in digital transformation. However, as many companies start down the path of transformation, they soon find that they need more than just the automating of tasks; they need to be able to make sense of all the data that they have accumulated and have stored in dispirited backends and repositories.
“Hyperautomation is a revolutionary approach to maximize the benefit of digital transformation to enterprises.” Hyperautomation is the application of advanced technologies such as RPA, Artificial Intelligence, (AI) Machine Learning (ML), and Process Mining to augment and automate processes, enhance the use of analytics in ways that are more impactful than traditional automation.”
Keep in mind that Hyperautomation is not just the bringing together of several technologies; it is the reimagining and redesigning of the entire business model. It is a continuous effort redefining the value you are bringing to your end user and redefining the business models that support those initiatives. In addition, according to Forbes, “Every day we create roughly 2.5 quintillion bytes of data, and that number is growing at an exponential pace.” By applying technology to help you make sense of all your accumulated data you can make impactful and insightful business decisions.
Stakeholders in the Hyperautomation Journey
Reimagine engagement with both your internal and external customers. Your internal customer is the talent in your teams that help you engage with your external customers either directly or indirectly. They are the people you want to help by freeing up their time from mundane and repetitive tasks so that they can apply their creativity and insight to more high-value work. This is the core premise of the ‘digital workforce’. The common definition of a digital workforce is digital tools and technologies that enable your teams to work smarter. They are not encumbered by an office or legacy systems to accomplish their tasks and goals. Providing talent with a digital workforce that can work alongside them doing the tasks of copying and pasting data from one repository to another, as an example, will free them up and improve their productivity and morale.
Managing business processes old school—manually and email-based—can make tracing key information difficult. Executed well, the deployment of a digital workforce should result in humans refocusing their available time to pursue innovative growth strategies and finding new ways to improve the external customer experience.
As for the external customer, the journey has and continues to change at a dramatic pace. If we look at our world today, smartphones grant us access to information, forge connections and transact at the palm of our hands, live streaming has opened up our world of consuming content, crowdsourced GPS provides the fastest way for us to reach our destinations, ride sharing has transformed transportation (as will large scale adoption of self-driving technology), short term rentals have disrupted the way we plan travel – and the list goes on. The commonalities in all these examples are the instant gratification and personalization that they provide. Thanks to digital-native companies, customer expectations have changed too. Customers expect to engage with you seamlessly, with speed and simplicity. They demand a highly personalized experience and interaction with proactive recommendations and offers. If your legacy processes cannot provide that experience promptly, you run the risk of losing that customer to a savvier competitor.
Hyperautomation can enable that level of personalization by predicting customer needs and accommodate the instant gratification of information and service through prescriptive engagement – all powered by artificial intelligence.
Where to next?
It isn’t enough to simply automate existing processes. Look at that big picture and long-term view and think about how automation (today) and hyperautomation (soon after) is going to transform your business. Here are a few questions to ask yourself:
What am I trying to automate?
What types of data do I need to use today and what would I need for decision making in the future?
Can I reimagine my business model with hyperautomation in mind? What is that vision?
Once you’ve asked yourself these questions and are on your way to creating that redefined business model, begin layering automation onto it and then introduce the concept of hyperautomation to the plan. With this long-term view, you will create additional value in your customer base and with your teams. This isn’t just about updating a legacy system; it’s about redefining and evolving your business to stay ahead of the curve.
Hyperautomation in the path ahead
Each year brings with it new market opportunities and scope for growth. The goal of every forward-thinking leader will always be to find ways to empower their talent and enhance the customer journey of their products and services with the mission of sustainable growth. Adding Hyperautomation with ML and AI to the right processes connects disparate data sets, eliminates inefficiencies, lowers lead times, reduces process times, provides greater customer insights and experiences, and results in more empowered and productive team for that brighter future.
We think it’s time to get rid of the swivel chair. Let’s talk about how we can help you on your automation journey. Explore our experience with enterprise automation and let’s accelerate your organization on its digital path.