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Category: Banking Automation

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 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.

Negotiation

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.

Loan Closing

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.

10 Tips to Successfully Implement RPA in Finance

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

10 Tips to Successfully Implement RPA in Finance

April 14, 2023
RPA in Finance

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

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

What is Robotic Process Automation (RPA)?

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

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

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

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

The Use of RPA in Banking Automation

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

Customer Service Automation

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

Loan Processing

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

Fraud Detection and Prevention

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

KYC and AML Compliance

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

Back Office Operations

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

Payment Processing

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

Risk Management

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

Internal Compliance Monitoring

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

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

10 Tips for Successfully Implementing RPA in Finance

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

Start Small

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

Define Clear Goals

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

Involve Stakeholders

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

Assess Processes

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

10 steps

Choose the Right Tools

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

Define Roles and Responsibilities

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

Ensure Data Security

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

Plan for Scalability

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

Monitor and Evaluate Performance

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

Foster a Culture of Innovation

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

Implementation Automation with Blanc Labs

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

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

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

Banking Automation: The Complete Guide

Financial Services | AI | Banking Automation | Gen AI | ML

Banking Automation: The Complete Guide

April 6, 2023
Banking Automation

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.

According to Insider Intelligence’s ChatGPT and Generative AI in Banking report, generative AI will have the greatest impact on data-rich sectors such as:

  • 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
  • Empowering employees

Below, are more reasons for your bank to automate operations.

Why Banks Need Automation

To Deliver Faster, Personalized Customer Experiences

New-gen customers want banks that can provide fast financial services online.

The 2021 Digital Banking Consumer Survey from PwC found that 20%-25% of consumers prefer to open a new account digitally but can’t.

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 operating costs: Banks can reduce costs by fully automating document processing, review, and decision-making.
  • 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 Banking Automatios

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.

Makes Processes Scalable

Banks noticed how automation could be an excellent investment during the pandemic. As explained in a World Economic Forum (WEF) article:

“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.

In addition to RPA, banks can also use technologies like optical character recognition (OCR) and intelligent document processing (IDP) to digitize physical mail and distribute it to remote teams.

Cost Reduction

Automation helps reduce costs on multiple fronts:

a. Stationery

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.

Happiness makes people around 12% more productive, according to a recent study by the University of Warwick.

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.”

Customer Satisfaction

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.

Book a discovery call to learn more about how automation can drive efficiency and gains at your bank.

Benefits of Intelligent Document Processing

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

Benefits of Intelligent Document Processing

January 20, 2023
Benefits of IDP

If you are reading this, chances are that you are exploring intelligent document processing (IDP) systems for your business.

Many businesses are curious about this document automation process because, just like you, they may have heard about how it simplifies complex document layouts, captures data and organizes it for a seamless workflow.

In this article, we will explore:

  • What Is Intelligent Document Processing?
  • How Intelligent Document Processing works
  • Benefits of Intelligent Document Processing

What Is Intelligent Document Processing?

Intelligent document processing uses Artificial Intelligence (AI), Machine Learning (ML), Optical Character Recognition (OCR), computer vision, and Intelligent Character Recognition (ICR) to automate data extraction from complex semi-structured and unstructured documents. This technology helps you categorize the extract data into a meaningful format that is easier for people or a system to comprehend. A popular use case for intelligent document processing is mortgage origination and decisioning, where applications can run into hundreds of pages.

Read more on how Intelligent Document Processing helps financial services organizations.

How Does Intelligent Document Processing Work?

IDP uses deep-learning AI technology to scan complex data, extract it and organize it into predefined categories. The best part is that this technology can be trained in up to 190 languages.

One of the best ways to understand how document processing works is to look into a few use cases first. Here are multiple ways in which IDP helps organizations from various industries to automate their data.

IDP for Human Resources

One of the popular intelligent document processing use cases is human resources. This paper-intensive industry has already transitioned to data automation by:

  1. Screening resumes and capturing the right skill sets that match a job description. This has helped HR teams avoid going through resumes manually and narrow down on candidates who are fit to take up the role, hassle-free.
  2. Keeping all the employee data in one place. Earlier, HR teams were forced to manage hundreds of thousands of files for new and existing employees. Not only was it hard to find specific data quickly, but it was hard to comprehend at times. But with IDP, it is easier to update and extract the employee data when need be.
  3. Simplifying the employee onboarding process by capturing employee information based on the forms filled in by them.

IDP for Mortgage Processing

Another data heavy use case you can refer to is mortgage processing. Given how vast the industry is, you can only imagine the number of documents that accumulate at every stage of the mortgage process. Let’s see how IDP simplifies document accumulation and data extraction for this industry:

  1. IDP is capable of processing high-volume mortgage data and identifying possible risks one may face during the process. With IDP, mortgage officers can identify the cause for rejection and inform approvers about these potential risks.
  2. Validating documents is time consuming. However, IDP helps you validate the data from documents with a button and offers you insights on whether to move ahead with the mortgage or not.
  3. You can also audit each mortgage application with IDP and that too without human intervention. It can be time consuming to audit documents for a mortgage even when you have a professional helping you out with the process. With IDP, you can save time and also check the authenticity of each document.

Top 7 Benefits of Intelligent Document Processing

  1. Increases Employee Productivity

One of the benefits of using IDP is its ability to increase employee productivity. Intelligent document processing helps employees free up their time that they spend on doing repetitive tasks such as data entry and record management. Lesser time spent on repetitive tasks helps increase their productivity.

  1. Helps Reduce Manual Work

IDP is also known for reducing manual work as it enables extraction of data from a document,  an image, sound, or even a video using its AI technology. What’s more, the data extracted is then transformed into text, thereby helping employees reduce manual work on such tasks. It uses Natural Language Processing (NLP) that understands the content and converts that data into text.

  1. Automates Classification of Documents

Once the data is converted to text, it is classified into different categories, without the need for human intervention. This helps your organization with data collection and streamlining the document management process with minimal errors.

  1. Enables the Processing of Large Volumes of Documents

Another benefit of using IDP is processing large volumes of documents in one go. While humans take time to extract data from each document, the same isn’t true in the case of IDP. Unlike humans, a document processor can extract data from multiple documents simultaneously. You can tackle a giant database and avoid spending capital on a data entry team.

  1. Improves Data Accuracy

Humans are prone to make errors when feeding data to a database, especially when there are a large number of documents. This may hamper the overall authenticity of the database and may leave managers questioning its accuracy.

Thankfully, the same isn’t true in the case of AI and ML-based IDP technology. It is capable of entering the data accurately in the database and retrieving the same data with speed when its users need it on an urgent basis. In short, an automated document processor can extract accurate data and eliminate mistakes with an accuracy of more than 90%.

  1. Increases User and Customer Satisfaction

Intelligent Document Processing is capable of providing users with optimal responsiveness through the life cycle of work to be performed. With IDP, you can process evidentiary documents, extract the right keywords from a data set in less time. What’s more, the automated process can use keywords to route emails to the right department for faster throughput from start to finish. This helps speed up the response time between receiving an origination request to approving it, from a claim initiation to notification of completion, etc. Documents are no longer the bottleneck.

  1. Offers Data Security

When documents are managed manually or in disparate data systems, there is always a possibility of a data breach. But with IDP, documents can be kept in a secure, centralized location. This enables businesses to be more compliant with data protection regulations. In other words, you can make sure that the data saved never gets misused by anyone.

Maximize the Benefits of Intelligent Document Processing

Benefits such as these (and others discussed in the article above), will help you identify various reasons why a data extraction processes like Intelligent Document Processing are essential for your business. Once you decide to opt for intelligent document processing, it’s time to look for the right implementation partner.

Why Choose Blanc Labs’ Intelligent Document Processing?

Blanc Labs believes in driving improved operational efficiency and better business outcomes.

Blanc Labs’s IDP solution can:

  • Automate workflows for document collection, digitization and analysis
  • Replace manual effort through intelligent data capture
  • Connect with third party data providers for analysis and insights
  • Analyze document data, provide status alerts, and flag fraudulent entries
  • Secure documents in a drop box
  • Deploy on premises, in the cloud or as a hybrid model

We hope that learning about these benefits will help you arrive at a decision faster and invest in the best document processing solution in the market.

Transforming a Bank’s Value Network with Automation

Financial Services | Banking Automation | Customer Experience | Enterprise Automation | IDP | Lending Technology

Transforming a Bank’s Value Network with Automation

August 11, 2022
Bank's Value

Michael Porter’s value chain has been one of the top seminal business management ideas that saw business operations with through a new lens. Just like the value chain resulted in concepts like value creation and value pricing leading to phenomenal growth in global business scale and operations in the last 50 years, we are now seeing a similar scenario in the financial services industry with intelligent automation.

At the turn of the century, we saw a new concept emerge that resulted in changing the business dynamics in the Y2K. This new business concept came to be known as value network, a series of interactions between individuals, entities, organizations, departments, and systems that collectively work towards benefitting the entire group or ecosystem. This new concept had an astounding impact on how businesses and markets operated and paved the course of today’s business ecosystem. For instance, the rise of Apple and its ecosystem can be attributed to this shift.

A similar shift is also taking place in the financial services industry, where the digitization, embedment, and now decentralization of the payments ecosystem with the commercialization of blockchain, cryptocurrencies, procure to pay (P2P) lending are being touted as the next big thing.

Given the pervasive technical and innovative initiatives that are emerging at breakneck speed, it is a necessity necessary to keep transforming and innovating. This is especially relevant for the financial services industry which have millennials as customers and will soon begin catering to GenZ.

To digitally transform a bank’s value network let’s start by stating the three core areas of a bank’s value network namely, network promotion & contract management, service provisioning & billing, and platform operations.

With a two-sided value network, the bank fundamentally connects a borrower with a depositor and thus, becomes the enabler of value creation for such a network. In doing so, a bank delivers core banking and back-office operations, payments and lending functions, and risk and treasury management activities.

For each of these areas, hundreds of functions and duties must be seamlessly executed with precision. Today, the increase in business volumes and scale of operations has led to bankers asking, “What if these complex and time-consuming operations can be boosted with robots (bots) assisting humans to accelerate speed, increase productivity, and assure the precision of key banking functions?”

Some of the key operational areas where bots can and, in many cases, are assisting humans to realize the true potential of an enterprise are customer service, compliance accounts payable, credit card processing, mortgage processing, fraud detection, know your customer (KYC) process, general ledger, report automation and account closure process.

By embracing bots, banks can improve the customer experience while reducing costs and improving efficiency. Increased automation combined with more efficient processes makes the day-to-day easier for teams and individual contributors as they will spend less time on tedious manual work, and more time on profitable projects. Let humans contribute to high-value innovation, and robots help in maintaining and running operations to ensure an efficient and effective enterprise. To realize the true value of bots, and for a bank to embark on its digital transformation journey, the right approach, executive sponsor, business alignment, process discovery & design, pilot, roadmap, and a center of excellence (CoE) is essential to succeed. By using tactics such as data alignment, problem framing, road mapping, and piloting new robots, a bank will be well poised to reach its automation goals.

Blanc Labs has deep industry knowledge and proven experience working with leading banks to gain efficiencies through intelligent automation solutions. We take a holistic approach, helping financial services companies build the necessary foundation and setting them up for long-term success.

Book a consultation with Blanc Labs to discover the impact of our Intelligent Automation solution.

Why Banks Need Intelligent Document Processing

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

Why Banks Need Intelligent Document Processing

June 2, 2022
IDP for Banks

By Charles Payne and Donald Geerts

In the last two years, we have witnessed a consumer engagement revolution. The pandemic has seen a rush toward digital channels in all facets of life, including the banking industry. The need for instant gratification and round-the-clock support means that lenders must process customer or broker requests faster while balancing security, compliance, and risk management. Data released by the Canada Mortgage and Housing Corporation (CMHC) suggests that in the first half of 2021, the mortgage industry in Canada saw its fastest growth in the last 10 years. Given the rising demand of the market, the “need for speed” in the loan origination and decisioning process is at the top of the list.

Staying ahead of the competition requires a digital transformation that often begins with intelligent document processing as the first step. Financial institutions must partner with the right intelligent document processing (IDP) solution provider that will deliver both speed and accuracy to meet consumer expectations.

why banks need intelligent document processing

Tedious and time-consuming processes

The process of mortgage approval or renewal involves many, many documents. Before a mortgage is even approved, a mobile mortgage lender must collect and organize documents (sometimes handwritten), send them to various personnel in the financial organization to be vetted, and finally return to the customer with a yes or no—a process that can take up days or weeks. If a bank takes too long to respond to a borrower, they may turn to offers from other lenders. Such a situation is easily avoided with the help of intelligent document processing. Once a document is received, the right IDP program can classify it, extract data from the document, and store the data in a way that is accessible around the clock, not just to employees of the lender but to RPA (robotic process automation) processes as well. If additional documents are required, the RPA process can notify the mortgage agent or borrower. If the application is complete, then the RPA can send the data ahead for auto-decisioning. Using IDP in combination with RPA can ensure a quick turnaround on an application without consuming too much time.

Organizations with no digital document processing reported 10x more at-risk customers and 2x more at-risk revenue compared to other companies. (Forrester, 2020)

Inability to scale

One way to address the growing demand for mortgages is to hire, train and retain more employees. However, increasing the size of the team may result in a higher time to value (as new employees will take time to ramp up to desired levels of efficiency) and increased costs too. Lenders can benefit from IDP solutions that may be scaled up quickly with a marginal infrastructure cost.

Just digitization isn’t enough

Many lenders today receive applications through mortgage portals. While the first step of digitization of documents is taken care of, banks do not follow through to ensure the proper classification, extraction, and storage of these documents. As a result, an employee must still go through the documents to verify and authenticate their contents to ensure they are adequate for an application. It is no surprise that knowledge workers lose 50% of their time preparing documents and therefore, experience a 21% loss in productivity because of document issues.

Security and risks

Worldwide, the digital fraud attempt rate grew by 52.2% in 2021 compared to two years earlier. Banks or financial institutions that do not have intelligent document processing capabilities may be caught off guard or may not be able to respond in time to stop transactions. IDP, on the other hand, can reduce the incidence of fraudulent transactions by assessing large volumes of historical data accurately and in real-time. By identifying the patterns, an automated system can immediately flag a suspicious transaction and stop it if necessary.

KYC is another area where IDP software can help by minimizing human error. The IDP program can read submitted documents, verify the identity and details of the customer by searching through data repositories and even assign them a risk score, thereby helping the lenders meet regulatory standards.

Unaligned with consumer demands

Unsatisfactory products and fees, slow response to problem resolution, and a lack of convenience are some of the top reasons why financial institutions are are losing out to FinTechs and digital-only banks. One of the biggest contributions IDP can make is to automate repetitive manual tasks and free up lenders employees’ time in activities that will increase customer satisfaction—building trust & rapport and enhancing product offerings.

Automate document processing with Blanc Labs

There are many reasons why banks need intelligent document processing, and Blanc Labs provides a 360-degree IDP solution that can:

  • Automate workflows for document collection, digitization and analysis
  • Replace manual effort through intelligent data capture
  • Connect with third party data providers for analysis and insights
  • Analyze document data, provide status alerts, and flag fraudulent entries
  • Secure documents in a drop box
  • Deploy on premises, in the cloud or as a hybrid model

 

Book a demo or discovery session with Blanc Labs to learn about the impact of our IDP solutions for banking. 

Extraction: The Next Step in Intelligent Document Processing  

Partners | AI | Banking Automation | IDP | ML

Extraction: The Next Step in Intelligent Document Processing  

May 31, 2022
Next Steps in IDP

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

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

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

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.

4 Ways to Avoid A Failed Automation Journey 

Financial Services | Banking Automation | Enterprise Automation | IDP

4 Ways to Avoid A Failed Automation Journey 

May 25, 2022
4 Ways to Avoid A Failed Automation Journey

by Saurabh Bhatia

In recent years there has been a growing desire among financial institutions to automate processes as they move upstream to meet customer requirements. Before important decisions like loans and underwriting decisions are even made, financial institutions are deploying automation at the start of the process to provide a seamless digital-first experience.

Research shows that automation is one of the fastest and most efficient ways for financial institutions to acquire, enhance and deliver information, reduce costs and save manual labor. Then why is it that most automation initiatives fail?

4 Ways to Avoid A Failed Automation Journey

A survey by EY states that 30-50% of initial RPA projects failed to realize their expected returns. The diagnosis suggests some challenges the enterprises typically face in their automation journey which potentially cause the RPA failure. Here we explore 4 ways in which enterprises can avoid automation journey failures:

1. Give importance to change management and training 

2. Create automation champions within the organization

3. Use the right tools to understand what needs to be automated 

4. Keep employee experience at the core of the automation vision 

Let’s dive in!

 

Give importance to change management and training

Automation cannot be successful without proper implementation of change management. Even if automation is a technical matter, it relies heavily on human relations. Intelligent Process Automation (IPA) is different than your typical IT project such as ERP/CRM etc.

These projects require engagement from business users because, in essence, the role of the very same individual will evolve with the adoption of automation. Organizations that have been successful in their automation journey have put a real emphasis on the training and mobility of their teams when implementing automated processes. One of the most common examples of this is the changes in the target user interface. Any changes to the UI interface of an RPA application or system will most likely halt the automation which can put projects in a critical condition. Therefore, some organizations ask their automation core team to regularly check for changes in the ecosystem of the dependencies (in this case the UI interface) to avoid any failures.

Create automation champions within the organization 

Automation is here to stay and is evolving rapidly. However, many organizations still see their automation initiatives as a project and not as a journey. Automation today has moved away from being just a technology project and is more about data transparency and technology capability. Most organizations don’t approach automation with the rigor it requires, assuming the business workforce need only attend a few training courses and that they can, without the support of IT, generate enough extensive automation to scale a program.

IT is a critical partner throughout the transformation process. Their role is to ensure that the system is scalable, reliable, secure, and performs well. To make automation scalable, organizations should have a core team who are experts in the automation space. Successful organizations use such a core team to articulate the business value of automation well and get buy-in from key stakeholders within their organization. The core team articulates the need, advantages, and roll-out plan to each stakeholder before moving ahead with automation.

Behaviours Driving Intelligent Automation Success & Failure

 

Use the right tools to understand what needs to be automated

Not all business processes are considered fit for automation. Choosing the wrong pilot process without understanding the needs can become one of the major reasons for failed automation initiatives. The success of any automation program strongly depends on a deep understanding of how processes will get handled on-ground.

Organizations are moving towards discovery tools such as process mining, task mining, and task capture which identify automation and improvement potential in end-to-end business processes and unleash the true value of automation.

You can use “The Three Rs of Automation Discovery”, to guide your approach towards successful discovery and automation implementation.

Keep employee experience at the core of the automation vision

In recent years we have realized that automation is not about replacing human beings but helping them be more efficient and using them for strategic work rather than manual. Automation projects require even more engagement from these individuals (users) to ensure the stability of the automation program.

It is important that organizations have full management support to communicate and reassure their teams that automation is more about reducing boring/repetitive tasks so that they can focus on more interesting and fulfilling work. Think about a contact center agent in a bank who struggles daily with clients because they had to go through 15 or 16 applications to do simple resolutions. They will be better off with an easier interface where they can focus more on the clients rather than swivel chair operations.  Automation can help these contact centers to reshape the experience not only of the customer but also of the team members, which will also lead to better talent retention rate.

The Bottomline

There is no question that Intelligent automation has demonstrated exceptional results, from predicting behavior to streamlining operations. But its implementation needs to be thoughtful, effective, and pragmatic to ensure its long-term success. There are other factors that can cause a project to fail including the lack of testing and not following the best development practices. However, these 4 reasons are primary and should be taken into consideration before starting your automation journey.

Blanc Labs has proven experience working with organizations to identify and implement intelligent automation solutions. We take a holistic approach, helping organizations build the necessary foundation and setting them up for long-term success in a hyper-automation environment.

 

Book a demo or discovery session with Blanc Labs to discover the impact of our Intelligent Automation solution.

Challenges in Digital Lending

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

Challenges in Digital Lending

May 12, 2022
Digital Lending

Digital lending has evolved over the last ten years and the pandemic has only exacerbated the need for intuitive, enjoyable, dynamic, and accessible lending systems for both lenders and borrowers. In the age of Apple and Amazon, borrowers demand a seamless experience that does not involve speaking to a human being or filling out paper forms.

While traditional banks and monoline lenders are overhauling their systems to address these challenges, FinTech companies are making use of the gaps left by the big banks.

The Era of Digital Lending

According to the Canada Banker’s Association, 49% of Canadians do their banking digitally, but more importantly, 75% of Canadians intend to maintain the digital banking habits they picked up during the pandemic. Compared to five years ago, FinTech companies in the US today account for more than 38% of the personal loan space. Traditional banks, on the other hand, saw a loss of 12% of the personal loan space during the same period.

Why is this? The reason is a lack of simplicity, speed, and accessibility.

Leading FinTechs today can provide multiple quotes within minutes and fund loans within a matter of day. Apart from speed, online lenders today offer a seamless, fully digital experience as well as advanced features including security and risk assessment that does not involve a long-drawn-out credit check process.

                                                                   U.S. Digital Lending Platform Market Size

Digital Lending Challenges Faced by Lenders Today

Creating a fully automated, digital-first lending solution can be a complex process, especially if you don’t have the right tools and automation in place.

Automated self-service, compliance, fraud and cyber-security, document processing are just some of the many challenges that traditional financial institutions must overcome to match the experience offered by FinTechs today.

Here is our point of view on some of these key challenges:

1. Complicated and Slow Loan Origination Process

Can you think of living in a time before same-day delivery or tap payments? Neither can borrowers.

Companies that don’t offer instantaneous loan decisions run the risk of losing their customer to a FinTech that is faster and can provide quick decisions. Relying on antiquated loan origination processes that require filling multiple paper forms, visiting the bank in person, and taking days to evaluate risks and make funding decisions, will spell trouble. One study shows that 42% of respondents abandoned their applications because the process was too long and complicated, and 62% said they were unsatisfied with the digital experience, due to “too many touchpoints” and “the necessity of going to a physical location.”

Nimble FinTechs on the other hand are assessing credit risk and offering funds at the speed of light. One study by Smarter Loans found that 53% of respondents received their funds a mere 24 hours after applying for it, “suggesting that same-day-funding is becoming a standard in the industry.”

Thankfully, banks can implement digital native loan origination platforms that can automate the end to end loan origination process;  or they can address bottlenecks in it like underwriting, which will make the process more streamlined for both borrowers and lenders.

2. Partial measures instead of end-to-end solutions

Delivering an end-to-end solution is a mammoth task that needs significant resources and time.   The world of lending is full of large projects that took twelve to eighteen months to deliver value and this is not going to disrupt the FinTech community. Success for this is now measured in mere months.  Moreover, automating one part of the customer journey and ignoring the rest can cause more complications in the long run and can re-introduce manual intervention. Instead of a piecemeal approach, consider a unified lending solution with a modular structure that addresses all steps from information collection to underwriting, servicing, and reporting.  It is important to get the end state vision right first and then you can make incremental changes building towards your best customer journey if you elect to do things in recommended phases.

3. Document Intake and Data Storage

While traditional banks and brokers have been busy processing loans through paper-based or hardwired systems, FinTech companies are using automated processes to process loans faster and more transparently. Intelligent Document Processing (IDP) can automate the document intake process and apply AI (artificial intelligence) plus ML (machine learning) to extract data with more accuracy while converting unstructured data into structured data, making the data more useable. This can save you money and reduce human data entry error.  In most cases, this data can be prepopulated into origination and adjudication engines to drive faster straight-through processing and time to decisioning the loan.

                           Productivity loss due to manual document management

4. Data Silos and Lack of Personalization

During a digital transformation project, it is important to design the system in a way that the multiple components within that system can speak to each other and convey relevant data. If for some reason this does not happen, then it simply consumes more time—time that can be used for strategizing and planning. With so many systems in  silos, banks may not get a true 360-degree view of their business with a customer, therefore making it difficult to create personalized offers and recommendations for that customer. Ideally, banks should have a unified, transparent view of deposits, loans, and personal accounts if they want to keep the customer engaged and cross-sell new lending products over time to grow their share-of-wallet.

5. Regulations

Borrowers that come through the digital lending channel hand over a lot of sensitive information and so it makes good sense that lenders hardwire a regulatory compliance framework into their platform. Luckily, there are experts that can help business leaders stay on top of banking regulations and data privacy laws. The regulatory environment is rapidly changing and new industry driven changes around Open Banking are emerging as well. These dynamics are going to redefine and further embolden  FinTechs, drawing clear lines around how the participants in the banking ecosystem will work together.  Full assurance on regulatory reporting and compliance with industry standards is table stakes in any solution that meets this demand.

A Unified Lending Solution for Lenders

Blanc Labs provides a three-stage approach for digital lending which includes:

– Consulting assessment of your needs

– Streamlining processes and platforms

– Intelligent documentation processing (IDP)

Book a demo or discovery session with Blanc Labs to discover the impact of our digital lending solutions.