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Category: Enterprise Automation

Top Use Cases for Banking Automation

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

Top Use Cases for Banking Automation

January 16, 2023
Top Use Cases for Banking Automation

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

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

What is Banking Automation?

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

Why Banks Need Intelligent Automation

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

Here are seven reasons why banks needs intelligent automation:

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

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

1. Customer Experience 

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

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

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

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

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

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

2. KYC

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

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

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

3. Mortgage Application Processing

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

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

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

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

4. Report Generation

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

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

5. Anti-Money Laundering (AML) Prevention

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

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

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

6. Audits and Compliance

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

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

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

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

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

7. Decision Making

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

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

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

Blanc Labs’ Banking Automation Solutions

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

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

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

The Transformative Power of Banking Automation

Financial Services | Customer Experience | Enterprise Automation | IDP | RPA

The Transformative Power of Banking Automation

January 10, 2023
The Transformative Power of Banking Automation
Image credit: vectorjuice on Freepik

McKinsey expects machines to be responsible for up to 10% to 25% of a bank’s functions. The reasons? Banking automation minimizes the need for your team to work on repetitive tasks, allowing them to focus on high-profile and strategic aspects of the business.

Automation also improves accuracy, which can save you a ton of money — a major reason why 80% of finance leaders have implemented or plan to implement Automation (including Robotic Process Automation).

Curious about how banking automation can help? We explain everything you need to know about how automating your banking workflow can help reduce costs and improve efficiency.

What is Banking Automation?

Banking automation involves using software powered by multiple technologies like AI (artificial intelligence) and ML (machine learning) to automate repetitive tasks. Automation has three primary benefits:

  • Frees up your team’s time for more strategic tasks
  • Improves process accuracy
  • Improves the Customer Experience (CX) and the Employee Experience (EX)

For example, you can automate your account opening process. A customer requests a new account via the chatbot on your website. The chatbot provides an application form. The applicant fills out the form, and it’s sent to your RPA robot. The robot performs the basic procedures, including checking the credit score and KYC verification.

Next, the robot scans the applicant’s documents using OCR (optical character recognition) for data extraction. The robot matches the information in the documents and the application form. It flags any details that don’t match and sends them for manual approval.

The robot continues to validate uploaded documents using NLP (natural language processing). It finds key data points in the document’s free text, categorizes them, and uses them in the automated process.

The robot then updates the bank’s backend system to create a new business account, provided the customer’s data meets the bank policy. Once approved, the customer receives an automated welcome email.

Why Banks Need Banking Automation

Banks need automation to compete in the modern banking environment. Now, that’s a broad statement, so here are specific reasons why a modern bank needs automation:

  • Allowing employees to focus on tasks that require a human touch: Most banks were set up long ago. Manual forms and workflows were a foundational pillar for legacy banks, and as a result, employees spend countless hours on things like data entry and account verification. Automation allows employees to “hand over” repetitive tasks to software, freeing up their time for high-profile tasks that require a human touch.
  • Record management: RPA can generate and check expense records for compliance. It auto-logs all transactions and prepares the necessary financial records to get an overview of your business’s financial performance and position.
  • Meeting customer expectations: The need for speed is a key driver of a modern customer’s experience. If you’re taking too long for basic operations like opening a bank account, you’ll lose customers fast. Automation can help speed up your processes and help deliver on your customer’s expectations.
  • Faster customer support: Your customers hate waiting hours to get an answer. Automating your support using RPA helps you respond faster. You can answer customers’ questions at scale using a chatbot. Also, you can use an AI-powered chatbot to answer questions you haven’t added as an FAQ.

These factors make automation more of a necessity than a nice-to-have — you need automation to compete neck-and-neck with other banks.

How Banking Automation Can Transform Your Bank

Transforming your bank’s value network with automation offers many benefits in various business aspects, including finance, legal, and customer experience. Here are the benefits of using RPA in banking:

Banking Automation Leads to Efficiency

You can improve productivity by up to 80%, especially if you identify the most impactful productivity levers. The efficiency improvement is a result of two factors:

  • Low manual effort: Employees have more time available once they hand over repetitive tasks to software. They can do more in the same amount of time, helping you scale your operations.
  • Improved accuracy: Errors are expensive because you spend time and resources on correcting the errors. Fewer errors = improved productivity.

A great example of efficiency is automated document processing. As a banker, you probably spend a good number of hours reading documents and inserting relevant data into your systems, depending on your role at the bank. However, you don’t have to spend all those hours manually entering data if you use intelligent document processing.

Better Customer Experience

An average company takes over 12 hours to respond to customer service requests. That’s a recipe for dissatisfied customers, especially if you’re a financial institution.

Your customers expect their money to be in the hands of a reliable entity, and guess what you communicate when you don’t answer customers for over 12 hours?

Using RPA to automate your customer support helps minimize response times. In most cases, the chatbot can provide real-time answers to the most commonly asked questions.

Speed is also critical for other client-side processes. For example, you want to be as fast as possible in opening accounts, processing personal investment requests, or enabling additional services for an account. Automating these processes (while ensuring accuracy) helps improve customer experience.

Compliance and Risk Reporting

According to Deloitte, the cost of compliance for retail and corporate banks has increased by over 60% since the pre-financial crisis spending levels. Non-compliance is even more expensive, but automation can help lower your spending on compliance.

RPA builds compliance into your processes. Automating compliance ensures you’re always meeting regulatory requirements without requiring teams to spend extra time double-checking for compliance.

Automation also creates an audit trail and automatically generates risk reports that give you added insights. The system can identify and flag suspicious activities so that you can investigate them.

Reduced Costs

It’s easy to see how banking automation using RPA can reduce costs. Reduced administrative load, saving time on repetitive tasks, and speeding up processes all yield dividends.

For example, Radius financial group reduced loan processing costs by 70% by using AI to automate their process.

Banking automation also removes human error, so you’ll spend less on fixing those mistakes.

Without automation, you’d need to invest a large amount of money in building more teams as you scale. However, automation empowers you to scale faster. You can continue investing in training current teams and save on costs you’d incur to accommodate a larger workforce.

Automation and Adaptability

Banking automation helps banks adapt faster to a client’s needs or the business environment.

For example, the increasing popularity of Fintech is one of the most significant concerns for banks. Fintechs are quickly gaining market share at the expense of legacy banks. Customers appreciate how a fintech offers better, faster services.

Fintechs aren’t the only factor banks need to consider, though. Your bank might want to integrate banking solutions with a new partner’s ecosystem to offer additional services like tax consulting. Or your bank needs to process offshore transactions faster, especially when the transaction is subject to jurisdictional restrictions on the amount of transfer allowed.

Adaptability is critical for banks to succeed, and automation can make adapting to changes seamless. Implementing an automation solution will improve your adaptability to changes and allow you to quickly catch up with your modern competitors.

The Bottom Line

Over the past five decades, banking has gone from paper-based to almost entirely digital. Next up? Automation.

Automation makes banking frictionless for both internal and external stakeholders — it’s a win-win. The only problem banks face with automation is the lack of a reliable partner who can guide them through the transformation journey. Book a discovery call with us, and we’ll answer all your banking automation questions.

Top Use Cases of Intelligent Document Processing

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

Top Use Cases of Intelligent Document Processing

November 22, 2022
IDP

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

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

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

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

What is Intelligent Document Processing?

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

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

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

How Does Intelligent Document Processing Work?

Here’s how a document is processed using IDP:

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

Top Intelligent Data Processing Use Cases in Banking

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

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

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

Mortgage Underwriting

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

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

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

Claims Processing

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

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

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

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

Customer Onboarding

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

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

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

Financial Document Analysis

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

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

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

KYC Process Automation

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

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

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

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

The Bottom Line

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

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

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

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

Why Choose Blanc Labs Intelligent Document Processing?

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

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

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

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. 

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.

The Three Rs of Automation Discovery

Technology | Banking Automation | Digital Transformation | Enterprise Automation

The Three Rs of Automation Discovery

April 29, 2022
Automation Discovery

By Saurabh Bhatia

Before you begin the process of automation at your company, you need to check for the Three Rs of Automation Discovery which will ensure the long-term success of automation implementation. In this article, we will look at:

Automation Discovery’s 1st R: Reimagine the vision

Automation Discovery’s 2nd R: Return on investment calculation

Automation Discovery’s 3rd R: Reusing Automation

Why are The Three Rs of Automation Discovery so important?

 

So you’ve decided that you want in on the automation game. Great! Now you’re in the all-encompassing process of discovery and implementation. This is where you, the organization that is looking to adopt automation, needs to articulate your business needs, asses how automation will impact your operations, and then determine a successful implementation roadmap with your tech partner. Here are three components, “The Three Rs”, that should guide your approach towards a successful discovery and automation implementation.

Automation Discovery’s 1st R: Reimagine the Vision

Begin by reinventing the way you look at your operations and its processes. It goes without saying that you should start with standard process automation first to demonstrate its value. But during the discovery phase, explore all tools that will compound the value of automation for your organization. For example, if you are looking to automate the mortgage lending process, take a step back and examine the added benefit your organization will get from adding power BI dashboards or analytics systems that provide more actionable insights or discovery tools which identify which other processes and tasks could be automated.

Keep in mind a vision of an organization fueled by hyperautomation as you explore various options in addition to standard process automation namely, intelligent document processing, business intelligence reporting, process mining, chatbots, etc. There is a full stack of options you can explore to heighten the benefits of technology and establish that future state of hyperautomation.

Automation Discovery’s 2nd R: Return on Investment Calculation

Understandably the most common driver in decision making is about the returns you will see with the option of automation (or any other technology solution). Determine how you will calculate ROI prior to beginning work on the solution. We recommend creating a business case with a very clear prioritization matrix and roadmap of automation and its benefits. This will determine the focus both long term and short term.

Pro Tips:

  • Determine which licenses and automation tools are most useful and cost effective for the overall solution
  • Adopt licenses and tools and build your infrastructure to support the scalability of automation (and sunset those that are no longer in use)
  • Don’t forget, to consider monetary metrics which deeply impact the business in your calculations

Automation Discovery’s 3rd R: Reusing Automation

Some organizations feel like they’re starting from scratch every time they introduce a bot to their processes. In reality, you reutilize some parts of your previous assets (from one bot to another) to make automation faster. For example, if you have multiple processes, teams and departments using the same application or program, we encourage you to create reusable assets with the first bot so when you introduce a second bot, you are reutilizing the same assets with the same application or program. This fuels enterprise scale adoption as a team or functional area and can demonstrate the value of automation to other departments, which will encourage further adoption of the technology.

Pro Tips:

  • Divide your projects into components and create reusable libraries, which makes it easier to reuse automation for further processes
  • When designing the bots, focus on smaller components within them but always keep in mind how this will affect future projects in a positive way

Why are The Three Rs of Automation Discovery so important?

  1. By reimaging your vision, you create a collective, growth oriented and collaborative mindset within the organization as it pertains to digital transformation
  2. The right ROI calculations help you determine which processes will garner better returns both short term and long term
  3. Reusing automations also makes your implementation scalable with faster adoption across teams and departments

Automation is transforming the way we run businesses, transact, and engage in the world. The key to achieving our vision and maximizing the possibilities of technology in our organizations is rooted in having the right approach. And the three Rs in automation is great place to start.

Find out more about enterprise automation, book a meeting with us today to see how we can accelerate your digital transformation journey.

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
Classification

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.

Why?

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!

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 loan processing experience.

The Human Effects of Hyperautomation: Redefining processes and business models

Technology | AI | Digital Transformation | Enterprise Automation | IDP

The Human Effects of Hyperautomation: Redefining processes and business models

January 31, 2022
Hyperautomation

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.