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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.

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.

thirdstream and Blanc Labs collaborating to bring intelligent document processing to financial institutions

Financial Services | Digital Banking | IDP | Partners

thirdstream and Blanc Labs collaborating to bring intelligent document processing to financial institutions

October 25, 2022
Digital Lending

Document understanding and data extraction are keys to accelerating account opening and supporting underwriting of new retail and commercial accounts.

thirdstream, a leading provider of retail, commercial and credit card onboarding services, deployed with over 40 Canadian financial institutions, today announced a partnership with Blanc Labs, a trusted technology innovation partner to leading financial institutions. The collaboration injects Blanc Labs’ Intelligent Document Automation (IDA) onto thirdstream’s platform, extending existing services to include a proven solution that removes up to 80% of manual document reviews, thereby improving the customer and member experience.

“Our goal is to help our clients continually improve the experience for applicants and account holders. Our partnership with Blanc Labs leverages our Platform-as-a-Service, where our clients will be able to spin up the services Blanc Labs has already deployed with leading financial institutions, reducing the reliance on employee reviews of documentation,” said CEO Keith Ginter. “For those already using the thirdstream platform as part of their onboarding process, Blanc Labs’ Intelligent Document Automation (IDA) helps remove up to 80% of the manual document reviews and results in considerable improvement of the customer experience.”

Automated document understanding and data extraction are some of the keys to moving faster, especially when onboarding new commercial deposits and retail lending customers. thirdstream and Blanc Labs present a structured series of steps to deliver starting with the moment of customer engagement to data extraction using artificial intelligence  from each page of documentation. This eliminates the need for the manual entry of data, with complete and accurate data presented as part of the decisioning and account creation experience.

“Our services today address challenges across the financial ecosystem. Together with thirdstream, we’d like to offer faster time to value realization with our solution. Blanc Labs’ Intelligent Document Automation eliminates manual intervention and creates a compelling value proposition for banks, financial services, and insurance institutions. By increasing operational efficiencies, banks can focus on creating additional revenue streams and provide greater value for the end customer,” says Hamid Akbari, Blanc Labs’ CEO.

thirdstream and Blanc Labs help financial institutions deploy new products and services faster – a key consideration in today’s economy – as they build improved personalized services at scale. Our solution benefits financial institutions looking for pre-integrated innovative solutions that can de-risk and accelerate their modernization and digital transformation efforts.

About thirdstream

thirdstream is headquartered in Lethbridge, Alberta, providing digital account opening solutions, online and in-branch, to over forty Canadian banks, credit unions and trust companies. From identity verification to account funding, thirdstream’s solutions support consumer and business account opening, credit card onboarding, and unsecured retail lending, including adjudication. The thirdstream platform is cloud-deployed, designed for retail and business consumers seeking out financial institutions, and for financial institutions targeting consumers anywhere, any time, from any device. To learn more, visit www.thirdstream.ca.

About Blanc Labs

Blanc Labs is a preferred partner for enterprises looking to digitize and build the next generation of technology products and services. To help companies rapidly deliver on their digital initiatives, Blanc Labs has developed expertise and bespoke solutions in a wide variety of applications in financial services, healthcare, enterprise productivity, and customer experience. Headquartered in Toronto, Blanc Labs serves the Americas through operations in Toronto, New York, Bogota, and Buenos Aires. For more information on how Blanc Labs is building a better future, visit www.blanclabs.com.

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.

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.