Articles

Three Reasons Financial Institutions Are Losing Out to FinTechs

…And How to Keep Up with Digital Natives 

by Bob Paajanen and Charles Payne

Reasons why Banks and Losing Out to Fintechs

The way we bank has changed forever. While FinTechs have the latest technology innovations, what they don’t have is decades-worth of relationships with customers and large swaths of Big Data. Financial institutions need to recognize this advantage, leverage their data, streamline processes, and thereby empower their relationship managers if they want to compete with their new-age rivals.  

Mismanagement of Data 

Most financial institutions have multiple customer-facing systems that operate in their own silos. As many as 50% of banks and credit unions state that they have trouble accessing their internal data. Without a single unified view of their customer, banks and credit unions are unable to collect, process, or indeed deploy insights that will enable them to cross-sell products and services to their customers. 

The services gap left by financial institutions is especially felt in commercial banking where FinTechs are sweeping up SMBs with targeted products and quicker access to funds. A prominent example of this trend is Shopify, which started out as an e-commerce platform, but is now the tenth-largest provider of financial services to SMBs. Another example is Stripe, which has created an end-to-end lending API (application program interface) as its next offer to SMBs. 

Paper-heavy processes, and disparate data management systems are some of the major causes of this issue. As the finance industry moves toward open banking, it is imperative that financial institutions unlock the value of their data and translate it into actionable insights so they can improve their languishing businesses.  

why financial institutions are losing out to fintechs
Source: 11:FS ‘Fintech filling services gaps’ Designing digital financial services that work for US SMBs 

Lack of Efficiency 

A 2019 Gartner report estimated that process automation, including document processing, could save financial institutions 25,000 hours of avoidable work per year. With advances in technology in the last three years, it is not hard to imagine that this number may have gone up even further.  

Most of the productivity loss mentioned above has been attributed to human error. This is hardly surprising when many financial institutions continue to use paper-heavy loan origination models. Without automation, document processing is rife with efficiency and security issues including document mishandling, collaboration on email (generating multiple copies of the same document), versioning issues, loss of time to find the documents when required, a lack of compliance, and a lack of remote access. 

Since the pandemic, consumer expectations have changed dramatically. Close to 60% of customers today are open to completing their mortgage applications entirely online without support on the phone or in person. Even more pressing than the platform, is the need for speed, with customer satisfaction falling 15 percentage points for approvals that take longer than 10 days.  

With unprecedented demand for mortgages, financial institutions must speed up intake, underwriting and decisioning processes faster if they want to keep the customer’s business. 

Costs  

Inefficient loan origination processes lead to rising costs. On average, loan origination costs $7-9k per application. That is over and above the cost of productivity loss, estimated to be $878,000 (for 25,000 hours lost per year) for a company with a 40-person finance team. This cost is invariably passed on to the customer who may end up paying higher fees and charges compared to what they might pay if they opted for a non-banking entity.  

rising cost of loan origination

The FinTechs Are Coming… And how to slow them down 

As a result of service gaps and inefficiencies, customers from both commercial and retail banking have been veering towards nonbanking entities for loans. 50% of Canadian SMBs in 2021 felt that they couldn’t maintain their growth strategies “due to a lack of capital.” Today, nonbanking entities, account for more than 70% of total originations. By using automated processes and digital interfaces, non-banking entities or FinTechs are 25% cheaper than the industry average and can deliver a decision 30% faster compared to other financial entities.  

In the age of Open Banking, it is important that financial institutions update their legacy processes and unlock the potential of their data if they want to survive.  

The automation journey begins with intelligent document processing. Blanc Labs provides a 360-degree IDP (Intelligent Document Processing) 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.  

 

Articles

Why Banks Need Intelligent Document Processing

By Charles Payne and Donald Geerts 

Why banks need intelligent document processing

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.  

Articles

Extraction: The Next Step in Intelligent Document Processing  

By Luciano Lera Bossi, Alejandro Nava and Parsa Morsal  

intelligent document processing extraction

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.  

  

Interested in hearing how we can accelerate your digital transformation?