Intelligent Lending Resources

The CRA is set to undergo a significant transformation starting January 1, 2026.

The FDIC, FRB, and OCC finalized a major overhaul of the Community Reinvestment Act on October 24, 2023. The interagency rule initially went into effect on April 1, 2024, but the agencies issued an interim rule extending the date to January 1, 2026. Data reporting requirements will go into effect in 2027.

The update was driven, in part, by financial institutions’ shift from a reliance on physical locations to more digital and mobile interactions. 

In this post, we’ll review key changes to the CRA and its implications for small business lending.

Required performance tests by size.

The breadth and depth of scrutiny will still depend on the financial institution's size. The rule breaks institutions into three size categories: small, intermediate, and large. It also gives small and intermediate banks the option to continue to use previous performance tests or opt-in to new performance measures.

New performance tests include:

Revised Retail Lending Test: Measures the lending volume compared to the bank’s depositor base within the bank’s facility-based assessment area and evaluates the geographic and borrower distribution of the bank’s major product lines across all its assessment areas.

Retail Services and Product Test: Measures the availability and responsiveness of the bank’s retail products.

Community Development Financing Test: Combines the community development loans and investments in community development financing metrics and adds an impact and responsiveness measurement.

Community Development Services Test: A qualitative assessment of a bank’s community development service activities, such as volunteer work or serving on a nonprofit board.

New geographic assessment areas.

The interagency rule created new geographic assessment areas to address financial institutions' expanding area footprint. 

Retail lending assessment areas
In addition to an institution’s facility-based assessment area, large banks will now also be assessed via a retail lending assessment area. The retail lending assessment area consists of a metropolitan statistical area or the nonmetropolitan area of a state in which the bank has a concentration of closed-end home mortgages (150) or small business lending (400). Large banks would be exempt from adding a retail lending area if there is more than 80% overlap between their facility-based and retail lending areas.

Outside retail lending area

Large banks and certain intermediate and small banks may also be assessed by an outside retail lending area if the bank conducts most of its retail lending outside its facility-based and retail-lending assessment area.

Direct loans are now considered a qualifying activity.

The new rule expands economic development activity under the Community Development Financing Test to include direct loans made to small businesses and farms that meet a size and purpose test. 

To qualify, the loan must be issued to small businesses that meet the size standards for an SBDC or SBIC or have gross annual revenues of $5 million or less. The loan must also meet the purpose of promoting permanent job creation or retention within low-to-moderate-income communities. 

While loans issued in conjunction with an SBDC or SBIC will be presumed to meet the purpose requirement, other types of loans, including SBA loans, will now also be considered if they meet the size and purpose criteria.

Additionally, small business loans can be counted under both the Retail Lending Test and Community Development Financing Test if they qualify under each category.

Concerns with the new rule.

While the rule passed, FDIC Board of Directors Vice Chair Travis Hill and Director Jonathan McKernan, and one Federal Reserve Board member, Michelle Bowman, voted against the Final Rule, citing concerns with the rule's complexity and cost to implement. 

In a statement, Michelle Bowman outlined several reasons for her dissent including the possibility of it being more difficult for a bank to get a passing grade under the new requirements. 

“As described in the materials before the Board today, based on changes to the retail lending test alone, nearly 10 percent of banks would be rated “Needs to Improve” based on data from 2018 to 2020. Today, the number of banks with a “Needs to Improve” rating stands at roughly one percent.”

She also argued that the body doesn’t have the data to determine the impact of the new community development financing test on banks’ CRA ratings.

Additionally, a lawsuit has been filed in a U.S. district court in Texas alleging the agency has gone beyond its authority by including areas outside the bank’s geographic areas and including non-credit products in the evaluation procedure. On March 29, 2024, the court granted a preliminary injunction pausing enforcement of the new rules until the lawsuit is resolved. The current injunction only applies to the plaintiffs in the lawsuit.  

Potential impact on banks.

The consequences of the rule, intended and unintended, are up for debate. Some argue that banks will have an incentive to stay small to avoid the more stringent requirements placed on intermediate-sized banks. There could also be an incentive to avoid offering services in expanded areas to avoid needing to create a new assessment area, reducing the number of small businesses being funded. 

However, the rule also rewards small business lending by including additional direct loans as a qualifying activity under the Community Development Financing Test and by allowing those direct loans to be counted within every test it qualifies for. 

As with any rule change, the new CRA rule brings new compliance risks for banks. Banks will need to get familiar with the lengthy rule and the evolving evaluation methodology all while managing a potentially wider assessment area. 

The case for small business lending.

While the outcome of the impending lawsuit is still up in the air, it’s clear that there’s increasing recognition of the value of small business lending. Through SMB lending, banks can meet (in part) CRA requirements, grow wallet-share with depositors, and grow lending opportunities.

However, banks face several challenges in lending to small businesses. Small businesses seek smaller loan amounts, making it cost-prohibitive for the bank to underwrite the loan with manual processes and legacy origination software.

Now, imagine a bank that has developed a significant depositor base in an inner city or rural area where it doesn’t have physical branches or loan officers available. Current systems are dependent on in-person meetings and printed paperwork, making it difficult for the bank to meet compliance criteria in the expanded area.

This bank now has two problems to solve. 1) How to effectively target and reach eligible borrowers in the expanded area and 2) How to provide those services cost-effectively. 

Banks using Lendio Intelligent Lending can overcome the second challenge with automated underwriting technology that can complete rapid, low-cost analysis of applicants based on their lending policy. 

To address the first challenge, the bank can offer loan products on Lendio’s online marketplace, which enables access to a broader spectrum of borrowers than a bank can normally serve.

Now, the bank can cost-effectively originate loans to qualified small business owners actively looking for a loan within the service area boosting compliance and total loan volume. 

84% of banks already see SMB lending as a significant opportunity for growth. Now, with updated technology banks can generate revenue and meet growing compliance requirements with a single solution.

Learn more about the opportunities in small business lending. 

Discover how Lendio Intelligent Lending can increase your lending staff capacity.

With the right strategies in place, banks can turn SMB lending into a new profit center while maintaining wallet share on third-party lenders.

Growing deposits was a main priority for banks in 2023 and will likely continue to be a main focus through 2024. Banks’ concerns over small business deposits, specifically, increased from 41% in 2022 to 72% in 2023. At the same time, many banks have deprioritized lending. However, data indicates that rather than being an either/or scenario, deposits and lending have a symbiotic relationship. In a recent survey, 66% of respondents indicated that lending is critical to their small business deposit retention strategy.

Deposit relationships are far stronger and more profitable when cross-sold with a lending product. Data from Curinos found primacy drives 8x fee revenue, 10x deposit balance, and 7x operating deposits compared to non-primary customers.

Yet, some lending products provide wider coverage of the deposit base than others. A commercial real estate loan, for example, serves one depositor while the same assets could serve 50 smaller businesses when packaged as SMB loans. The commercial real estate market is also becoming increasingly risky for lenders as prices have dipped 11% due to rising interest rates and lower demand for office and retail spaces. 

Given that SMB deposits are far less costly and SMBs with a lending relationship drive 2.3x more deposits, profitable SMB lending may be the crux of navigating the current financial landscape and staying competitive in the future.

Prequalify existing depositors.

However, lenders have traditionally thought about SMB lending as an independent upsell rather than relying on deposit analytics as the basis to grow the relationship. By combining deposit data with third-party sources, banks can accurately prequalify business owners for a loan against a transaction policy. 

Now, instead of an impersonal direct campaign encouraging all depositors to apply for a loan, the bank can send a highly personalized message indicating the business owner has been prequalified for a loan up to the determined amount. This not only improves the impact, and by extension ROI, of those marketing campaigns, but also shows the depositor that their bank or credit union has their back. 

Of the over 6 million employer firms in the U.S., 40% seek a loan, line of credit, or cash advance in a given year. While the number of SMBs that initially respond to a prequalified loan will be lower, the uptake will grow over time as the timing matches up with the business’s needs and cyclicality throughout the year.

Build a digital loan experience.

The seamless experience described above highlights another attribute of small business lenders. SMB lenders' preferences in how they interact with a bank or credit union are much more like a consumer than corporate or commercial borrowers. This includes a preference for digital experiences. 48% of borrowers now prefer to apply for a loan or line of credit via an online lender or a bank’s mobile app. 

To meet these borrowers' needs, financial institutions need a digital loan experience that is fast, personalized, and secure.

The good news is that technology that makes SMB lending faster can also make it more secure. An underwriting solution with automated checks of business ID and criminal background creates a faster, less intrusive experience while better identifying potential fraud cases.

Boost SMB lending profitability.

Of course, increasing revenue from deposits doesn’t do much good if the bank loses too much revenue for every SMB loan deal closing. Many banks without automated decisioning then end up in a Catch-22. They need to increase their volume of SMB loans to improve depositor retention, but SMB lending is itself unprofitable. Banks that adopt automated decisioning and underwriting, however, can dramatically reduce the amount of manual labor required to underwrite an individual loan and turn SMB lending into a new profit center for the bank.

Maintain wallet share on third-party lenders.

The demand for funding in the SMB market is huge, and those SMBs will continue to seek it wherever they can find it. 30 years ago banks owned deposits and lending, but accessibility to SMB loans decreased with the decline of community banks. Then the 2008 financial crisis spurred the creation of alternative lenders who offered an alternative to small businesses. In the latest small business credit survey, 33% of SMBs applied for credit via an online lender or fintech company.

Now, many fintech companies are moving beyond lending into deposits. Block, for example, recently announced in their annual letter to shareholders that they see their Cash App Card as a gateway to customers adopting Cash App as their primary banking solution. 

In the letter CEO Jack Dorsey highlighted the value of a customer with a lending and deposit relationship, “We see a meaningful step up in value and engagement as customers choose to deposit their paycheck with us: Cash App Card actives who deposit at least $2,000 of paychecks per month spend nearly 6x more than Cash App Card actives who do not deposit a paycheck with Cash App.”

As fintech companies continue to dive further into the world of deposits and discover their value, they’ll likely shift from seeing deposits as a nice addon to a core product they aggressively promote.

Wholistic offerings are now paramount.

Growing deposits is critical in the current economic environment, but deposits are no longer a guaranteed stronghold of the banking industry. Competition is continuing to grow not just from alternative lenders but from digital-first companies that offer a streamlined customer experience. Banks and other financial institutions will need to cement their foothold in the market through increasingly complete, efficient, and digital offerings for SMB depositors – and this must include profitable SMB lending. 

Section 1071 of the Dodd-Frank Wall Street Reform and Consumer Protection Act (Section 1071) would be easy to see as yet another time-consuming compliance project. Smart institutions will see it as a moment to embrace the possibilities of data and technology.

In a 2022 survey of banking leaders, 45% of respondents indicated their bank relies on outdated technology with 48% stating they are not effectively using/aggregating their bank’s data. Given that many banks use old, legacy core banking and loan origination systems, it’s unsurprising that so many banks face challenges with effective data management. New regulations under Section 1071 are creating a moment for financial institutions to reckon with how they will better leverage data and technology in the future.

What is Section 1071?

On March 30, 2023, the Consumer Financial Protection Bureau (CFPB) released its final rule implementing small business lending data collection requirements under Section 1071. The goal of the rule is to help facilitate the enforcement of fair lending laws and identify needs and opportunities for women-owned, minority-owned, and small businesses.

Key facts for financial institutions.

The CFPB has released thorough documentation of the regulations for banks and other financial institutions to follow. Below is a summary of key details.

Implementation deadlines

As part of the ruling, the CFPB created a tiered compliance schedule with the first tier required to begin data collection by Q4 of 2024. Each tier is based on the number of “covered originations” –a covered credit transaction– made to a small business including declined, withdrawn, and incomplete applications. Under the ruling, a small business is defined as any business that made $5 million or less in gross annual revenue from the preceding year. Note that credit renewals, extensions, and amendments are not counted as a covered origination.

The original deadline was set for data collection to begin in October 2024. The ruling was stayed last year pending a Supreme Court decision in CFPB v. CFSA. The Supreme Court decided on May 16, 2024, in favor of the CFPB. The CFPB has now issued an interim ruling pushing back the original deadlines 290 days with a first data collection date of July 18, 2025.

Data collection requirements

The rule requires the collection of specific data points under three categories.

Data points to be generated by the financial institution.

• Unique ID
• Application Date
• Application method
• Application recipient
• Action Taken/Date
• Reason for denial
• Approved amount
• Pricing information (interest rate, origination charges, broker fees, initial annual charges, prepayment penalties, costs for merchant cash advances or other sales-based financing)

Data points to be provided by the applicant or a third-party source.

• Credit type
• Credit purpose
• Amount applied for
• Address or location for purposes of determining Census tract
• Gross annual revenue for the preceding fiscal year
• NAICS code (or information about the business such that the covered financial institution can determine the NAICS code)
• Number of employees
• Time in business
• Number of principal owners

Data points to be collected based on demographic data.

• Minority-owned, women-owned, and LGBTQI+-owned business status
• All principal owner’s (25% equity in the company) ethnicity, race, and sex

Under the ruling, the financial institution must request demographic information but cannot require it. If an applicant declines to provide the information, the institution should report that the applicant declined. 

Some applicant-provided data points such as the NAICS code are notoriously difficult to collect accurately. The ruling does not require lenders to verify the accuracy of applicant-provided data including if that data is pulled from a 3rd-party source.

While banks are already documenting denial reasons under Adverse Action rules, banks who previously relied on oral notices and notations for businesses with $1 million in gross revenue or less will need to adapt their system to ensure all of the denial reasons are recorded consistently to make aggregating and reporting the information easier.

For those institutions engaged in partnerships with brokers or banking-as-a-service providers, ultimately it is the party responsible for setting the terms of the credit transaction (the financial institution) that will be required to collect and report data to the CFPB. That being said, fintech tools can be a powerful tool to help banks improve their data collection techniques by collecting and storing the data at the time and place that makes the most sense to the customer.

Firewall requirements 

Perhaps one of the more difficult requirements created by the rule, financial institutions are required to shield demographic data from underwriters. Today, most underwriters typically have access to the entire borrower file. To fully comply with the ruling, FIs will need to build new systems for collecting, storing, and reporting data. For smaller community banks, the firewall requirement creates a unique challenge since employees often serve multiple roles. If it’s determined that an employee in the loan decisioning process must view demographic data, the bank must give notice to the applicant. 

Current lawsuits and their implications.

Multiple lawsuits have been filed combatting the small business lending rule. On Oct. 26, 2023, a U.S. District Court in Texas issued a nationwide injunction prohibiting the rule's implementation or enforcement, pending a ruling from the U.S. Supreme Court. Since the Supreme Court ruled in favor of the CFPB, all compliance deadlines have been extended based on how long the stay was in place. A Kentucky federal district court issued a similar injunction. In December 2023, the House voted to overturn the CFPB’s final rule, but the resolution was vetoed by President Biden. Congress is not expected to have the votes to override the veto.

Data requirements won’t be going away.

Regardless of ongoing litigation, data collection requirements are unlikely to disappear. A Thomson Reuters survey of compliance leaders found that 73% believe regulatory changes will increase over the next 12 months with only 2% expecting regulations to decrease.

Embracing data

If nothing else, this ruling should serve as yet another reminder to banks to upgrade outdated systems and start looking for ways to turn data collection into an asset instead of a burden. While the CFPB has its reasons for creating a database of relevant SMB data, banks already have extensive databases of highly valuable data waiting to be leveraged. Depositors’ transaction data can be analyzed not only to prequalify potential borrowers for a loan but also to provide more accurate and fairer underwriting decisioning for SMBs – the very area the CFPB is seeking to monitor more closely.

Technology upgrades

Much of the stir around Section 1071 has been around the implementation deadlines indicating the rules touched on another sore spot in the finance community- many systems are too clunky to adapt quickly to changing environments. Partnering with fintech companies that know how to build flexible systems can serve as a stopgap as banks adjust their in-house technology. 

Increasing oversight

While it hasn’t received as much attention, one key detail to note about Section 1071 is that it doesn’t just apply to traditional lenders. Non-traditional lenders, such as those that provide merchant cash advances, are also required to collect data under the ruling. While fintech lenders have avoided a lot of oversight in the past, this will likely be the first of many rules impacting them moving forward.

Leveraging partnerships to enable compliance.

Fintech partners can play a critical role in enabling compliance with the rule. Partners can help banks collect legally required information with less friction by collecting and storing the data at the time and place that makes the most sense for the customer. Demographic data can then be safeguarded and only shared with the lender when appropriate helping financial institutions meet firewall requirements. These partners may also be used as a central repository for collecting declination reasons and generating reports required under Section 1071. 

The opportunity in SMB lending.

A recent Lendio survey found that 84% of banks see SMB lending as a high priority; yet many banks struggle to scale SMB lending programs profitably due to manual systems and siloed datasets. While it may not be the original intention of Section 1071, the ruling may spur banks to adopt the systems they need to turn SMB lending into a powerful source of revenue. Read the entire small business lending survey here.

Discover how Lendio Intelligent Lending can increase your lending staff capacity.

As banks increasingly partner with fintech companies to expand their customer bases and sources of revenue, regulatory scrutiny has grown, challenging banks to balance the need for innovation with stringent regulatory requirements.

As banks have increasingly partnered with fintechs to expand their customer bases and sources of revenue, they’ve come under closer scrutiny from regulators. In 2022, the OCC ordered Blue Ridge Bank to improve its oversight over third-party fintech partners, and the bank must now obtain a non-objection from the OCC for any new partners or products it adds.

More recently, Cross River Bank entered a consent order with the FDIC over fair lending compliance that required the bank to strengthen lending and third-party compliance controls and prepare assessments on fair lending compliance.

While banks are heavily monitored and regulated, their fintech partners don’t receive the same level of oversight. Despite this, the bank is still responsible for ensuring that any interaction it facilitates through a partner meets compliance requirements. 

So how did we get here? And how can banks balance the need to meet stringent regulatory requirements while continuing to innovate?

What is Banking as a Service (BaaS)? Why does it concern regulators? 

In the last 10 years, and particularly over the last five years, fintech growth has rapidly accelerated. At least 30% of the banking industry’s revenues from products such as payments, personal loans, and credit cards are sourced through third parties, making up $2.6 trillion of total U.S. financial transactions in 2021. By 2026, that number is projected to reach $7 trillion.

While many of these providers are seen as competitive to traditional banking, most actually continue to rely on financial institutions to facilitate some portion of their activities. In fact, studies have found that regardless of age, customers are 2.5 times more likely to purchase embedded banking products if the product is managed by a bank partner. 

Lending is a common example. In this scenario, the bank sets the parameters for their loan products and the fintech partner uses those parameters to lend money from the bank’s balance sheet. 

In some respects the model might seem harmless—if the bank sets the loan parameters and the fintech executes them correctly, then there’s no harm. However, this model erodes a fundamental principle of banking, which is that the bank (or credit union) is ultimately responsible for assessing borrower risk and making a decision.  

Amidst the proliferation of the BaaS model and growing concerns amongst regulators, a group of U.S. government regulators recently released interagency guidance relating to relationships with any third-party partner, including fintechs.  

“Whether activities are performed internally or via a third party, banking organizations are required to operate in a safe and sound manner and in compliance with applicable laws and regulations. A banking organization's use of third parties does not diminish its responsibility to meet these requirements to the same extent as if its activities were performed by the banking organization in-house.”

The regulators have continued to maintain a position that any type of third-party relationship, including fintechs, is effectively an extension of the bank itself. In the case of the aforementioned regulatory fines, the fintechs (and therefore, the banks by extension) were not adhering to applicable regulations, therefore requiring the regulators to fine the banks and not necessarily the fintechs.  

How can banks expand distribution and drive efficiency while maintaining control? 

Despite the regulatory pressures and risks, fintech partnerships do serve an important role in the market, from advancing digital experiences to automating risk analysis. The good news is that advancements in technology coupled with business model evolution are enabling these benefits to co-exist with regulatory frameworks.  

Imagine a world in which a lender can fully utilize fintech innovation, while still being able to review each loan individually without sacrificing timeliness or thoroughness.  

Consider Lendio’s Embedded Lending Marketplace, which allows partners with large networks of potential borrowers to embed a lending marketplace within their own branded platforms and ecosystems. The model works like this:

1. Using data sourced through the partner, Lendio is able to pre-populate a significant amount of the data required for a loan application.  
2. That data is then supplemented with additional data supplied by the borrower and/or sourced from third parties such as credit bureaus, KYB vendors, and bank data aggregators.  
3. This application data is then shared with prospective lenders through an API to an existing LOS or Lendio’s Intelligent Lending platform. 
4. Those lenders then return instant offers (under 15 minutes) within the embedded experience on the partners’ platform. The borrower can then select the offer that best matches their needs before meeting final stipulations and proceeding to funding.  
5. Final loan documents are customized to the lender, but are presented and signed within the embedded ecosystem.  

While this approach may sound remarkably similar to the aforementioned BaaS models, the key differentiator is that the lender maintains full control over loan decisioning and documentation ensuring that all loans issued follow its risk policy and other compliance regulations.

How these models are equally relevant to traditional channels.

Many of today’s banks who are participating in fintech partnerships and BaaS models are doing so as a largely independent strategy from their traditional community or regional banking relationships. Some have even gone as far as to create a separate division, implement a new core, or create legal separations in the business. While those steps may be useful to drive focus on BaaS, they do little to help the bank’s existing business.  

Lendio’s digital borrower experience and automated underwriting technology described above can be used both in embedded finance and to lend to the bank’s own customers. The challenges here are arguably more acute, where most lenders rely on paper-based processes to originate and decision loans. Fintech partnerships (but not of the BaaS variety) can help drive digital adoption, heighten the use of analytics, and improve underwriting efficiency.

Lendio’s unique capabilities

Some of the unique features Lendio offers traditional channels include:

Distribution: Access to embedded finance, Lendio marketplace, pre-qualification of the bank’s depositors, or new loan applicants 

Branding: Ability to build new borrower relationships by using the bank’s branding on all documentation, regardless of which channel first brought the borrower in

Borrower analytics: Insights into the borrower through self-reported data, credit bureau pulls, KYB data, and bank transaction data 

Smart decisioning: Use the lender’s credit policy to evaluate applicants across a wide range of attributes gleaned from borrower analytics through a machine-assisted or fully automated system 

Competitive intelligence: Insights into comparable institutions and their SMB loan programs with real-time analytics, benchmarking, and proposed policy adjustments 

Servicing: The lender closes and funds the loan and is responsible for servicing and collections

Solutions for risk management

As discussed in the updated guidelines, risk management of third-party vendors is an ongoing process from planning and onboarding to ongoing monitoring and the eventual termination of the partnership requiring compliance, technology, and legal resources. Luckily, technology can help ease some of the risk management burden as well, from automated underwriting and compliance checks to better fraud detection. Banks that are quick to adopt technology that eases their regulatory burden will be able to move faster in taking advantage of third-party partnerships.

From fraud detection to regulatory compliance and credit analysis, tech tools are poised to make risk management easier, faster, and more reliable.

One could argue the entire underwriting process is about evaluating risk. Yet, manual underwriting processes make it expensive to evaluate a borrower and certain types of analyses are unfeasible to perform manually, no matter how many resources you have. 

For example, 86% of banks say transaction analysis is more important than credit scores for driving small and medium business (SMB) lending decisions, with 9 out of 10 banks employing some form of transaction analysis for decisioning. 

However, the majority are doing only a cursory review of activity levels and minimum balance thresholds rather than performing highly detailed analyses because inefficiencies and manual processes within the loan origination system (LOS) prohibit deeper transaction analysis. On the other hand, of those banks that rate their LOS as efficient with SMB loans, 68% are performing highly detailed transaction analysis to qualify and underwrite borrowers.

Technology presents new opportunities for banks to efficiently mitigate risk across three crucial areas: credit risk, financial crimes, and regulatory compliance.

1. Credit risk

Current processes for credit risk analysis in loan origination rely heavily on personal and business credit scores. This leaves many small businesses—namely those that may have a profitable business but poor personal credit or that deal primarily in cash—at a disadvantage. This also leaves banks reliant on the credit bureau’s evaluation of the borrower. To fix this shortcoming, banks should use technology to independently evaluate each variable within the score.

This starts by combining existing account data with data from third-party banks through aggregators like Finicity, Plaid, or Yodlee. These statements can be ingested and turned into machine-readable text that can be used for detailed transaction analysis. Add to that automated KYB checks, identity verification, and tax document pulls, and in minutes, the bank has a deeper analysis of the borrower’s credit risk than it ever had before with hours of manual processes.

AI and machine learning can also be used to improve credit risk monitoring. For example, AI can monitor a borrower’s spending habits, credit score, and payment history and send an alert if those metrics start to indicate potential financial difficulty and a greater risk of default.

2. Financial crimes

Despite multiple due diligence checks in place, fraud still remains an issue for SMB lenders. SMB lenders reported a 14.5% increase in fraud from 2021 to 2022. Sixty-eight percent of that fraud was caught after the point of origination. While a lot of fraud goes unreported, investigations into the PPP loan program found that at least 17% of all COVID-19 Economic Injury Disaster Loans and PPP funds were disbursed to potentially fraudulent actors, amounting to over $200 billion. 

While banks have responded to fraud by hiring more staff and tightening restrictions on online transactions, ultimately the bank has to strike the right balance between asking enough questions to ensure the applicant is legitimate while not creating too much friction for the borrower.

The good news is that a robust underwriting solution can layer automated checks of business ID, bankruptcies, judgments, UCC filings, criminal background, and current liens into the customer journey. This, in turn, allows banks to evaluate more fraud risks without creating an experience that’s overwhelming or difficult for the customer to use. In fact, of those banks that use layered fraud mitigation solutions, 50% are able to catch fraud at the point of origination versus the 18% that don’t use a layered approach.

Incorporating transaction analysis into underwriting criteria can also help reduce fraud. If your origination system sees a healthy variation in transactions and no suspicious transactions or statement tampering, then the borrower is unlikely to be fraudulent.

3. Regulatory compliance

Of all their top risk category priorities, 53% ofcommunity banks placed compliance in the top three. Staying on top of new compliance regulations is a monumental task. This year alone, changes have been made to SBA and Community Reinvestment Act rules, and CFPB 1071 added new data collection and reporting requirements for SMB lenders.

Technology with natural language processing capabilities can help banks stay on top of changing regulations by analyzing documents and extracting key points of information. 

Automated underwriting systems can help banks collect legally required information under CFPB 1071, such as gender or veteran status, by collecting and storing the data at the time and place that makes the most sense for the customer.

Within compliance, 79% identified fair lending and 35% identified third-party or fintech partners as top priorities.
While disparate treatment can be fairly simple to avoid within risk policies, disparate impact can be harder to account for. For example, credit scores are commonly an ineffective tool for measuring the creditworthiness of underserved communities. Studies support creating a well-rounded and well-documented approach for evaluating borrowers, which can help reduce the potential for bias and create fairer outcomes for those communities.

Bank risk management as a revenue-generating function

While automation can improve efficiency and reduce costs across the three areas discussed, perhaps one of the most interesting use cases for technology in bank risk management is revenue generation. 

Many current risk models are binary—if a borrower barely misses the minimum monthly revenue threshold, it’s an automatic rejection, even if other factors indicate a good risk potential. 

For example, take two SMB borrowers with the following profiles.

Historically, banks might have a fixed SBSS threshold of say 170, meaning that Borrower A in this example would receive funding, while B would not. But in fact, the data would say that Borrower B is actually a very low credit risk.  

Now, imagine a more complex profile developed by regression analysis of past loan approvals optimized for expected outcomes. By using a more holistic approach, the bank is able to adjust its minimum thresholds and optimize how it evaluates risk, all while potentially approving more borrowers with positive outcomes that may have been overlooked in simpler models.

In this scenario, risk management technology transforms from a pure cost function to a tool that can be used to drive new revenue for the bank.

Low risk, high reward

It’s a rare opportunity for a financial institution to simultaneously reduce risk, improve the customer experience, and drive new sources of revenue. However, as banks evaluate priorities and budgets, reframing how they think about risk management could have a huge impact on the bottom line.

Learn more about automation in lending.

Discover how Lendio Intelligent Lending can increase your lending staff capacity.

The banking industry is in a state of rapid transformation. Amidst these challenges, banks have opportunities to create new revenue streams and establish themselves as leaders in customer experiences.

Few, if any, would argue with the statement, “Customer experience matters.” Banks with great customer experiences have customers who are 1.9x more willing to recommend their services and 2x more likely to adopt new services. Yet, only 30% of respondents would rate their primary bank’s customer service as excellent, and 59% recently acquired financial services from a provider other than their primary bank.

Part of this disconnect can be explained by a rapidly evolving industry, combined with changing consumer expectations, including:

• Emerging competition - Growth of the Fintech industry creates both new competition and opportunities for partnerships 
• Digital adoption - Adoption and expectations of digital banking services are growing and evolving rapidly.
• Changing expectations - Expectations for fast and seamless application and onboarding experiences are becoming widespread.
• Industry consolidation - Recent mergers have reduced small businesses’ access to local banks in small, rural communities.
• Third-party partnerships - The number of consumers purchasing financial products via third parties is on the rise.

While these challenges are daunting, they also provide an opportunity for banks to identify and build new revenue streams, while differentiating themselves from the competition by creating exceptional customer experiences. Banks looking for a competitive advantage should consider the following five trends.

1. Re-use existing data

Using more sophisticated technology, it is becoming easier to extract meaningful insights from bank data, even if it resides in a legacy core. 

For example, banks can use transaction data from existing depositors to better segment and market to current customers. One European bank used machine learning algorithms to predict which customers were likely to churn. Based on that segmentation, the bank could create a targeted marketing campaign that reduced churn by 15%. 

Similarly, banks can use data to segment customers into groups that are likely to benefit from a particular product and even prequalify that customer for that product or service.

2. Tailor experiences to customer needs

Customers crave personalized advice and experiences, yet only 25% said their bank performs ‘extremely well’ at being aware of changes to their financial and personal situations. By better-utilizing data as discussed above, banks can provide highly personalized offers based on a customer’s current financial situation.

For example, imagine a scenario where a bank can utilize transaction data from existing small business owner customer accounts to develop a lending profile and pre-qualify that business owner for a loan. The bank then sends a personalized email, text message, or push notification from the mobile app to that customer, telling them they’ve been prequalified for a loan up to a certain amount.

Now, not only does the bank have a pool of qualified prospects to market to, but the bank’s current customer base is now getting highly personalized, relevant offers that can help them achieve their goals along with a personalized borrower experience via pre-filled applications from the bank’s customer data.

3. Develop integrated experiences

Despite many opportunities for cross-selling, only 23% of respondents rate their bank highly for its range of products and services and the competency of its tailored financial advice. 

To start, banks can create integrated experiences across lending, credit card, and bank account products for loan applications, loan management, deposit information, and mobile experiences. This involves reducing silos within the institution itself, but can also be expanded further by using aggregators like Plaid or Finicity to connect accounts across multiple institutions.

By better utilizing and integrating data, banks can create a holistic offering for their customers. This moves the bank away from a strictly transactional relationship to the role of an advisor or partner—especially for the small business demographic. 

Thinking holistically also creates new opportunities for the bank to create and demonstrate value to its customers. A business owner’s personal and business bank accounts may not be large enough to warrant a special offer or rate, but what about the personal and business bank accounts combined with a loan? When the offer goes out to the business owner, the application can be prefilled with data from the bank’s core dataset.

Now, instead of shopping around for the best rate every time a customer needs a new financial product, the customer has a reason to stick with their primary banking institution.

4. Adopt proactive outreach based on tailored needs

Seventy-six percent of small business owners are interested in receiving financial advice, yet only 15% say they receive comprehensive advice from their financial institution.

According to the same survey data, top areas in which banks can provide guidance to business owners’ include:

• Advice on lending products
• Ways to improve cash/deposit management 
• Practical advice on ways to reduce banking fees 
• Tips to help improve the business’ financial situation
• Information on how the bank’s technology can benefit the business

Once again, better access to and analysis of data can help financial institutions meet these needs. By combining bank data with other partner data, banks could provide pre-qualified loan offers, pre-filled tax documents, or even notifications on inventory supply.

Banks have also started to adopt artificial intelligence to improve direct customer interactions including using chatbots for basic service requests. For the SMB customer, these could be applied to provide business advice from an AI-powered advisor.

5. Focus on speed and timely communication

Consumers have a growing expectation for quick applications, onboarding, and approval flows across payments, pending transactions, loan applications, and any other “in-flight” activities. 

Amazon's embedded financing shows how AI can speed up processing and approval times by using existing customer data to almost instantaneously approve Amazon’s customer for a business credit card, payment plan or loan while using existing or third-party data to pre-fill or skip application forms altogether.

Similarly, banks can adopt AI and data automation to significantly boost speed-to-offer. In lending, for example, a bank could run automated checks against internal and third-party data based on the bank’s pre-determined risk profile to create a near-instant loan offer for a borrower.

Additionally, banks can offer their services through embedded financing to expand their customer pool, reach customers where they are, and access additional third-party data that will better personalize communication with the customer. 

The future is now

Rather than a hypothetical look at the future, the ideas and capabilities discussed in this article are ready to be implemented now. By evaluating and adopting SaaS technology that is API-ready with responsive parameters, banks can avoid an extensive development process and start providing a better customer experience today.

Learn more about how automation in lending can boost the customer experience.

Traditionally, the loan underwriting process has been a slow, cumbersome affair. But now, lenders are increasingly adopting automated underwriting systems to streamline the process and expedite loan decisions.

Traditionally, the loan underwriting process has been slow and clunky with multiple data sources and lots of paperwork to sort through. Lately, more and more lenders have adopted automated underwriting systems, including Freddie Mac, which announced automated underwriting capabilities for mortgage lenders in 2022. While mortgage lenders still haven’t adopted a fully digital end-to-end solution, they have benefited from decreased costs and faster processing times.

Automated underwriting can be equally beneficial for SMB lenders.

How automated underwriting works

At the most basic level, underwriting automation entails using technology to collect and analyze information about a borrower to make a loan decision. 

An automated underwriting system will verify firmographic and demographic information, along with third-party data, including identity verification, tax documents, know-your-business compliance checks, credit bureau checks, and account verification.

Once the data has been collected and analyzed, the system can finalize approval of the loan within 15 seconds.

Financial institutions may choose to implement automated underwriting in a number of ways including:

• Using automation to source data, but still allow a human to review and give final approval.
• Using automation to source data and recommend a decision. 
• Relying solely on automation to make a decision.

Benefits of automated underwriting

The benefits of automation in banking are vast, including:

• Faster processing times - Approving a loan in seconds, rather than days, provides a better experience for the borrower and frees up employee time to focus on activities that can have the biggest impact on the financial institution.
• Better outcome predictions - Multiple studies across auto, mortgage, and commercial lending have found that machines can more effectively analyze multiple datasets to predict outcomes than humans can, lowering the risk of default and decreasing the cost of bad debt.
• Less bias - Automation has been credited with reducing human bias in the loan approval process, but modern underwriting systems provide additional benefits to traditionally underserved communities by pulling in data sources that better reflect the health of the business, rather than relying on the business owner’s personal credit score. 
• Expanded addressable market - Due to time and cost constraints, banks typically focus on servicing larger loans. By automating processes, banks can reduce the time and money spent prequalifying and underwriting a loan, making it possible to service the SMB market—which makes up a large part of the bank’s depositors—and access millions of new revenue opportunities.
• Wider distribution - Embedded finance and banking-as-a-service channels are creating new frontiers in lending. Banking-as-a-service is projected to reach a market size of $11.34 billion by 2030. However, along with those channels comes new requirements for the re-use of existing borrower data, faster time to offer, and faster time to fund.  Automation is becoming a necessity, particularly in these times. 

Documenting policies and sticking to them 

Banks pride themselves on having well-documented policies. In fact, many banks have taken important strides to sharply reduce—and even eliminate—the elements of subjectivity in decision-making. However, in practice, lenders often have exceptions. But what if, rather than designing a process to accommodate the exceptions, we designed a process to optimize for the vast majority of applications that can be accurately measured and adhere to a written policy? 

Machine vs. human decisions

Of course, like the adoption of any new technology, automated underwriting has produced a debate around when/if a machine truly is a better option than a human. A human, for example, can better assess nuance than a machine. Does this then give the human the advantage in better predicting outcomes? In actuality, studies have found that machines are better able to assess risk for borrowers with lower credit scores or a prior history of bankruptcy.

Users adopting the technology also need to trust that the decision engine is accurately assessing risk profiles. To help alleviate those concerns, underwriting systems can be adjusted to match the bank’s risk profile.

Going beyond underwriting

Automation in banking has a much bigger role beyond underwriting. It can be used to pre-qualify borrowers and market to them, creating a much more attractive offer for the borrower and better ROI for the lender’s marketing and loan officer teams. 

In this scenario, the loan origination system pulls in transaction data from the bank’s system and third-party sources, classifies those transactions, tabulates results to develop a profile, and then runs that profile against a predetermined transaction policy. Based on the results, the bank can then send a pre-qualification offer to the customer.

Bottom line

There is a huge opportunity for the lending industry to become more efficient, reduce risk, and capitalize on the underserved small business market via automated underwriting. Banks that adopt this technology early will have a key competitive advantage over other institutions as automation in banking becomes more mainstream.

Are you interested in lending automation? Learn more in this ebook, Automated Lending: A Mandatory Upgrade.

As the demand for SMB lending grows, banks can use automation to capitalize on an underserved market.

As online banking has grown, banks have met the expectations of digitally savvy customers who prefer to open their accounts online by automating much of the account-opening and customer-onboarding process. 

While banks have been rushing to keep up with the digital revolution, another chasm has been building in the background for the past 30 years. 

In 1994, there were 10,329 community banks. By 2014, that number shrunk to 6,094. From 1994 to 2018, community banks' share of banking assets and lending markets fell by 40%. In 2020, community banks accounted for just 15% of the banking industry’s total loans. 

Causes for the decline include the savings and loan crisis of the 1980s and the 2008 financial crisis, as well as increasing regulatory and compliance costs and the removal of barriers to bank consolidation.

Why does this matter? Community banks are typically the largest source of funding for small rural communities, and they hold 36% of small business loans. Since community banks are able to form personal relationships with business owners, they’re able to make risk assessments based on “soft factors,” rather than just the hard metrics that larger institutions rely on. This makes them better able to finance SMBs.

As the footprint of community banks shrinks, SMBs have had to turn to larger banks, only to find them less willing to provide loans to them as the banks have become more cost-conscious. The cost of underwriting a loan is the same, regardless of loan size, making it uneconomical to fund smaller loan amounts. 

At the same time, the recent COVID-19 pandemic accelerated digital adoption, making business owners more prone to apply for a loan via phone, rather than walk into a physical branch. 

These three factors have combined to create a huge unmet need for small business lending that is waiting to be filled. The key for banks to capitalize on this opportunity is automation.

Automation use cases in lending

Current processes for pre-qualifying and underwriting loans are time-consuming and manual, leaving banks with limited staffing resources to expend precious attention on small business loans, instead of focusing on larger loans that would yield higher individual returns. 

Additionally, credit scores are less effective in predicting borrower performance for small businesses because small businesses often have a limited credit history, requiring decisioning on the owner. However, this tells a lender little about the business itself.

Transaction analysis is a much better leading indicator of business health. Using data science, banks can utilize transaction data and cash flows to measure the health of the business and prequalify borrowers before a human even gets involved.

Transaction analysis

In the case of assessing a bank’s existing depositors, data is typically sourced directly from the bank’s core system. However, the same approach can be used for customers of third-party banks. In such cases, the borrower may submit bank statements or log in through an aggregator like Finicity, Plaid, or Yodlee. Optical character recognition (ORC) technology is used to ingest statements and turn them into machine-readable text that can be assessed similarly to aggregated or bank-sourced data. 

Once the data is ingested, the system will analyze the data through the following steps:

1. Classify transactions (revenue-based deposit, non-revenue deposit, NSF, third-party debt service, or suspicious transactions). 
2. Tabulate results to generate a monthly, quarterly, or annualized profile. 
3. Run that profile against a transaction policy (maximum NSFs, minimum number of deposits). 
4. Establish the pricing/offer amount based on annual gross revenue, credit score, and other factors.
5. Generate prequalification offers. 

Prequalification

Regional and community lenders are often challenged with what, how, and when to market lending products to their customers and members. Many report an email or direct mail response rate of roughly 1%. Even once the potential borrower takes action, the approval rates are meager—typically below 40% but often substantially lower, based on Lendio’s experience with our broad base of lenders.  

Transaction data can be used to generate a pre-qualified offer that the lender can stand behind.  Imagine the level of engagement for a business owner as they receive a message that says, “Come apply for a loan,” compared to, “John Doe, LLC, you’ve been pre-qualified for a loan of up to $75k.” The use of transaction data, accompanied by personalization of the marketing message, significantly improves the effectiveness of marketing investment and lender teams.  

Underwriting

Once a customer has accepted the pre-qualified offer, he/she is prompted to verify some demographic and firmographic information. In parallel, to collect that information, the system will verify the information and pull additional third-party data including:

• Credit bureau checks 
• KYB compliance checks
• Identity verification
• Tax documents 
• Account verification 

Once the borrower has been evaluated, the system can finalize approval within 15 seconds of receiving the application. At the same time, lenders can choose the level of automation they would like to utilize. That may range from automation of application ingestion and data pulls with a human review to a fully-automated approval or decline.  

Benefits of banking automation

The automation of banking systems has benefits across the board including: 

Customer experience

Customers want a streamlined, online experience that can get them an answer fast. With automated checks from multiple data sources, banks can provide instant pre-qualification based on robust criteria. 

Regulatory compliance

By building and using automation systems built to match the bank’s risk and compliance policies, banks can have more confidence in their decision, instead of resorting solely to shoring up loan offers with higher rates.

Cost savings

The lending process immediately becomes less time-consuming and expensive when all of the data that a human used to sort through is automatically tabulated by a computer. This frees up staff time to focus on areas that require human judgment.

Predict better outcomes

The Bank for International Settlements found that alternative data could predict future loan performance better than traditional methods, especially in areas with high unemployment.

Automation challenges

Of course, automation comes with its own set of challenges. 

Outdated core banking systems

Many banks use outdated core systems that are notoriously difficult to work with. Any automation technology will need to be built with the flexibility to work with a less friendly system. 

Managing the technology

Banks don’t necessarily have the resources to manage and apply the technology independently. Look for a SaaS platform maintained by a third party that’s API-ready and can be configured to varying product parameters and pricing models.

Fraud concerns

Combating fraud is a crucial component of adopting automation. Putting safeguards in place through KYB and IDV verification is critical to balance risk with the benefits of automation. Additionally, by targeting existing depositors for prequalification, banks can work with a pool of already-vetted accounts.

Customized loan products

Certain loan products, such as commercial real estate, require much more customization to the borrower, making them harder to automate. Typically, the same thought processes have constrained automation in SMB lending. However, with new technologies, borrower risk can be more easily assessed thus enabling far greater levels of automation. 

An untapped opportunity and improved accessibility

Banks that effectively adopt automation to overcome small business lending challenges will be able to access an untapped market. But the impact goes far beyond a substantial revenue and cost savings opportunity. 

Underserved communities, such as those with higher immigration and crime rates, are frequently overlooked because of the inherent biases in credit scores. By relying on alternative data sources such as transaction data, banks can get a better understanding of the business’ health and create fairer outcomes for those communities. 

Interested in lending automation? Learn more in this ebook, Automated Lending: A Mandatory Upgrade.