The Next Step in FICO Credit Modeling

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Since its introduction by Fair, Isaac & Co. more than three decades ago, FICO has long been considered the No. 1 standalone credit decisioning model. Is there a way the banking industry can build on that foundation to create a more future-forward way of predicting lending outcomes?

The way the financial services industry analyzes data has evolved since FICO’s inception in 1989. New and exciting technology has led to innovative algorithms that give bankers a more defined look at an even greater data set. An all-encompassing view of a borrower’s story can bring a new realization: these new methods of analyzing credit, combined with the FICO mainstay, can lead to even better outcomes for everyone.  

Many facets make up an individual’s credit story — beyond payment history and amounts owed. There is data that, once analyzed, can give lenders critical insights into borrower characteristics that can’t be categorized by a single number. People are more dynamic than their credit scores.  

Imagine a traditional consumer credit scoring model as a printed picture: a one-dimensional take on a person’s whole life in credit. In that static picture, there are balances on debt obligations, utilization of revolving types of credit like credit cards, delinquency and statuses, among others. This information comes from the three major credit bureaus — Equifax, TransUnion and Experian — and represents a vast cross-section of loans originated by banks, credit unions, finance companies and other lenders across the credit industry. This information adds up to that single definitive score.

In contrast, non-traditional models that build on the foundation of FICO can incorporate additional predictive information. Think of it as the motion picture version that creates a more dynamic view of consumer creditworthiness. This model gives lenders an ability to assess point-in-time information and the momentum of trended credit data factors, which may help predict the future credit conditions for a potential borrower and allow a lender to make more informed decisions. Bankers have greater visibility into the depth of a borrower’s story, like balances or utilization increasing or decreasing, and can capture that relationship with risk outcomes.

Alternative data sources can complement static and trended credit history by introducing consumers’ checking history, property ownership and alternative finance activity into credit scoring models. Consumers with comparable credit files can have vastly different repayment history and patterns; incremental information related to creditworthiness equips lenders to optimize risk differentiation when the credit file alone doesn’t capture the full story.

Creating a new model to calculate and predict high-performing loans is no small feat. BHG Financial, a leader in unsecured business and personal loans and creator of one of the country’s largest community bank loan networks, once relied on the traditional credit scoring model to help with their decision-making. The company decided to evolve their credit model to identify miscategorized but high-quality borrowers that most lenders were missing.

BHG Financial data scientists partnered with TransUnion to analyze over 2 million consumer loans; each loan was over $20,000, had at least 36-month terms and originated between 2015 and 2017. This amounted to more than a billion pieces of data points to analyze and assess, resulting in their proprietary credit model, the rScore.  An updated credit model resulted in faster approvals, and the identification of subprime borrowers that perform well along with prime borrowers with high default rates.

Evolving the already successful and established FICO score, the chances are lower that good-paying borrowers will be labeled as high risk. This enables some lenders to approve pockets of creditworthy consumers that others might decline. At the same time, the chance of labeling risky borrowers as low risk also declines — allowing lenders to protect the credit quality of their portfolio.

Lenders unable to dedicate time and money to develop their own evolved credit scoring model can collaborate with companies that have created updated credit models, skipping the extensive research and the costly origination process. This gives them immediate access to purchasing top-quality loans with low risk, which can quickly strengthen their loan portfolio to meet their bank’s criteria. This solution is possibly the best answer to finding a more future-forward way of predicting lending outcomes.    

What’s Changed In Business Lending

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In today’s fast-moving world, business leaders expect quick decisions, and forward-thinking banks are speeding up the loan process to serve clients in less than three minutes. So what’s changed — and what hasn’t changed — in commercial lending? In this video, Bill Phelan of PayNet explains that relationships still drive business banking and shares how the development of those relationships has changed. He also provides an update on Main Street credit trends.

  • How Banks Are Enhancing Credit Processes
  • New Ways to Build Relationships
  • Small Business Credit Trends

Three Ways Directors Can Solve the 3,000-Year-Old Credit Problem

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credit-7-9-19.pngHistory has shown that knowledge is power. One place that could use the benefit of that knowledge is commercial credit.

Banks have been lending to businesses for 3,000 years and has yet to figure out the commercial credit process. But executives and directors have an opportunity to fix this problem using data and digital capabilities to make the process more efficient and faster, and become the lending legends of their institutions.

In 1300 B.C. Egypt, the credit process looked something like this: A seafaring trader would trade bronze bowls with a local bronze merchant for cloth and garments. But to make this transaction, the bronze merchant would need to borrow from multiple merchant lenders. This process required lenders to understand the business plans of the borrower, go “door to door,” have community knowledge and know the value of all those goods. There were a lot of moving pieces—and a great deal of time—involved for that one transaction.

Fast-forward to today. A lot has changed in 3,000 years, but the commercial credit process has actually gone backwards. It can take a lender 60 to 90 days and more than $10,000 per lead to identify potential leads—and that’s before they review the application. After a borrower applies, the lender must look up credit reports, collect and spread financial statements and decide on the terms and conditions. Finally, the application goes through the credit department, which can take another 30 to 45 days and cost $5,000 per application.

Lenders will have spent all that time and effort to process the loan—but may not end up with a new customer to show for it. Meanwhile, borrowers will have spent time and effort to apply and wait—and may not have a loan to show for it.

While this problem has persisted for 3,000 years, the good news is that executives and directors have an opportunity to fix the problem by turning their manual-lending process into a digital-lending one. This evolution entails three steps that transform the current process from weeks of work into days.

First, a bank would use a digital-lending portal to gather applicable demographics to identify prospective borrowers. In researching prospects, they see critical borrower information such as name, address, years in business, legal structure, taxpayer identification number, history, business description and management team. Rather than having to wait until later in the process to uncover this critical information, they can immediately identify whether to pursue this lead and quickly move on.

Second, a bank uses a credit-decision engine to gather and analyze the applicable borrower data. Not only can the engine pull in consumer and credit bureau information, but it can also include automated financial collection, credit score and industry data for comparison. The bank can use data from this tool to determine terms and conditions, credit structure, purpose of credit facility, pricing, relationship models and cross-sell strategies.

Third and finally, the bank’s credit policy and process integrate with its credit-decision engine to enable an automated review of a loan application. This would include compliance checks, terms and conditions and credit structure. Since the data gathering and analysis has already taken place and automatically factored into the decision, there is no need to review all those pieces, as would be required with a manual process.

These three steps of this digital lending process have distilled a weeks-long process into about five days. Executives and directors can not only grow their institution in a shortened time period; they can do so without adding any risk. A bank I worked with that had $250 million in assets was able to add $20 million in loan volume without taking on any additional risk.

By using knowledge to their advantage and implementing a digital lending solution, bankers can save not just time and costs, but their institutions as well as their communities. They can now spend their limited time and resources where they matter most: growing relationships along with their banks. Having fixed the 3,000-year-old credit problem, they can place those challenges firmly in the past and focus on their future.

The Huge Lending Opportunity You’re Overlooking

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entrepreneur-4-12-19.pngSince opening her Brooklyn-based gym, HIIT Box, four years ago, Maryam Zadeh has been featured for her fitness expertise in publications like Marie Claire magazine and Self.com. This exposure has caused business to explode.

The number of clients and revenue have tripled, she says. HIIT Box has relocated three times in four years to pursue more space. And there’s still a waitlist to join.

But despite this success, Zadeh has struggled to obtain the capital she needs to keep up with the rapid growth of her business. She initially invested her own money—a $13,000 inheritance—and later obtained $35,000 from American Express (her payment processor, through its working capital program) and two smaller loans totaling $27,000 from the online lending platform Biz2Credit.

But it wasn’t enough, and other lenders turned her down when she sought additional capital to move into a bigger space. So, she turned to customers to fill the funding gap, offering her 40 largest clients a discount if they paid a lump sum up front. Twenty-three clients took advantage of her offer. “That’s what gave us that big chunk of money [for] construction, because no lender would give it to us,” says Zadeh.

Growing pains like these are common among female entrepreneurs.

Women own more than 11 million businesses in the U.S., or 39 percent of businesses, according to a 2017 study commissioned by American Express—a number that has risen over the past 2 decades. A Bank of America survey published last year found that 56 percent of female entrepreneurs plan to grow their business over the next five years. To do so, however, many of them will need to raise capital.

“Women-owned firms face persistent funding gaps and funding source mismatches,” according to a study published in 2016 by the Federal Reserve Banks of New York and Kansas City. Twenty-eight percent of women-owned firms applying for a loan over the previous year were not approved for any funds, and 64 percent obtained less money than they needed.

Some banks have developed educational programs to better engage this potentially lucrative demographic.

Renasant Bank, based in Tupelo, Mississippi, launched its “Nest” program in March, which provides financial education to female entrepreneurs. It’s part of a larger bank-wide program focused on developing female leaders, both in the community and within the bank.

Tracey Morant Adams, the chief community development and corporate social responsibility officer at the $13 billion asset bank, saw that female entrepreneurs often weren’t as comfortable discussing the financial position of their business. They also didn’t understand the financing options available to them and were more likely to rely on personal wealth—dipping into their retirement savings, for example—to fund their small business.

Renasant will use a lunch-and-learn format to explain financial basics—how to read pro forma financial statements, for example—so women can gain the confidence and knowledge they need to understand their financial position. Renasant will also explain the funding solutions available, and how to understand which one is the best fit for their business—when a line of credit is more appropriate than a credit card, for instance.

Ultimately, at least in theory, some of these women will seek a loan or deepen their relationship with the bank. “You have to be intentional and deliberate in your efforts to reach out and find that business,” says Adams. “The Nest is going to allow us to be more intentional, particularly in that female space.”

Bank of America’s study asked female entrepreneurs to identify solutions to address the funding gap women face. Twenty-four percent pointed to education—echoing the importance of programs like Renasant’s.

But even more women—42 percent—pointed to the need for gender-blind financing to reduce the role that unconscious bias—and outright sexism—play in the loan application process.

When she’s applied for capital, Zadeh—the CEO and sole founder of her company—has been asked where her (male) partner is. Some have assumed she was running a yoga studio, not a gym. She’s even been asked if she can do push-ups. (She can.)

Women—and small business owners in general—are more likely to be approved for a business loan by a small bank than any other option, according to the Fed. Twitter_Logo_Blue.png But despite higher approval rates at small banks, women are more likely to seek funding from a large bank or online lender.

business-loans-chart.png 

Stories like Zadeh’s may explain what’s driving women to online lenders and larger banks. “Everything is driven by the data, and there is no possibility of any kind of gender bias,” says Rob Rosenblatt, the head of lending for the online lending platform Kabbage.

Applying online, in theory, reduces bias, so a female applicant could be more optimistic that her loan would be approved.

For women seeking to grow their businesses, access to capital can make a big difference—and expand lending opportunities for the banks that enhance their efforts to this group.

Navigating Your Bank Through Rising Rates

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interest-rates-4-2-18.pngBanks have been lamenting low interest rates for almost a decade. In boardrooms and on earnings calls, low rates have been blamed for shrinking margins, tepid deposit growth and intense loan competition.

With rates now up more than 100 basis points from their lows, we’re about to find out where that was true, and where interest rates were just a convenient scapegoat. Management teams and boards now face a few strategic questions. Among them: How is lending typically impacted by higher rates, and what strategies should my institution consider as rates continue to rise?

First, as the Federal Reserve’s Federal Open Market Committee puts upward pressure on overnight rates, there is typically a follow-on effect further out on the curve. But, these effects are rarely 1:1, resulting in a flattening yield curve. Bear flatteners, in which short-term interest rates increase more quickly than long-term rates, differ in severity, but if this one is anything like the period following the last Fed tightening cycle—from June 2004 through August 2006, as shown in the chart below—banks could be in for some pain.

Second, as rates start to quickly rise, nominal loan yields lag, resulting in declining credit spreads. It takes time for borrowers to adjust to the new reality, and competing banks can be expected to play a game of chicken, waiting to see which will blink first on higher loan rates and face a potential loss of market share.

Taken together, these two phenomena can put intense pressure on loan profitability. Banks are now enjoying an increase in net interest margins, but this comes on the back of rising yields on floating rate loans funded with deposits that have not yet become more expensive. Deposit costs will soon start moving, and once they do, they can move quickly.

This is a time when a rising tide no longer lifts all boats, and the banks that properly navigate the asset side of their balance sheet will start to separate themselves from everyone else. So, what does “proper navigation” look like? It’s not timing the market or outguessing the competition. Instead, winning during pivots in interest rates is all about adhering to a disciplined pricing process.

Trust the Yield Curve
The top performing banks let the yield curve guide pricing. We see evidence of this discipline in the mix between floating and fixed-rate structures. When rates were low, and the yield curve was steep, many banks were tempted to move out on the curve. They instituted arbitrary minimum starting rates on floating structures and saw their share of fixed-rate loans reach record highs. Now that rates are starting rise, these banks fear the exposure those fixed rates created, so they are desperately trying to correct the mix.

Disciplined banks ended up with the opposite scenario. With a steep curve, they found the lower floating-rate structures to be popular with borrowers. Now that the curve is flattening, borrowers are choosing more fixed-rate structures. These banks have large blocks of floating-rate loans that are now repricing higher, and that mix will naturally shift to fixed as rates move higher and the curve flattens, protecting them from dropping yields when the cycle eventually turns again. These banks let the yield curve help them manage their exposure, working in sync with borrower demand instead of against it.

Supercharge Cross-Sell Efforts
We also see top performing banks paying more attention than ever to their cross-selling efforts. In a rising rate environment, low cost deposits become much more valuable. Banks that already have deposit gathering built into their lending function are taking advantage, as their relationship managers can offer more aggressive loan pricing when the deals are accompanied by net new deposits. These banks have well-established processes for measuring the value of these deposits, tracking the delivery of promised new business and properly incentivizing their relationship managers to chase the right kind of new accounts.

When it comes to surviving—and thriving—in a rising rate environment, there is no magic bullet or secret shortcut. Instead, the answer lies in continuing to do what you should have been doing all along: Trusting the process you’ve built, staying disciplined and ignoring all the noise around you in the market.

And if you don’t have a process—a true north that you can use to guide your commercial bank’s pricing strategy—then get one right now. Rough seas may well lie ahead.

How PrecisionLender Helped Woodforest Bank Expand into Commercial Lending

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When Woodforest National Bank made the strategic decision to grow its commercial loan portfolio in 2015, it wanted to leverage the latest technology to take full advantage of that opportunity. Woodforest is a privately held bank based in Woodlands, Texas, with over 740 branches across 17 different states, and also in-store branches with WalMart and Kroger. At the time, Woodforest management realized that the bank’s lending portfolio was heavily weighted towards commercial real estate. The commercial lending operation was based out of—and primarily managed by—its Houston office. Rates and pricing were based strictly on what the market would bear, with no system in place to tailor rates to different markets, industries or clients. Woodforest then sought out a technology partner to help implement a more intelligent pricing model and methodology for the commercial side.

After establishing five different commercial lending business lines, Woodforest partnered with Charlotte-based PrecisionLender to help relationship managers (RMs) win better deals that aligned with the bank’s strategy, in terms of profit, risk and growth. The cornerstone of the PrecisionLender platform is “Andi,” an AI-powered virtual assistant. Andi works with relationship managers as they price each opportunity, showing them multiple ways to structure deals that will reach their targets, while also highlighting ways to expand the relationship.

Part of what made PrecisionLender an ideal partner was the ease and speed of which Woodforest could implement the platform across relationship managers in multiple states. The firm worked closely alongside the Woodforest business and IT teams during the implementation, and the platform was launched in March 2016. The commercial banking team now uses the system to input pricing for all opportunities that require approval. To help ensure the RMs price deals that work for both the borrower and the bank, Andi considers a multitude of factors, such as fixed versus adjustable rates, fee structures, duration and deposits the applicant already has with the bank. This flexible, data-powered approach empowered Woodforest’s RMs to better tailor deals by client, industry and region, helping the bank rebalance its portfolio and put a greater emphasis on middle market banking.

Woodforest and PrecisionLender conduct a quarterly return on equity (ROE) meeting to discuss performance trends of products, branches and even individual relationship managers. PrecisionLender also reviews ROE targets set for each region during these sessions. And at last year’s third quarter ROE analysis meeting, the firm surfaced several key issues from the system that Woodforest could not have found without this rich data set.

PrecisionLender continues to seek feedback from Woodforest to optimize and improve on its use of Andiand the overall platform. Currently, the firm is working to create a “Promise versus Delivery” dashboard, which will give management a snapshot of lending opportunities in progress in comparison to what’s been forecasted for each region, branch and relationship manager. This will create real-time visibility into each potential deal, and ensure that relationship managers are providing accurate forecasts.

There’s also a “performance scorecard” in the works to evaluate each relationship manager as if they were running their own mini business, taking into consideration not just new loan generation but also income generation and risk management.

“PrecisionLender helped us grow from a Houston commercial banking organization into a national presence with five new locations from coast to coast while generating the return needed for the expansion, while also providing flexibility to our relationship managers and great relationship pricing for our clients,” says Derrick Ragland, president of commercial lending at WoodForest.

This is one of 10 case studies that focus on examples of successful innovation between banks and financial technology companies working in partnership. The participants featured in this article were finalists at the 2017 Best of FinXTech Awards.