Credit & Lending Decisions
As we ponder the impact of a recession with a magnitude second only to the Great Depression, most lenders have to ask themselves: Are my lending decisions really based on the latest and greatest insights into a borrower’s behaviors? Or are they simply using someone else’s “guesstimation” of their ability to pay?
Even in the best of economic times, a credit score and the latest income paystub can only give you an idea of the borrower’s potential ability to pay—and then only at the time said data was recorded. These data alone cannot give you a true picture of a borrower’s propensity to pay or walk away—especially during a sudden economic downturn. When people are suddenly jobless or—in the case of the recent pandemic—they are compelled not to work, their latest income data and credit score likely aren’t true indicators.
While credit scores, income data, and a lender’s first-party data on its customers are valuable input into lending decisions and risk management, analysing data from external sources can reveal insights into a borrower’s level of responsibility and intent in ways traditional data cannot. Armed with a more holistic view, lenders can better determine the level of risk associated with a loan applicant, even should they be suddenly and temporarily hit by the effects of a recession.
Today we’ll examine how leveraging bank transaction data to uncover a borrower’s spending, payment, and income patterns can lead to making smarter, more informed lending decisions.
Using bank transaction data beyond its traditional applications
Bank transaction (BT) data has been used by underwriters to varying extent for years, but its applications have been limited. For example, it is most often used to confirm an applicant’s identity by matching their name with the underlying bank account. BT data is also used as part of manual reviews of applications and creditworthiness, with the operative word being “manual”—decision processes that not only aren’t scalable but can be subjective at best. Some lenders have gone so far as to leverage BT data to reveal spend types, debt ratios, transaction amounts, and overall cash flow trends, which can be used to make better decisions though still nonetheless manual.
However, the true power of bank transaction data lies in using the behaviors it reveals about borrowers to make smart, automated lending decisions in real-time—without relying on manual, offline reviews. Only then can lenders improve their efficiency, scalability, and likelihood of repayment, while reducing their risk of delinquency, losses, and fraud. In short, to use this data build a more recession-proof portfolio.
Of course, this first requires that a lender have access to bank transaction data from major providers in a consistent set of data and metadata. It is then necessary to assess various financial risks, from first-party fraud to credit risk, by creating and training advanced machine learning models. These lending decision models can be tuned to allow a decisioning platform to provide insights into more than just income, expenses, identity, and balances, but also assess loan affordability, incremental risk, and other metrics that have been traditionally difficult to measure. By integrating bank transaction data with credit bureau data and scores, as well as its own internal data about customer behaviors, a lender can build its own custom scores to make the best lending decisions possible.
Affordability as a better metric than disposable income
Why is it so important to go beyond traditional credit scores and take other situational and behavioral data into account?
Consider first that during a booming economy, credit scores can be artificially—though unintentionally—inflated, overestimating a borrower’s actual ability to pay on time, simply because of the positive economic environment. This tends to obscure actual risk so that when a recession does hit, lenders are left holding a bigger bag (so to speak) than expected. That’s why all lenders, can benefit from assessing aspects of a customers’ financial status other than credit scores. Having access to a prospective borrower’s current bank transaction data is the gateway to far greater insight into an applicant’s actual ability to pay.
Here are just a few examples of insights from BT data that lead to smarter lending decisions:
· Disposable income data only gives an aggregate picture. Bank transaction data can reveal whether the borrower is currently or most often short on cash.
· Many new borrowers haven’t sufficient credit history or scores to make an informed underwriting decision. Bank transaction data can reveal those that can afford the loan, allowing lenders to make low-risk decisions that a mere credit score would not.
· By assessing bank transaction data, a lender can determine the optimal payment dates for new loans, based on the customer’s typical monthly cash flow, increasing the likelihood of on-time payments.
How can you leverage bank transaction data for better decisions?
As we mentioned before, to leverage the power of BT data, your lending decision and analytical models must first have access to it. That’s why it’s important to carefully choose a risk management, decisioning, and analytics partner. At GDS Link, in addition to more than 100 data source integrations, we have developed our own credit bureau and bank transaction data suite of premium attributes, normalised across all major credit bureaus—model ready, governance reviewed, and maintained with transparency into the calculation.
In addition, GDS Link is building a standalone bank transaction data solution that can work in tandem with your current models and is simple to integrate with your current data sources. Stand by for more information on this new solution in this space. Meanwhile, you can get more tips on how to recession-proof your lending portfolios by downloading the latest GDS Link whitepaper, Why You Should Always Manage Your Lending Portfolio as Though in an Economic Recession.