Brent Chandler: Timely Employment Verification Pivotal to Mortgage, Other Consumer Lending Risk Management
Brent Chandler is founder and CEO of FormFree, Atlanta.
Mortgage bankers and consumer lenders of every stripe have their plates full right now, so some may have missed a significant development or not had the bandwidth to consider its implications. One of America’s primary investors in loans to consumers has lost confidence in automated systems used for verifying loan applicants’ current employment status, accelerating an already-in-motion shift in how borrowers’ ability to repay needs to be assessed.
On May 5, Fannie Mae suspended automated validation of mortgage applicants’ employment status, effectively withdrawing reps and warrants relief from verification of employment (VOE) bureaus and the lenders who rely on them. A small change, yes, and only “temporarily” in force, it nonetheless signals the fading usefulness of aging, intermediated data in lenders’ risk decisioning. Today’s economic upheaval is testing systems elasticity and has forced a spotlight on how rapidly changing economic circumstances add risk to financial transactions.
From a lending risk management perspective, it has always been prudent to verify borrower employment as close to real time as possible. Payroll providers were once considered a timely source of employment data, but the two-week payroll cycle can no longer reliably deliver investor-accepted VOE.
Lending risk management experts realize that in the current environment, payroll data is dangerously outdated, and understandably it makes them nervous. Securing a manual VOE by voice contact with the employer is an alternative, but will add delays and opportunities for fraud, and the ripple effect will further decelerate our economy. Especially now, when so much rides on completing a timely transaction, we need to address what can be done for loan applicants to ensure that their lenders can confidently affirm their stable employment. The good news is that borrowers themselves hold the key in the unique financial activity data aggregated in their bank accounts and reported in their bank statements.
We know this is true as an authorized asset verification report provider for Fannie Mae’s Desktop Underwriter validation service. Since 2008, we have been working with consumers’ asset account providers and mortgage lenders to enable fully permissioned, protected access to consumer asset account data. Our original mission has been to derive consumer ATR using raw bank data directly from the source and replace the paper-based bank statements. In this way, the borrower’s data — the actual bank data itself, not a model derived from larger groups of data — becomes their ATR.
Data aggregation of source bank data is widely utilized; however, in order to meaningfully evaluate a borrower’s ATR and meet investor guidelines, lenders need a standard report that is easily evaluated by underwriters. Focusing on data analysis such as AI, machine learning and NLP plus the use of corroborating sources drives a high confidence factor in the data, which translates to a high confidence in the borrower’s ATR, all with a real-time view into the borrower’s financial DNA.
This work is paving the last mile of automation for credit underwriting decisions. When consumers’ data are made available by explicit agreement from the account holder, even more ATR confirmations can be extrapolated.
For instance, most American workers receive their paychecks in their checking account (DDA), and most pay their obligations through the same account. That is how and why the same technology that allows a reliable understanding of consumers’ assets also allows for the validation of their income and their employment status as recent as their most recent paycheck.
Once consumers permit their financial institution data to be shared with their lender, the data becomes the proof point, and third-party intermediation to perform validation becomes unnecessary. When lenders access assets, income and employment in a single pull, they’ll be able, with that bank statement data, corroborated with other source data, to achieve a very deep understanding of the borrower’s true ATR. The experience is seamless and cost effective.
The average consumer who seeks financing for a home, an automobile or even a new home entertainment system owns a bank account through which they conduct the majority of their financial transactions. For mortgage lending and, by logical extension, all manner of consumer lending, verifying employment, assets and income in one fell swoop adds greater confidence in ATR than legacy indicators of creditworthiness.
Let’s grow with this understanding toward a consumer lending risk management model that allows direct-source asset, income and employment data a primary role in determining consumer creditworthiness.
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