How Advanced Data Analytics Can Make Credit Decisions More Inclusive

If you’re a marathon runner, you’re constantly exploring ways to conserve your resources, increase your speed and maximize your results ahead of the next race. The same principles can be applied to the banking industry. Lenders are always looking for better, faster, smarter ways to do things. And that’s as it should be.

Data has always been at the core of making sound lending decisions. But, as the breadth and diversity of data available to make decisions increases and diversifies, banks, credit unions and other lenders must find new, innovative ways to leverage these insights. There are plenty of advantages to doing so, including the potential to unlock credit opportunities for a larger number of consumers.

The effort is not as challenging as it might sound. The use of technology like advanced analytics can help harness the power of data in valuable ways that don’t require significant changes to infrastructure. Our research shows most lenders are already using both advanced analytics and expanded data to some degree.

Here are three practical ways lenders can maximize their results and create new opportunities that benefit consumers and the broader economy along with their own bottom line:

1. Improving Risk Assessments to Give More People Access to Credit

While the banking industry as a whole has made a lot of progress with incorporating new data into decisions, we can and must do better to ensure all consumers have access to fair and affordable credit.

When determining whether to extend an offer of credit, lenders typically review a consumer’s credit profile to get a historical view of how that individual has been managing and repaying debt over time. But that standard is no longer enough.

We know millions of consumers are excluded from the mainstream credit ecosystem. Our research shows 106 million Americans, or 42% of the adult population, lack access to mainstream credit because they are credit invisible, unscoreable or have a subprime credit score. As a result, credit quality assessments, as historically performed by lenders, leave these potential borrowers behind.

A Market Opportunity:

The number of Americans who are credit invisible, unscoreable or have a subprime credit score:
106 million

Instead of relying solely on mainstream credit data, lenders should also draw on expanded Fair Credit Reporting Act data. This would allow more consumers to be scored and enter the mainstream credit ecosystem. Essentially, these insights enable lenders to expand their pool of applicants without compromising their risk tolerance.

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2. Reducing the Deployment Timelines for Credit Models and Other Operational Inefficiencies

Here’s a major challenge many in the banking industry face today: As credit models become more sophisticated, deployment timelines and costs can increase.

For example, many lenders are leveraging sandbox environments to build and test custom models. Moving these models into a production environment can be a lengthy process.

“As credit models become more sophisticated, deployment timelines and costs can increase.”

Our research shows it takes 15 months on average to build and deploy a model for credit decisioning, with 10 months dedicated to the deployment of the model alone. Not only that, 55% of lenders have built models that have not made it to production. This creates operational inefficiencies, although that doesn’t have to be the case.

Barriers to developing models are contributing to these long implementation timelines. Common challenges include finding and retaining data science talent, as well as acquiring the right data. By leveraging the power of artificial intelligence and machine learning, lenders can deploy new models to market in days or weeks, instead of months, and lessen the burden on the internal teams involved in the process.

Today’s newest technology solutions allow lenders to deploy and run models created in any popular open-source language or development platform without recoding or additional technical support. This is a gamechanger in operational efficiency and allows valuable talent resources to be allocated elsewhere.

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3. Automating the Monitoring of Credit Models

In a rapidly changing environment, it’s extremely important for lenders to be empowered to react to trends in the banking industry. As consumer behavior changes, so must the models used to make credit decisions.

It is common for lenders to periodically monitor for model drift. However, with the latest developments in advanced analytics, lenders can continuously monitor models without creating operational inefficiencies.

The Advantage of Automation:

Instead of periodically monitoring for model drift, why not do so continuously? Technology can facilitate this without straining the staff.

In addition, consumers deserve explainable models based on current and predictive behavior. For example, if a consumer misses a payment, but rectifies that delinquency, lenders should have visibility on this in near real time. Similarly, if a consumer applies for a loan with a lender in the morning and then applies for another loan from a different lender that afternoon, lenders should have visibility to this in near real time.

Leveraging advanced analytical tools to proactively monitor and track credit model performance allows risk to be assessed more accurately while improving fairness for consumers.

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A Win for Lenders and Consumers

In short, the use of advanced analytics — including AI and machine learning — will help reduce operational inefficiencies and allow lenders to gain meaningful insights from significant amounts of data with speed and accuracy.

This type of technology can help lenders overcome the challenges they face in deploying better predictive models to market while helping more consumers gain access to fair and affordable credit.

About the author:

Scott Brown is the president of consumer information services at Experian.

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