Alternative lending key to growing membership, loans in 2020 and beyond

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There are 100 million consumers who are restricted by the traditional credit scoring methods used today either because they have a subprime score or they lack a traditional credit history. By harnessing the power of new credit score models that go beyond traditional credit data and incorporate an expanded set of data sources, credit unions can not only increase their customer base but also do so responsibly by minimizing risk in 2020 and beyond.

Greg Wright is chief product officer for consumer information services at Experian
Greg Wright is chief product officer for consumer information services at Experian

Expanded FCRA data, sometimes called alternative data, is a hot topic in the lending industry these days and there’s a valid reason for that. These new data sources can allow lenders to identify viable new customers while also gaining a more accurate picture of risk.

According to Experian’s 2019 State of Alternative Credit Data report, 65% of lenders say they are using information beyond the traditional credit report to make a lending decision and we expect to see this number increase significantly. Looking to the future, lenders plan to expand their sources for insight. The top three expanded data sources that lenders say they plan to use in the future are trended data or historical payment information (25%), rental payment history (24%), and telephone and utility payment history (19%).

The latest scoring models available today are making it easier for lenders to incorporate these new data sources into their decisioning. These new data advancements can help improve access to credit for the over 40 million credit invisibles who were viewed as unscoreable to lenders until now.

As we begin this new decade, here are the top reasons why lenders should integrate the latest data scoring models and data sets into their business process:

1. Identify new creditworthy customers and increase revenue

Traditional scoring methods can restrict access and opportunity for consumers who are subprime or lack a traditional credit history. Many of these consumers are just getting their financial feet wet, recovering from a financial setback or life-changing event, or are simply credit averse. Expanding beyond traditional credit data is an effective way to score consumers who may have previously been overlooked.

Data assets such as how a consumer manages their rental payments, whether they have a professional license, how they’ve managed a payday loan or other alternative financial products, and how they manage credit overtime can create a more complete picture of a creditworthiness. By incorporating these assets into FCRA regulated score models, credit unions can improve access for consumers who might otherwise be declined by looking at their financial stability, willingness to repay and ability to pay.

This empowers lenders to feel confident to lend deeper, make approvals that they otherwise wouldn’t and leverage additional data points that weren’t available until now to ultimately increase overall revenue. Consumers can benefit from the additional data by getting a first or even second chance at credit they wouldn’t otherwise have.

2. Mitigate risk with a more complete picture

Traditional scoring models can be an effective means for measuring a consumer’s creditworthiness, but they don’t work for everyone. To create meaningful growth in your portfolio in 2020 and beyond, finding new means for identifying consumers who have been overlooked by traditional methods used today is key. With the latest alternative data scoring models, you can do this without compromising risk. In fact, the latest models are proving to be more predictive and build a more accurate picture of a consumer’s ability, stability and willingness to repay than today’s most commonly used scores.

For example, by looking at historical payment information through trended data attributes that span more than 24 months, credit unions can see how a consumer uses credit or pays back debt over time to create a more accurate risk profile. By using these new predictive scores, lenders can minimize losses and delinquencies and detect risks earlier, all while complying with new regulations.

3. Leverage the latest advancements in technology

To stay competitive, credit unions must incorporate machine learning and artificial intelligence tools into their business practices to truly enhance predictive performance. The latest scores available today combine advanced analytics and are 23% more predictive than models that are currently used to score and underwrite credit invisibles. Half of that lift in performance comes from the new data sources included in the score models and the other half comes from the technology being used.

Lenders can use these new scores in three ways. The first is as a primary score and this can be very valuable for lenders specifically targeting the thin-file population. It also can be used as a second chance score where lenders can reexamine consumers that were declined and give them another chance to get approved. Finally, it can be used as an overlay to an existing score, which can help lenders better assess consumers because of that additional data and it can also allow lenders to say yes to a consumer they might have said no to or no to someone they might have said yes to without the score. Credit unions can seamlessly integrate these new scores into their current models without any major overhaul for better risk management and more agile decisions.

As we enter into the new year, it’s a good time to reflect on growth opportunities for your organization. For many credit unions, this growth will have to be sustained by finding new means for growing their member base and extending credit to new, responsible borrowers. The good news is that, we believe, expanded data scoring models will become the new “normal” in the upcoming decade – ultimately helping more consumer gain access to the financial products they need while helping lenders make more informed decisions. That’s a win-win for everyone.

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Consumer lending Data mining Experian
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