Five Questions on FinTech – Liz Pagel

1. The COVID-19 pandemic and provisions of the CARES Act have led to record numbers of consumers in forbearance. As the forbearance period ends, how can FinTechs evaluate consumers’ likelihood of financial recovery?

With more than 13 million consumers in forbearance on one or more loans in their wallets, FinTech lenders need to think about how consumers’ financial situations have changed in this unprecedented environment.[1]

As the forbearance periods end, consumers in forbearance programs will experience a payment shock. FinTechs also need to be aware of how consumers’ loans in forbearance with other lenders might impact their ability to pay their personal loan.  

Our research found that 9.5% of FinTechs’ unsecured personal loans are currently in forbearance programs.[2]  Many of these consumers will be coming off forbearance in the coming months, potentially in significant waves, leading to a new influx of calls to customer service teams.

In addition to the end of forbearance periods, there are several other payment and income shocks looming in the US economy.  Lenders, landlords and utility companies who had been lenient about missed payments will tighten their policies over the coming months.  At the same time, with record high unemployment, the expanded unemployment benefits are a topic of intense debate in Congress and we are likely to see changes. While some consumers will receive additional stimulus checks, the timing, amount, and who will receive them are also uncertain.

All of this means that FinTech lenders need to prepare now.

2. What treatment approaches should FinTechs consider for consumers coming out of forbearance?

Lenders should prepare for this by proactively segmenting their forbearance populations into categories:

• Likely to be able to resume regular payments
• Likely to need additional month(s) of forbearance
• Likely to need to restructure for lower payments
• Likely to move to collections

Using this segmentation, FinTech lenders can proactively manage their accounts and be prepared with appropriate offers when consumers call in at the end of the forbearance period looking for relief.   

TransUnion has the tools to enable this segmentation. Lenders can use CreditVision Acute Relief Attributes to identify which borrowers are also in forbearance on off-us trades, giving better visibility into the consumer’s financial situation.

FinTechs can also use the additional insights provided by trended credit data – such as aggregate excess payment – to understand which borrowers have extra funds. For instance, if a consumer is paying $100 over their minimum payment due across their other products, they could repurpose that payment to cover their personal loan as it comes off forbearance.

3. We’re operating in a very different environment than before the pandemic. As consumers’ liquidity sources – stimulus payments, expanded unemployment benefits, forbearance – dry up, what account management or collections strategies should FinTechs consider?

Consumers appreciate the lenders that have their best interests in mind.  We recommend FinTechs proactively reach out to consumers who may be struggling. This is a very valuable way to build trust with consumers, which may ultimately help lenders stay higher up in a consumer’s payment hierarchy.

For FinTechs, account management and data hygiene strategies are more important than ever. During this time, FinTechs should conduct account management reviews at least monthly. These practices can help FinTechs understand when consumers are in financial distress early. Triggers can identify consumers who are originating high-cost loans, detect changes in aggregate excess payment and identify off-us forbearance status (CreditVision Acute Relief Attributes).  All of these tools can help lenders get a more complete view of their portfolio dynamics.

4. FinTechs’ competitive advantage is their use of data and analytics. How should lenders rethink their models in the current environment?

FinTechs embraced trended and alternative credit data long before the pandemic. It’s helped them understand their customers and portfolios, and that same data can provide an advantage in the wake of COVID-19.

As forbearance begins to come off consumers’ credit files and we see performance results, we anticipate that FinTechs will look to rapidly redevelop their models to account for what we’ve learned about post-forbearance risk.  TransUnion’s analytics teams have been developing methodologies in anticipation of these model rebuilds all summer. They have also built highly effective models that leverage payment data and trended data instead of the derogatory data that is masked – these have proven to be competitive with existing scores.  We’re working closely with our FinTech customers to provide data and scores as they prepare to build their new models (or tweak existing ones) and evolve them through the end of 2020 and into 2021.

5. Since the pandemic, identity fraud has increased. How can FinTech lenders reinforce their defenses against fraud as it evolves and shifts?

As borrowers moved online, fraudsters saw a significant opportunity to take advantage of vulnerable consumers and lenders who were not as accustomed to doing so much business virtually. Since the pandemic, identity fraud has increased 23%, according to a TransUnion traffic analysis.

While FinTechs have led the way in online lending, fraudsters are becoming more aggressive, targeting all lenders – not just those who are new to the online channel.  As a result, additional fraud prevention measures are more important than ever.  Document and device authentication capabilities are particularly helpful for FinTechs as new segments of consumers apply and conduct business online. A device’s fingerprints are a powerful tool, allowing you to see if the device itself has been associated with fraud in the past, effectively targeting some of the new fraud tactics.

[1] Source: TransUnion consumer credit database. This metric does not include student loans.

[2] Source: TransUnion consumer credit database.