How data shortens the distance between payment and finance


Credit is as old as money itself, but like all areas of financial services, credit is being disrupted by new players and growing customer expectations. Here we take a look at how data and technology are transforming retail lending to meet the needs of an impatient digital world.

There is no doubt that lending is a powerful financial tool – allowing individuals to match their spending with their needs and lubricate the cogs of world trade. The earliest examples of loans date from around 2000 BCE, when merchants gave loans to farmers from seed to harvest. Such loans are today one of the pillars of the World Bank.

But retail credit is changing, and the distance between payment and lending continues to grow shorter. Here’s how data and technology are at the heart of this change and what consumers can expect in the future:

Credit checks – yesterday and today

Traditionally, lenders have relied on historical data to perform a credit check. By reviewing a person’s credit history, a lender (or a credit reporting agency) could determine if the credit was paid off on time and how it is currently being managed. But with many modern retail loans, lenders are often exposed to a new generation of eager borrowers who have a different attitude towards money. Often they cannot meet traditional borrowing criteria and sometimes have a low or no credit rating. This is most marked among Generation Z.

As lenders examine new data sets to assess creditworthiness, they can potentially open up a whole new world of potential borrowers. Experian Boost, for example, allows individuals to assess and increase their credit score by connecting bank accounts to regular payments – such as utility bills – to find eligible on-time bill payments. These timely payments can be used to build and improve a credit score, which can make a big difference to a potential borrower.

Data, Artificial Intelligence and Regulation

The important point is that lenders harness the power of data to offer loans to new customers who would not qualify with traditional credit. All lenders are currently on a hunt for good data. Among many options, things like buy now, pay off subsequent installment loans that are not currently reported under credit scores or even social media accounts that are rated for their potential to augment traditional credit checks. , help get marginal loans approved, and provide credit where it’s needed.

Artificial intelligence (AI) is playing a growing role in testing and building loan models. While progress is being made, many models rely on historical data that do little to foster financial inclusion. This often has an “unconscious bias” in some data, for example postal codes. There is a real risk that AI will reproduce the sins of the past but on a larger scale. The key to successful credit modeling is to simulate who should have received credit rather than who received it in the past.

But, in the United States, lending and credit are tightly regulated, so supporting data must be standardized to ensure fairness and facilitate comparison. The challenge is how to regulate something brand new and constantly changing. In practice, the approach is often piecemeal – new data allows lenders to focus on specific loans and borrowers to make informed decisions.

While new data is used for different purposes, credit regulation will always require standardized data. In addition, new accounting standards – such as CECL (Current Expected Credit Loss) – require banks to immediately recognize expected future losses on loans. Thus, banks and lenders are under increasing pressure to constantly monitor loan portfolios.

How can banks prepare?

With lending constantly changing, banks must prepare for an inherently uncertain future. But there are some things we do know. The future of credit is digital and will be driven by data. And digitizing the lending process has the potential to lead to better lending decisions, significant cost savings, and a better customer experience.

With the right technology, banks can be prepared to ingest larger volumes of complex data to meet a wide range of lending needs and ultimately better serve consumers.


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