The following article was originally published by FORBES on August 3, 2021

Lending is as old as money itself but, like all areas of financial services, lending is being disrupted by new players and rising customer expectations. Here we consider how data and technology are transforming retail lending to meet the needs of an impatient, digital world.

There’s no question that lending is a powerful financial tool - empowering individuals to align spending with their needs and lubricating the wheels of global commerce. The earliest examples of lending date from around 2000 BCE, when merchants gave loans to farmers from seedtime to harvest. Such lending is a mainstay of global banking today.

But retail lending is undergoing seismic change and the distance between payment and lending is constantly shortening. Here’s how data and technology are at the heart of this change and what consumers can expect ahead:

Credit Checks – Then and Now

Traditionally, lenders have relied on historical data to perform credit checking. By looking at an individual’s credit history, a lender (or a credit reporting agency) could establish whether credit was repaid on time and how it’s 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 they have a low – or no – credit score. This is most marked among Generation Z.

As lenders consider new data sets to assess credit worthiness they can potentially open up a new world of potential borrowers. Experian Boost, for example, allows individuals to assess and increase their credit score by connecting bank accounts with regular payments – for example utility bills – to find qualifying 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 are harnessing the power of data to offer loans to new customers who would not qualify using traditional credit. All lenders are currently engaged in a hunt for good data. Among many options, things like buy now, pay later installment loans that are not currently reported as part of credit scores or even social media accounts that are being evaluated for their potential to augment traditional credit checks, to help get marginal loans approved and deliver credit where it is needed.

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

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

While new data is being used for different purposes, credit regulations will always require standardized data. In addition, new accounting standards – such as CECL (Current Expected Credit Loss)– force banks to acknowledge expected future losses on loans immediately. So, there is increasing pressure for banks and lenders to monitor loan portfolios continually.

How Can Banks Prepare?

With lending in a constant flux, banks need to plan for a future that is inherently uncertain. But there are some things we know. The future of lending is digital and will be driven by data. And digitalization of the lending process has the potential to drive better lending decisions, significant cost savings and an improved customer experience.

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