How will AI change the consumer credit market?

What will consumer credit look like in a world where data is doubling every two years? Can innovative Artificial Intelligence (AI) technology increase accuracy? Will a new banking future open credit to more consumers?

Bankers, once renowned for their traditional desktop roles and proverbial handshakes are fast shifting into a new banking future. Falling long term rates, low profitability and a disruptive digital reality are impacting their business models, macroeconomic cycles and even their customers’ behavior. Navigating through shaky waters, banks are reviewing a slew of innovative technologies to cut funding costs and drive profitability.

Five reasons why consumer lending is experiencing an innovation leap with AI technology:

1. Complex and time consuming consumer lending processes are ripe for AI technology

One of the most lucrative banking processes ripe for AI technology innovation would have to be credit scoring for consumer lending. Consumer lending; from mortgages to credit cards, student loans, is a massive business as well as a treasure trove of big data----. Valuable Information including credit history, employment and income tax are just some of the data attributes that are captured and used for complex scoring calculations and forecasting.

“Forty-eight percent of banking executives think new technologies, such as AI, will have the biggest impact on retail banks through 2020, marking the first time that the impact of digitization trumps regulation.”

Source: The Economist Intelligence Unit Report

Traditional credit history is an important source of information, but it’s not the only data set that can be used to paint an accurate credit picture. At the end of the day, a loan’s value is tied to a consumer’s creditworthiness, so the more consumer data is made available, the better the assessment.

Source: The Economist Intelligence Unit Report

2. AI enables banks to tailor loans to customers’ needs

When data is leveraged with the right technology, credit scoring systems can help to predict consumer behavior and improve decisions.

This is where Artificial Intelligence comes in: The promise of AI is the ability to immediately analyze multiple sources of data, streamline internal processes, tailor loans to a consumer’s specific needs, and deliver faster service. 

The outcome is a win-win to the borrowers and lenders. On one hand, loans that were previously turned down to consumers can be reassessed and deemed safe, and by doing so broaden the bank’s client base. On the other hand, the loan review process is simpler and faster, creating a more appealing customer experience.

3. AI opens the credit loaning market to new revenue streams

A customer’s capacity to repay a loan is critical for their credit score, but in traditional banking it wasn’t so clear cut. In the past, bankers who knew their customers and valued their reputation, would often approve a loan, regardless of whether the person was able to pay it back. Branded as an unfair practice, banks eventually adopted the FICO model; a mathematical system for assessing credits.

Unfortunately, the downside of this conservative scoring system meant that droves of potential customers who had, on face value an unimpressive credit history, were deprived of loans. Ironically, in many cases, a poor record did not necessarily mean that these consumers were potential defaulters. This is why banks have used internally developed models that do not rely solely on the FICO credit score. Today, better modeling possibilities are available.

4. Makes it possible for banks to tap into a huge potential market

Decisions made by credit scoring systems that are backed by AI algorithms can offer a more accurate risk prediction than traditional scoring methods.

Equipped with a lot more internal data, as well as alternative data sources and predictive modeling, banks can provide more accurate credit-based decisions that are less inclined to be swayed by racial, gender and socio-economic biases. They can assess a customer’s ability as well as their willingness to repay, which in the past was impossible to realize. Finally, they can capitalize on a huge segment of previously unbanked customers, who for lack of a traditional credit history, had been excluded. Banks opting to target this new untapped market gain a significant competitive advantage over organizations that practice traditional credit scoring.

5. The impact of speed and automation when managing waterfalls of credit data

AI based technologies can help banks and other large institutions analyze waterfalls of data very quickly. Assessments performed at heightened speed make it possible to manage a far broader range of credit inputs, and measure at scale a far greater pool of potential customers, within significantly shorter timeframes.

Rogue customers working across multiple accounts, can be identified in near real time and trigger alerts. Enhanced reports and even portfolio monitoring can be performed automatically, saving banks weeks of intensive labor activities.

In sum

Traditional lending processes are becoming more time consuming and complex, and with every passing day, the gap between accessible data and the same narrow scope of utilized data grows. The introduction of advanced AI makes the process of consumer lending fast, more accurate and efficient. It uncovers the full potential of customer data and delivers better financial return, while cutting down expenses. Finally, intelligent consumer credit AI helps to identify and eliminate explicit and implicit bias and by doing so, creates a more fair system.

Have you or are you planning on adding AI machine learning technology to your banking strategy?