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Credit is king. A recent study found 77% of consumers preferred paying with a debit or credit card compared to only 12% who favored cash. But easier payment options isn’t the only reason the availability of credit is important to consumers.
Having good credit aids in receiving favorable financing options, landing jobs and renting an apartment, to name a few examples. With so many of life’s important necessities hinging on credit history, the approval process for loans and cards is more important than ever.
Artificial intelligence solutions are helping banks and credit lenders make smarter underwriting decisions by utilizing a variety of factors that more accurately assess traditionally underserved borrowers, like millennials, in the credit decision making process.
Lenders of all shapes and sizes are on a path of digital transformation that will allow them to realize the benefits of process automation and capture new business opportunities. Before the outbreak of COVID-19, early adopters of innovative digital technologies were already gaining competitive advantage. It’s easy to see the impending disruption of the entire banking industry by AI – disruption that is accelerating, thanks to easier access to better and faster algorithms.
According to the Financial Stability Board, use of AI, machine learning (ML) algorithms and automation is now commonplace across many lines of business within banks, including marketing, customer experience management, fraud detection and trading. It’s expected that AI and ML could also be used to detect early warning signals of distress by analyzing cash flow forecasts, income and expenditure data, and more. In addition, these technologies could help generate more accurate forecasts using real-time data − specifically, short-term forecasts rather than longer term views.
However, adoption of AI and ML has been slow in other business areas; what will it take for banks to trust AI and ML with judgments about data accuracy and commercial lending process automation?
This whitepaper, drawing on recent academic evidence and business insights, provides a contemporary look at what AI and ML adoption could mean for commercial lending and credit risk assessments while also proposing different approaches to AI and ML adoption tailored to each step of the commercial lending process.