According to a recent McKinsey study, the typical “time to decision” for commercial lending is between three and five weeks, while “time to cash,” on average, fluctuates around three months. This is a far cry from the rapid, automation-centric decisioning enjoyed in consumer lending for decades, enabling near-instant approvals for customers.
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. Surging inflation and COVID-19 has caused major disruption in credit risk management across multiple industries though, resulting in uncertainty and supply chain disruption. Some lenders are delaying adoption due to concerns about the lack of transparency and interpretability of AI and ML models and incompatibilities with legacy systems. The commercial business segment has other deeply engrained reasons for holding on.
As also noted by the McKinsey study, commercial credit lending has been historically based on “years of root-cause analysis of defaults and assessment of soft factors.” Operationally, this has translated into a continued dependence on manual processes and cross-checks. Trusting a highly manual commercial underwriting process is hindering the adoption of all types of automation, including AI and ML powered techniques.
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 will be increasingly 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 for short-term forecasts rather than longer term views.
Concerns about the quality of self-reported data remain front of mind for loan officers. Organizations seeking a loan may use data from doctored books, unrealistic sales and growth projections, and more to strengthen their case. Banks currently typically rely on experienced loan officers to judge data accuracy without the assistance of AI and ML-driven analytics which can be used to seek out alternative data sources to gain clarity into an applicant’s ability to repay a loan and scale the size of the loan appropriately.
Commercial lending innovators are increasingly adopting AI and ML processes. By developing an incremental approach, AI and ML processes can be equally as effective as manual processes for commercial lending purposes but with potentially huge cost and time saving efficiencies. To succeed, lenders need a well-structured, step-by-step plan, a dedicated team of data scientists and data engineers, and clear business targets. Plans should encompass all areas where AI-driven automation is sought and outline specific benefits. Plans should focus on clearly defined goals; for example, by improving automation by 20%, reducing losses by US$20 million or lowering time to cash by 20 days. Lenders will typically also need to bolster their IT maintenance, monitoring and governance processes. They should conduct tests to identify exactly where they can improve the commercial loan process using advanced AI and ML models, start small and learn before aiming for bigger AI and ML projects with bigger potential impacts.
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