3 ways machine learning is having an impact on commercial lenders 📈

Machine learning is shaking up a number of different industries and this is especially true in data rich industries such as financial services. For commercial banks, specialty finance companies, and other companies making commercial loans, rising competition means that the use of predictive data analytics is critical, both for making better lending decisions as well as improving efficiency. Here’s a rundown of a few specific ways that machine learning is changing the commercial lending sector for the better.

Before we start, if you’re unfamiliar with machine learning, please take a look at this post for an introduction.

1. Better Underwriting

A primary driver of bottom-line returns for commercial lenders is loss rates on lending portfolios. The goal of the underwriting process is to make an accurate assessment of the likelihood of borrower default to drive the approval decision and loan terms. Commercial loan underwriting has historically been a very manual process, involving detailed financial analysis of items like cash flow forecasts and debt coverage ratios. Predictive models trained with machine learning techniques offer the potential to automate and optimize portions of this process. By creating models based on the lender’s historical loan performance, commercial lenders can create underwriting systems that automatically assess credit risk and support the conclusions of the standard financial analysis conducted by loan officers.

The use of such models has numerous benefits, including more consistent assessment of risk, greater efficiency in the underwriting process, and additional predictive accuracy. For many other sectors in the lending space, such as consumer lending, decision automation has been critical in optimizing operations. Much higher loan amounts in commercial lending have meant that automation is less of an imperative, however the potential for machine learning models to help commercial lenders make better decisions – and not just faster ones – is driving increased adoption.

Commercial lending has a very significant legal aspect, with a number of documents required to execute a lending transaction and secure the lender’s interest in the collateral. Machine learning models can assist lenders with ensuring these documents are created properly. Models can be trained to examine data on a deal and flag exceptions that are out of line with comparable deals. Such models can go beyond simple rules-based checks to identify discrepancies that may not have been anticipated by other built-in checks. By handling some of the repetitive work in validating documents, predictive models allow commercial lenders to save money and improve their margins.

3. Improved Forecasting

A huge number of variables influence a business’s ability to service its debt. Commercial lenders seek to incorporate as many of these influences as possible into their credit assessments. One important factor for businesses is the economic climate in which they operate, which is often specific to the geography and industry of the company. Machine learning models can help commercial lenders process large volumes of historical government data on economic cycles for a wide range of geographies and industries to create predictive models that can support an accurate assessment of the financial projections for a borrower. For underwriters, knowledge of such economic factors can be critical in helping them make lending decisions.

Conclusion

While commercial lending has traditionally relied more on judgmental analysis and less on predictive analytics that other lending sectors, commercial lenders have a lot to gain by implementing machine learning models. Such programs can help improve underwriting results, reduce errors and enhance efficiency, driving improved bottom-line results. In addition, the recent emergence of tools, such as automated machine learning, make it very simple for lenders to incorporate powerful predictive analytics into their lending processes.


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