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Credit Intelligence: Bridging the Gap in SME Credit

Credit Intelligence: Bridging the Gap in SME Credit
Credit Intelligence: Bridging the Gap in SME Credit

By Ashish Nayyar, Co-head – India, OakNorth

Small and medium-sized enterprises (SMEs) are the backbone of communities and economies globally, representing c.90% of businesses and more than 50% of employment worldwide.

They are the businesses governments and regulators around the world continue to identify as those in need of support – yet much about the decision-making process used by banks when an SME applies for a loan has changed little (if at all) over the last four decades despite the enormous advances in technology and society in that time.

Banks still make credit decisions largely on the basis of intuition and experience of their credit officers, and if they use models for forecasting at all, the forecasts are based on historical data and share fundamentally flawed root assumptions: firstly, that tomorrow will be a lot like yesterday and, secondly, that most businesses are more or less alike. Over the years, this has proved to be good enough for the most part, and, as a result, these models are considered sacrosanct.

Traditional commercial loan modeling tends to follow this process: a credit analyst constructs a financial model (usually using a spreadsheet program such as Microsoft Excel) to simulate the cash flow, balance sheet, and income statement of the business.

They then project these forward for the lifetime of the loan and use assumptions to ‘sensitize’ or stress this model to observe the performance of the business under adverse circumstances. This allows them to see whether the business will have the liquidity to continue operations and generate enough free cash flow to pay back the loan.

This is augmented by peer group analysis, where a prospective borrower is compared with other similar businesses in order to establish reasonable expectations for future performance. If similar businesses have been able to generate a certain amount of profit or pay back a similar loan in the past, it is reasonable to assume that the borrower will also be able to. A bad experience with a particular type of business, however, could put the credit officer, relationship manager, or the bank, generally off the idea of ever lending to a similar business again. 

The process of constructing both the financial model and the list of peers is subjective, and, therefore, the quality of results can vary depending on the experience and skill of the credit analyst. In many institutions, the usual procedure is to take the most recent model for a similar business, copy it, and make changes to update it to reflect the new business. There are several challenges with this, however:

  • It could be subject to human error by accidentally leaving idiosyncratic features of the initial business in subsequent iterations of the model.
  • There could be inconsistencies resulting from the application of different models and, therefore, different standards to different sectors or from using different base models for different businesses in the same sector.
  • There could be concept drift, whereby the model does not account for gradual changes in the underlying industry because it is continuously being reset to a common baseline by this copying process and therefore becomes increasingly inaccurate over time.
  • It is also clear that historical models can only account for conditions that are seen in the historical data. Unprecedented events, such as the COVID-19 pandemic challenge these models to the point where their predictions break down entirely.

We believe that by adopting a data-driven alternative that takes into account the fundamental differences between businesses, lenders can make faster, smarter, data-driven decisions that will ultimately lead to better credit outcomes.

This is an approach known as Credit Intelligence – a new way of operating that gives commercial lenders a 360-degree view of borrowers based on historical data and, critically, a comprehensive, forward-looking view using algorithmic, continuous analysis of multiple drivers across the business, its peer group, and the wider economy.

This provides an independent, consistent, detailed framework offering deep contextual insight that enables rapid underwriting, immediate stress-testing, and the ability to open up new opportunities at lower risk through more agile and targeted strategic lending. By leveraging Credit Intelligence, lenders can transition commercial lending from a backward-looking, excel-based, business intelligence-led approach to a forward-looking, platform-based, data-driven approach that combines business intelligence with artificial intelligence.
In doing so, they will be able to lend faster, smarter, and more to businesses – thereby helping to bridge the gap in SME credit.
The authored article is written by Ashish Nayyar, Co-head – India, OakNorth and shared with Prittle Prattle News  exclusively.
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