Auto Decisioning for Commercial Loans: A Good Thing for Community Banks?

As a lifelong proponent for bringing efficiency and the elimination of redundancy out of the commercial credit origination and management process, I want to offer up a cautionary yellow flag before you proceed with the newest big thing being pushed by FinTech companies at banking conferences. The push for commercial loan growth and automating decisions on new commercial loan originations is the current rage at these conferences and is the newest shiny object being pushed by FinTech vendors. While I have always advocated technology and efficiency in the commercial lending process, I have never done so if it violated good credit best practices or could lead to credit quality issues when the economic cycle turns down or into recession mode. It has been 10 years since the last great recession. If you believe we will never have another economic downturn in the future, then please ignore the remainder of this memo. The following is an excerpt on regulatory guidance and auto credit decisioning and the sound practices to consider when developing these models for use in your institution. If you must proceed with this approach to commercial lending, even for just smaller commercial credits, please take the following guidance into account.

Subject: Sound Practices for Model Risk Management

The Office of the Comptroller of the Currency (OCC) has adopted the attached Supervisory Guidance on Model Risk Management. This guidance, developed jointly with the Board of Governors of the Federal Reserve System, articulates the elements of a sound program for effective management of risks that arise when using quantitative models in bank decision making.

Like other models, automated commercial credit scoring systems should be carefully evaluated and periodically validated. Until banks gain more experience with them under a range of market conditions, they should use such systems to supplement more traditional tools of credit risk management: credit analysis, risk selection at origination, and individual loan review.

One of the biggest impediments to the development of commercial credit scoring models has been the lack of data. Until recently, most banks did not maintain the data on commercial loan portfolios needed to develop the statistical analysis for modeling. However, after the credit events of the late 1980s and early 1990s, banks began to develop these databases. Because defaults and losses have been rare in recent years, constructing the databases with the number of observations necessary (thousands in some cases) has been difficult. Furthermore, these models have not yet been tested through a full business cycle. Whether they will be accurate during a recession, when safety and soundness concerns are most acute, remains a question.

Models can improve business decisions, but they also impose costs, including the potential for adverse consequences from decisions based on models that are either incorrect or misused. The potential for poor business and strategic decisions, financial losses, or damage to a bank’s reputation when models play a material role is the essence of “model risk”.

Model risk should be managed like other types of risk: Banks should identify the sources of that risk, assess its magnitude, and establish a framework for managing the risk. The extent and nature of the risk varies across models and banks; risk management should be commensurate with the nature and scope of the risk. Model risk management should include disciplined and knowledgeable development and implementation processes that are consistent with the context and goals of model use and with bank policies.

Banks should objectively assess model risk using a sound model validation process, including evaluation of conceptual soundness, ongoing monitoring, and outcomes analysis. Model usage provides opportunities to test whether a model is functioning effectively and assess its performance over time. A central principle for managing model risk is the need for “effective challenge” of models: critical analysis by objective, informed parties who can identify model limitations and assumptions and produce appropriate change. Effective challenge depends on a combination of incentives, competence, and influence.


Duane Lankard
Suntell CEO & Founder

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