The new generation of business owners

A Harvard Kennedy School survey found 59% of 18- to 29-year-olds view AI as a threat to their careers, while employment for young adults in AI-exposed jobs has fallen 16%. The same report said vocational-based community college enrollment has risen nearly 20% since 2020. NPR reporting has pointed in the same direction, describing a “toolbelt generation” and rising interest in vocational paths tied to HVAC, electrical, and wind-turbine work.

When more electricians, plumbers, HVAC technicians, welders, and contractors enter the market, one of their first steps is often equipment financing: a truck, a trailer, a compressor, a lift, or a set of specialized tools that allows them to take on jobs and bill customers. The bank or credit union that can engage a trade-based business customer is financing the machinery behind a revenue stream.

The Bureau of Labor Statistics projects that electricians will grow 9% from 2024 to 2034, heating, air conditioning, and refrigeration mechanics and installers will grow 8%, and plumbers, pipefitters, and steamfitters will grow 4%. Overall employment in installation, maintenance, and repair occupations is projected to grow faster than average over the decade.

Why traditional monitoring late cycle falls short

Credit risk has always been a science and an art. Institutions vary widely in their approach to credit risk modeling and monitoring. But many traditional credit risk models and processes share a common limitation: they rely on periodic data pulls, “black box” third-party models, and static assumptions. And in many cases, analysis is limited to retrospective/historical review.

Processes that rely entirely on past loss rates, monthly delinquency positions, and/or instrument-level probabilities of default (PDs) from a third party that haven’t been backtested against your own experience or that of named peers are increasingly insufficient this late into a credit cycle.

There are real advantages to evaluating or re-evaluating your approach to credit monitoring and adjacent process in the current environment. AI and modern visualization tools can help leadership charged with managing and monitoring credit risk by providing real-time data and trends, relevant industry data, and consolidating inputs and outputs from critical models such as allowance, stress testing, ALM, and deposit-related tools.

Moving beyond allowance in a vacuum

The allowance is the one area of credit modeling that directly impacts financial statements. The process is subject to external audit and examination. For these reasons alone, it’s common for institutions to modernize the process. As we all know, some choices can have a cascading effect throughout an organization, and this area is one of them.

When the allowance is managed in a spreadsheet or in a vacuum, the exercise becomes one of data entry, simple historical loss rates, storage, filed away spreadsheets, and canned reports. It becomes challenging to communicate inputs, assumptions, and results to anyone within the organization removed from the actual creation of the “answer.” To simply view output trends becomes a time-consuming exercise for everyone involved.

There are also approaches that may seem advanced, such as some third-party provided PD and LGD that haven’t even been backtested against your own experience, but they can’t be audited/reviewed. Nor can the default rates be explained by leadership. This severely limits the value of the entire process and leadership’s ability to let the allowance process become an integral part of credit risk monitoring.