Publication | ThinkSet
Using Data Science to Predict Bank Failure and M&A Opportunities
Karl Schliep and Steven Gawthorpe
It began with a question from BRG’s Financial Services (FS) professionals a few months into the pandemic: Can open-source data be used to predict if a US bank will ultimately fail or be a target for M&A?
They had good reason to ask the question. While COVID-19 dragged on, bank M&A started to surge—it hit a fifteen-year high in 2021—as institutions looked for new ways to scale amid lackluster loan growth, low interest rates, and the growing prominence of digital banking. The FS team also saw parallels to the lead up to the Great Recession.
Over the next eighteen months, FS and artificial intelligence and machine learning (AI/ML) professionals collaborated to build a one-of-a-kind model that predicts bank health. The Bank Health Index (BHI) offers quarterly forecasts and access to BRG’s banking experts, which investors, private equity firms, law firms, and regulators can leverage to make decisions related to bank health and M&A.
Here’s a behind-the-scenes look at how we developed the data science behind this powerful tool.
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