A Comparative Machine Learning Survival Models Analysis for Predicting Time to Bank Failure in the US (2001-2023)

Abstract

<p style="text-align:justify"><span style="font-size:10.5pt"><span style="font-family:等线"><span dir="ltr" lang="EN-US" style="font-family:&quot;Cambria&quot;,serif"><span style="color:black">This study investigates the likelihood of time to bank failures in the US between 2001 and April 2023, based on data collected from the Federal Deposit Insurance Corporation&#39;s report on &quot;Bank Failures in Brief - Summary 2001 through 2023&quot;. The dataset includes 564 instances of bank failures and several variables that may be related to the likelihood of such events, such as asset amount, deposit amount, ADR, deposit level, asset level, inflation rate, short-term interest rates, bank reserves, and GDP growth rate. We explore the efficacy of machine learning survival models in predicting bank failures and compare the performance of different models. Our findings shed light on the factors that may influence the probability of bank failures with a time perspective and provide insights for improving risk management practices in the banking industry.</span></span></span></span></p>