AI Agent Autonomously Predicts CFPB Enforcement Actions Using BoTorch

An AI agent, powered by Perplexity, autonomously developed a Bayesian Optimization (BO) pipeline to predict which companies the Consumer Financial Protection Bureau (CFPB) might target for enforcement actions. The agent, without prior knowledge of BoTorch, independently selected the library and configured it using MixedSingleTaskGP as the surrogate model and LogExpectedImprovement as the acquisition function. The BO pipeline significantly outperformed random search, achieving an 86% higher mean F1 score across 48 evaluations. The agent optimized the entire research design, including parameters such as lookback window, minimum complaints, class weight ratio, and feature subset. The model identified several companies as high-risk based on statistical patterns in public consumer complaint data. The analysis revealed that a short lookback window of approximately 156 days is optimal, suggesting that recent complaint velocity is a stronger signal than cumulative volume. Heavy class weighting, upweighting enforcement cases by 18.5x, was also found to be critical. The model's performance is limited by a small matched dataset and a small test set, which raises concerns about overfitting. The lack of temporal validation, using a random train/test split instead of a chronological split, further limits the generalizability of the results.

Transparency note: The analysis is based on the provided description of the AI agent's methodology and results. The limitations of the model, including the small dataset and lack of temporal validation, should be considered when interpreting the findings.