Data Chaos, Model Fragility, and the Human Fix for Financial GenAI

Abstract

While Gen AI can offer a significant advantage in terms of financial forecasting, this might become neutralized if institutions employ similar models. The prediction error correlations during high volatility may enhance the systemic risk through the homogeneous architecture. In this study we seek answers to the questions whether good data quality can have consequences and whether human guidance can temper this risk. The data consists of 285 survey responses collected from financial professionals at Pakistani banks, asset managers, fintech and investment banks. Using structural equation modeling on the cross-sectional data gathered from financial professionals from 3Q2025 to 1Q2026, we conclude that increasing Gen AI adoption does improve forecasts; this effect stems partly from demanding more holistic multi-domain data quality. However, the finding comes with a caveat: human expertise calibration is characterized by an inverted-U shape — lack of it opens up blind spots while overreliance on it has a similarly negative effect. The best result lies in a moderate human judgment setting. These findings have clear implications for regulators, the design of corporate governance, and efforts at re-evaluating the human judgement setting. These finding have clear implications for regulators, the design of corporate governance,and efforts at re-evaluating the human contribution to Ai driven financial forecasting. 

Keywords- Generative AI; Financial Forecasting; AMOS; Moderated Mediation; ; Adaptive Fragility Risk; Human Expertise Calibration