Financial institutions face a clear problem: translating artificial intelligence capability into measurable improvements in risk control and forecasting accuracy across heterogeneous regulatory contexts. The purpose of this study is to quantify those links. Using a quantitative cross-sectional, case-based design, we analyze 360 cloud-enabled enterprise cases spanning banks, insurers, non-bank financial institutions, and fintechs in 12 countries. A scoping review of 46 peer-reviewed studies informed construct design and hypotheses. Key variables include AI Maturity, Predictive-Use Intensity, and Governance or Risk Culture, with outcomes covering credit-loss ratio, non-performing loan ratio, Value-at-Risk exceptions, and business planning errors such as revenue and liquidity MAPE. The analysis plan combines harmonized descriptives and correlations with OLS for continuous outcomes, negative binomial models for over-dispersed counts, clustered robust standard errors by country, moderation by national digital readiness, and extensive robustness checks including leave-one-country-out and alternative estimators. Headline findings show that higher AI Maturity is associated with lower credit losses and fewer VaR exceptions, while greater Predictive-Use Intensity is associated with materially lower forecasting errors; effects are stronger in digitally ready environments and governance complements but does not substitute for maturity. Implications for practice are to prioritize data lineage, deployment automation, and monitoring, scale predictive use across risk and FP&A processes, embed explainability and subgroup calibration, and align controls to the strictest-applicable regulatory standard so gains travel across jurisdictions.
Dola et al. (Wed,) studied this question.
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