Causal AI for Evaluating the Impact of AI-Driven Credit Decisions on Financial Inclusion

Abstract

Credit decision-making is one area where artificial intelligence (AI) is making a big splash as it transforms the world of financial services. Credit scoring models powered by AI outperform their more conventional counterparts in terms of efficiency, scalability, and predictive capacity. But there are serious worries about bias, fairness, and the actual effect on financial inclusion that these models will have if they are widely used. Metrics for performance like as accuracy and area under the curve (AUC) don't tell us much about the reasons and effects of these models on various socioeconomic groups. For the purpose of assessing the effect of AI-based lending choices on financial inclusion outcomes, this research presents a Causal AI framework, with a focus on vulnerable and disadvantaged communities. Our method isolates the influence of AI-driven credit models on access to credit by using causal inference methods such propensity score matching, treatment effect estimates, and counterfactual analysis. Using real-world datasets to build treatment-control groups, we evaluate financial inclusion metrics (such as approval rates, loan amounts, and credit ratings) before and after AI-based solutions are implemented. To better understand whether AI is helping to close or grow the inclusion gap, this causal approach helps to separate correlation from causation. To better understand how AI-based lending choices affect various subgroups, we conduct heterogeneous treatment effects analysis. These subgroups are characterised by income, gender, region, and credit history. While AI can increase productivity generally, our research shows that it might unintentionally reinforce existing biases if not well vetted. On the other hand, AI may greatly increase credit accessibility for underserved populations via fairness-aware design and pragmatic validation. To ensure accountability in algorithmic lending, the suggested Causal AI architecture serves as both a diagnostic tool and a governance mechanism. When it comes to deploying, evaluating, and correcting models, it helps regulators, financial institutions, and AI practitioners make data-driven judgements. By advocating for fair access and ethical innovation, our effort ultimately helps with the responsible use of AI in financial services.