AI-Powered Hybrid Decision Support Systems for Sustainable and Inclusive Community Banking

Abstract

The operations of community development banking create essential financial services accessibility to rural and underprivileged regions. Standard Decision Support Systems (DSS) prove insufficient when it comes to dealing with intricate decision situations which occur across these environments. The research evaluates Hybrid Decision Support Systems (HDSS) to examine their ability in improving operational and strategic outcomes for community banks. The combination of artificial intelligence with multi-criteria decision-making (MCDM), fuzzy logic and optimization techniques enables HDSS to deliver enhanced accurate adaptive and overall superior decision-making capabilities. Research investigators conducted a comparison between Traditional DSS and HDSS systems according to five performance measures that included Credit Risk Accuracy and Customer Ranking Efficiency and Decision Speed and ESG Impact Inclusion and Operational Cost Reduction. The research sample consisted of thirty banks where fifteen organizations operated Traditional DSS and another fifteen organizations adopted HDSS. All metrics indicated HDSS provided superior performance than Traditional DSS because HDSS achieved scores between 77 and 81 whereas Traditional DSS reached 61 to 66. The relationship between both systems showed strong positive correlation according to analysis and frequency results indicated higher bank scores surpassed 75 under HDSS. Science-based optimization approaches helped users determine bank rankings as well as pick the most effective performance approaches. The research proves that the adoption of HDSS produces accurate results while being both efficient and economically sustainable. The investigative work demonstrates HDSS systems produce significant transformative potential for community banking which provides essential knowledge for both policy stakeholders and banking institutions and developer teams working in technology.