Using Artificial Intelligence to Predict Financial and Administrative Risks: An Applied Study at the International Development Bank for Investment and Finance (2015–2024)

Abstract

This research aims to determine the impact of using artificial intelligence (AI) techniques on predicting financial and administrative risks, through an applied study of the International Development Bank for Investment and Finance, covering the period from 2015 to 2024. The study is motivated by the growing need within banking institutions to develop analytical tools capable of handling increasing data complexity and enhancing the accuracy of risk management decisions—particularly in light of the economic and financial shifts observed in the Iraqi market in recent years. The research employs an applied methodology based on the analysis of the bank’s historical data and the development of predictive models using AI techniques—such as Random Forests, XGBoost, and neural networks—to compare their risk-prediction capabilities against traditional risk management methods. The study examines various financial and administrative variables, including credit default indicators, liquidity levels, capital adequacy, compliance and governance metrics, and employee turnover, and assesses how these factors influence the bank’s risk exposure. The findings indicate that the use of AI can significantly improve the early detection of risks and reduce uncertainty in decision-making, while also enhancing resource allocation efficiency and the quality of internal governance. Furthermore, the research offers practical recommendations for integrating intelligent models into the risk management framework, ensuring the sustainability of their performance through periodic reviews and robust model governance.