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All Journal Mikailalsys Journal of Advanced Engineering International Mikailalsys Journal of Mathematics and Statistics
Adanna, Arinze Betsy
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Chemical Engineering, Chemistry & Bioengineering Computer Science & IT Electrical & Electronics Engineering Engineering Mathematics Mechanical Engineering

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The Impact of Artificial Intelligence on Risk Management in Banking and Finance Akinnagbe, Olayiwola Blessing; Akintayo, Taiwo Abdulahi; Adanna, Arinze Betsy
Mikailalsys Journal of Advanced Engineering International Vol 2 No 2 (2025): Mikailalsys Journal of Advanced Engineering International
Publisher : Darul Yasin Al Sys

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58578/mjaei.v2i2.5195

Abstract

This research explores the transformative role of Artificial Intelligence (AI) in risk management within the banking and finance sector. It examines how AI technologies such as machine learning, natural language processing, and predictive analytics are enhancing risk assessment, fraud detection, and regulatory compliance. The study also highlights challenges such as data privacy, algorithmic bias, and the need for skilled professionals. The findings suggest that AI is revolutionizing risk management but requires careful implementation to mitigate associated risks.
Developing an AI-Driven Predictive Model for Stock Market Forecasting in the Banking Sector Akinnagbe, Olayiwola Blessing; Akintayo, Taiwo Abdulahi; Adanna, Arinze Betsy
Mikailalsys Journal of Mathematics and Statistics Vol 3 No 2 (2025): Mikailalsys Journal of Mathematics and Statistics
Publisher : Darul Yasin Al Sys

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58578/mjms.v3i2.5197

Abstract

This study develops an AI-driven predictive model for stock market forecasting in the banking sector, using LSTM, Random Forest, and Linear Regression. Historical stock prices, macroeconomic indicators, and banking sector metrics were analyzed, with data preprocessing techniques applied to enhance accuracy. Model performance was evaluated using MAE, RMSE, and R², with LSTM achieving the best results (R² = 0.92). Findings suggest AI models can improve investment decisions, trading strategies, and risk management. Future research should explore real-time data integration, sentiment analysis, and hybrid AI models for enhanced forecasting accuracy.
Co-Authors Akinnagbe, Olayiwola Blessing Akintayo, Taiwo Abdulahi