American International Journal of Research and Innovation

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Artificial Intelligence-Based Financial Risk Prediction Models

Author(s) Georgios Sermpinis
Country United Kingdom
Abstract The increasing complexity of global financial systems, market volatility, digital transactions, and emerging economic uncertainties has created significant demand for advanced risk prediction approaches. Traditional financial risk assessment methods often face limitations in handling large-scale, dynamic, and complex financial data. Artificial intelligence (AI)-based financial risk prediction models provide advanced analytical capabilities by integrating machine learning, deep learning, predictive analytics, and automated decision-support mechanisms.
This conceptual research article examines AI-based financial risk prediction models by analysing their applications in credit risk assessment, market risk forecasting, fraud detection, financial stability analysis, and intelligent decision-making. The study adopts a conceptual research methodology based on the synthesis of scholarly literature, financial analytics frameworks, artificial intelligence models, and risk management approaches. The analysis indicates that AI-driven models improve prediction accuracy, identify hidden patterns in financial data, and enhance proactive risk management capabilities.
Machine learning algorithms, neural networks, and advanced analytics techniques support financial institutions in evaluating risks more efficiently and adapting to changing market conditions. However, challenges related to data quality, model transparency, regulatory compliance, cybersecurity, and ethical considerations influence practical implementation. The study concludes that AI-based financial risk prediction models can strengthen modern financial systems when developed through transparent, responsible, and well-governed analytical frameworks.
Keywords Artificial Intelligence; Financial Risk Prediction; Machine Learning; Deep Learning; Credit Risk Analysis; Financial Analytics; Risk Management.
Field Engineering
Published In Volume 7, Issue 6, November-December 2025
Published On 2025-11-03
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