Machine Learning Models for Financial Risk Assessment

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@article{1707832,
      author = {Mahabub Sultan},
      title = {Machine Learning Models for Financial Risk Assessment},
      journal = {Iconic Research And Engineering Journals},
      year = {2025},
      volume = {8},
      number = {10},
      pages = {330-338},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/1707832.pdf},
      abstract = {Financial risk assessment is a very crucial part of decision-making for institutions, investors, and regulators that involves the assessment and subsequent mitigation of probable threats that will adversely impact the financial condition of an entity or an entire market. Recently, the conduct of financial risk assessments using machine learning (ML) models has revolutionized the ability to provide dynamic, data-driven prediction, quantification, and management of risk. It differs from the historical methods of risk assessments whereby using ML methods, it can be distinguished and processed with vast historical and real-time data, revealing hidden patterns and being subjected to complexities-among other non-linear formations found in the financial ecosystem. The paper reviews various applications of ML algorithms in financial risk assessment, ranging from supervised learning (e.g., decision trees, random forests, gradient boosting, and neural networks) for credit scoring, loan default prediction, and fraud detection, as well as unsupervised learning (clustering, anomaly detection) to identify patterns that are out of the ordinary in transactions and market behavior. Models concerning deep learning and reinforcement learning were also studied concerning their high-level capabilities in market movement predictions, optimization of investment strategies, and portfolio management in relation to risks. One of the significant advantages of ML models in financial risk assessment is their ability to work with different types of data such as transactional data, social media sentiment, and macroeconomic indicators. It reveals risk from a much broader perspective by considering both structured and unstructured data sources. It allows real-time risk monitoring for financial institutions, enabling them to respond rapidly to new risks as they arise. However, the application of ML into financial risk assessment has not been without challenges, concerns such as data quality, the interpretability of complex models, over fitting in models, and bias in data that are required to obtain reliable results, among others. Further, new evolving regulatory frameworks to keep pace with the rapid progress of ML techniques have presented additional concentration in assessment and compliance with the model as well as validation. This paper recommends that the hybrid models that the paper envisions are the future of financial risk assessment as hybrid models have the best of traditional financial theories such as Value at Risk and stress testing with the advanced techniques of machine learning. Such hybrid systems can provide a more comprehensive, accurate, and adaptive framework for financial risk management which is beneficial both for financial institutions and regulatory bodies in an increasingly volatile global economy. Additionally, the continued development of Explainable AI (XAI) techniques is expected to improve model transparency and encourage stakeholder trust along the same lines.},
      month = {April},
  }