چکیده مقاله
The landscape of financial risk management is undergoing a profound transformation, driven by the advent of Machine Learning ML methodologies, marking a distinct paradigm shift from conventional statistical and econometric approaches Historically, financial institutions relied heavily on rule based systems, linear models, and expert judgment, which, while foundational, often struggled with the escalating complexity, high dimensionality, and sheer volume of modern financial data The ML revolution heralds a new era, empowering risk professionals with sophisticated algorithms capable of discerning intricate non linear relationships, identifying subtle anomalies, and generating highly granular predictive insights across diverse risk categories This shift enables a proactive rather than reactive posture, fundamentally altering the assessment, measurement, and mitigation of credit risk, market risk, operational risk, and systemic risk By harnessing advanced computational power and iterative learning capabilities, ML models offer unprecedented precision in forecasting defaults, anticipating market volatility, detecting fraudulent activities, and optimizing capital allocation, thereby enhancing the overall resilience and stability of financial systems This predictive paradigm leverages a spectrum of ML techniques, from supervised learning algorithms like Gradient Boosting Machines and Deep Neural Networks for credit scoring and fraud detection, to unsupervised methods for anomaly detection in operational risk, and reinforcement learning for dynamic portfolio optimization The tangible benefits extend beyond mere accuracy, encompassing enhanced efficiency through automation, more robust stress testing capabilities that absorb intricate scenarios, and the potential for real time risk monitoring, which is critical in fast paced financial markets However, the integration of ML also introduces novel challenges that necessitate careful consideration Key among these are issues of model interpretability and explainability XAI , addressing inherent biases in historical data, ensuring regulatory compliance in an evolving landscape, and managing the inherent model risk associated with complex black box algorithms Future advancements require a concerted effort towards developing transparent, robust, and ethically sound ML models, alongside fostering a skilled workforce capable of deploying and governing these sophisticated tools, ensuring that the transformative potential of ML is fully realized while safeguarding financial stability and upholding ethical standards
کلیدواژهها
نویسندگان
Masoud Karimkhani
Department of Computer Science, Central Tehran Branch , Islamic Azad University, Tehran, Iran
Suresh Aluvihara
Department of Chemical and Process Engineering, University of Peradeniya, Peradeniya, Sri Lanka
Roohollah Arabzadeh Ghahyazi
Department of Management, South Tehran Branch, Islamic Azad University, Tehran, Iran
Mohammad Reza Radfar
Department of Financial Management and accounting, S,T C Islamic Azad University, Tehran, Iran
Hossein Abyar
Department of Financial Management and accounting, Central Tehran Branch , Islamic Azad University, Tehran, Iran
Mohammad Karimkhani
Department of Clinical Psychology, Zanjan Branch , Islamic Azad University, Zanjan, Iran
شیوه ارجاع
Karimkhani, Masoud and Aluvihara, Suresh and Arabzadeh ghahyazi, Roohollah and Radfar, Mohammad Reza and Abyar, Hossein and Karimkhani, Mohammad,1404,The Machine Learning (ML) Revolution in Financial Risk Management from Traditional Methods to a Predictive Paradigm: A Review,Second National Conference on A World without Oil,Tehran
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