2022· International Journal of Applied Data Science & Modern Computing· Vol 5, pp. 01-15· 0 citations
TL;DR
Experimental insights show that models like random forests, gradient boosting, SVMs, and neural networks provide better predictive accuracy and early warning capabilities compared to traditional methods, but challenges related to data quality, interpretability, and ethical concerns remain.
Abstract
Financial risk assessment is essential in handling uncertainties and complexities in modern financial systems. Traditional statistical models often struggle with nonlinear relationships and dynamic market conditions. This paper explores the application of data science techniques—such as machine learning, deep learning, and big data analytics—in evaluating various financial risks, including credit, market, operational, and systemic risks. The proposed framework integrates data preprocessing, feature engineering, predictive modeling, and explainability to enhance decision-making and regulatory compliance. Experimental insights show that models like random forests, gradient boosting, SVMs, and neural networks provide better predictive accuracy and early warning capabilities compared to traditional methods. However, challenges related to data quality, interpretability, and ethical concerns remain. Overall, data science is identified as a transformative approach to financial risk assessment when supported by proper governance and validation practices.
The paper shows that ensemble learning models have superior predictive power and argues that behavioral data complement traditional datasets for underbanked populations, such as "credit invisibles," and makes a strong case for XAI being essential for model transparency, combating bias, and meeting regulatory requirements.
Bojun Chen· Advances in Economics, Manag...· 0 citations
The banking sector requires accurate risk assessment to maintain financial stability and reduce losses. Traditional risk assessment methods rely on historical data, credit scores, and statistical techniques but often struggle with large-scale data, complex patterns, and real-time decision-making. Advances in Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning (DL) have introduced intelligent solutions for evaluating credit, fraud, operational, market, and liquidity risks. AI-based models analyze vast amounts of structured and unstructured financial data to identify hidden patterns and generate predictive insights. Techniques such as Logistic Regression, Decision Trees, Random Forests, Support Vector Machines, Gradient Boosting, Neural Networks, and Deep Learning models improve risk prediction accuracy and fraud detection. This study proposes an AI-driven risk assessment framework comprising data collection, preprocessing, feature extraction, model training, risk prediction, and decision support. Performance evaluation using metrics such as accuracy, precision, recall, F1-score, and AUC shows that AI models outperform traditional approaches by enhancing prediction capability and reducing manual intervention. Despite challenges related to data privacy, interpretability, regulatory compliance, and ethics, AI-based risk assessment significantly strengthens modern banking risk management and supports sustainable financial operations.
Anita Verma· International Journal of Com...· 0 citations
With the growth in complexity and unpredictability of the business environment, early warning techniques of financial risks are faced with the following issues: slowness in recognition and lack of accuracy to process multi-source data, dynamically responsive, and description of nonlinear relationships. In a bid to solve these problems, this paper proposes the concept of big data analytics and machine learning to develop a combined model of financial risk early warning and control. This model manages to use a single data system created through the combination of heterogeneous data that is provided by many sources, a model based on the XGBoost approach toward adaptive screening of primary risk factors and modeling of the non-linear relationships, and combining time series analysis and dynamical threshold approaches to describe the risk evolution and dynamically modify early warning boundaries. Experimental findings indicate that the model has an accuracy of 0.913, precision of 0.901 and recall of 0.887 in financial risk identification, which is much better than the traditional methods. False alarm rate reduced to 0.182 to 0.098, false negative rate reduced to 0.214 to 0.121, lead time increased to 2.4 periods (as opposed to 1.2 periods) and the overall reduction rate in risk was 26.7%. It can be of great benefit in enhancing accuracy of identification and timeliness of early warning and can also offer technical support in corporate financial risk management.
It is suggested that superior ranking performance does not necessarily imply superior decision quality and that effective credit risk modeling requires balancing predictive flexibility with probabilistic reliability and governance stability.
The findings indicate that machine learning effectively compensates for the defects of traditional methods, yet faces common challenges including weak model interpretability, high dependence on high-quality data, and low cross-market adaptability.
Artificial Intelligence (AI) is quickly taking center stage in modern financial decision-making. The capacity of financial institutions and investors to analyze vast and intricate data sets has increased thanks to developments in machine learning, deep learning, natural language processing, predictive analytics, and generative artificial intelligence. Conventional methods for investment analysis, credit evaluation, fraud detection, financial forecasting, risk assessment, and portfolio management have been altered by the growing use of AI. This study looks at the expanding use of AI in financial decision-making and assesses both its possible advantages and the difficulties in implementing it. While taking into account issues with data quality, algorithmic bias, privacy, cybersecurity, explainability, model risk, and an over-reliance on automated systems, the study focuses on how AI can enhance the speed, consistency, analytical depth, and efficiency of financial decisions. Using contemporary scholarly and institutional literature on AI and finance, a descriptive and analytical research technique is used. According to the investigation, AI may greatly improve financial decision-support by digesting data quickly and seeing connections that conventional analytical techniques can miss. However, data quality, model architecture, governance, and human oversight all have a significant impact on how successful AI is. Increasing the use of AI can potentially lead to new vulnerabilities in the financial industry, especially through third-party concentration, cyber risks, market correlations, and model risk, according to recent international data. The study comes to the conclusion that rather than completely replacing human judgment, AI should be included into finance largely as an enhancement of human competence. Strong governance, open decision-making procedures, trustworthy data, ongoing model review, and significant human monitoring are all necessary for responsible deployment. The study offers a theoretical framework for comprehending how financial institutions might profit from AI while managing the dangers related to technology, ethics, and finances.
Shalu, Garima, Bhumika, Dr. Bhawana· International Journal of Adv...· 0 citations