This research focuses on using approaches such as ML and ANNs in FRM while will look at and try to analyze their effectiveness compared to logistic regression, random forest, and support vector machine. Training and testing of the models were done using accuracy, precision, recall and F1-score with a sample database comprising of 15000 financial records. Imputation of missing values; selection of informative variables; and data scaling, were performed to enhance the reliability of the models used. Analysis of the results revealed that ANNs and more so DNNs surpassed conventional approaches in the prediction of financial risks. Still, the integration of traditional and AI-based approaches resulted in improved performance outcomes as well as proved to be more resilient to multiple risk factors. Thus, the work concludes that the enhancement of the integration of AI in the management of financial risk can enhance the accuracy of risk assessment. The future work should include improvements regarding the interpretability of the model, testing on a more substantial number of data and experimenting with reinforcement learning to apply it to decision making in the financial risk cases.

zenodo DOI:- https://doi.org/10.5281/zenodo.13934871

Artificial Neural Network, Machine Learning Algorithms, Financial Risk Management

R. S. Kaplan and A. Mikes, “Risk Management-the Revealing Hand,” Journal of Applied Corporate Finance, vol. 28, no. 1, pp. 8–18, Mar. 2016, doi: https://doi.org/10.1111/jacf.12155.

A. Deiva Ganesh and P. Kalpana, “Future of artificial intelligence and its influence on supply chain risk management – A systematic review,” Computers & Industrial Engineering, vol. 169, no. 169, p. 108206, Jul. 2022.

A. J. Pitman et al., “Acute climate risks in the financial system: examining the utility of climate model projections,” Environmental Research: Climate, vol. 1, no. 2, p. 025002, Aug. 2022, doi: https://doi.org/10.1088/2752-5295/ac856f.

H. Benbya, T. H. Davenport, and S. Pachidi, “Artificial Intelligence in Organizations: Current State and Future Opportunities,” papers.ssrn.com, Dec. 03, 2020.

https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3741983.

T. Lynn, J. Mooney, P. Rosati, and M. Cummins, “PALGRAVE STUDIES IN DIGITAL BUSINESS AND ENABLING TECHNOLOGIES SERIES EDITORS: Disrupting Finance FinTech and Strategy in the 21st Century Edited by,” 2019. Available: https://library.oapen.org/bitstream/handle/20.500.12657/23126/1007030.pdf?sequence=1#page=54

R. Y. Choi, A. S. Coyner, J. Kalpathy-Cramer, M. F. Chiang, and J. P. Campbell, “Introduction to Machine Learning, Neural Networks, and Deep Learning,” Translational Vision Science & Technology, vol. 9, no. 2, pp. 14–14, Jan. 2020, doi: https://doi.org/10.1167/tvst.9.2.14.

S. Bhatore, L. Mohan, and Y. R. Reddy, “Machine Learning Techniques for Credit Risk evaluation: a Systematic Literature Review,” Journal of Banking and Financial Technology, vol. 4, no. 1, pp. 111–138, Apr. 2020, doi: https://doi.org/10.1007/s42786-020-00020-3.

T. Adrian, F. Grinberg, N. Liang, S. Malik, and J. Yu, “The Term Structure of Growth-at-Risk,” American Economic Journal: Macroeconomics, vol. 14, no. 3, pp. 283–323, Jul. 2022, doi: https://doi.org/10.1257/mac.20180428.

G. Baryannis, S. Validi, S. Dani, and G. Antoniou, “Supply Chain Risk Management and Artificial intelligence: State of the Art and Future Research Directions,” International Journal of Production Research, vol. 57, no. 7, pp. 1–24, Oct. 2019, doi: https://doi.org/10.1080/00207543.2018.1530476.

J. Dou et al., “Improved landslide assessment using support vector machine with bagging, boosting, and stacking ensemble machine learning framework in a mountainous watershed, Japan,” Landslides, vol. 17, no. 3, pp. 641–658, Oct. 2019, doi: https://doi.org/10.1007/s10346-019-01286-5.

Y. Xia, X. Guo, Y. Li, L. He, and X. Chen, “Deep learning meets decision trees: An application of a heterogeneous deep forest approach in credit scoring for online consumer lending,” vol. 41, no. 8, pp. 1669–1690, Jul. 2022, doi: https://doi.org/10.1002/for.2891.

R. Sweet et al., “Credit Risk Assessment Using Statistical and Machine Learning: Basic Methodology and Risk Modeling Applications,” International Journal on Computational Engineering, vol. 1, no. 3, pp. 62–67, 2024, doi: https://doi.org/10.62527/comien.1.3.21.

L. Yang and A. Shami, “On hyperparameter optimization of machine learning algorithms: Theory and practice,” Neurocomputing, vol. 415, pp. 295–316, Nov. 2020, doi: https://doi.org/10.1016/j.neucom.2020.07.061.

A. Sanyal, Dokania, Puneet K, V. Kanade, and Philip, “How benign is benign overfitting?,” arXiv.org, 2020. https://arxiv.org/abs/2007.04028 (accessed Sep. 12, 2024).

D. Marcek, “Forecasting of financial data: a novel fuzzy logic neural network based on error-correction concept and statistics,” Complex & Intelligent Systems, vol. 4, no. 2, pp. 95–104, Sep. 2017, doi: https://doi.org/10.1007/s40747-017-0056-6.

T. Sun and M. A. Vasarhelyi, “Predicting credit card delinquencies: An application of deep neural networks,” Intelligent Systems in Accounting, Finance and Management, vol. 25, no. 4, pp. 174–189, Aug. 2018, doi: https://doi.org/10.1002/isaf.1437.

E. Dumitrescu, S. Hué, C. Hurlin, and S. Tokpavi, “Machine Learning for Credit Scoring: Improving Logistic Regression with Non-Linear Decision-Tree Effects,” European Journal of Operational Research, vol. 297, no. 3, Jun. 2021, doi: https://doi.org/10.1016/j.ejor.2021.06.053.

M. Chen, U. Challita, W. Saad, C. Yin, and M. Debbah, “Artificial Neural Networks-Based Machine Learning for Wireless Networks: A Tutorial,” IEEE Communications Surveys & Tutorials, vol. 21, no. 4, pp. 3039–3071, 2019, doi: https://doi.org/10.1109/comst.2019.2926625.

M. Selvaggi, “A new metric for the interpretability of artificial neural networks in medical diagnosis applications. - Webthesis,” Polito.it, Oct. 2022, doi: https://webthesis.biblio.polito.it/secure/24521/1/tesi.pdf.

M. Soui, I. Gasmi, S. Smiti, and K. Ghédira, “Rule-based credit risk assessment model using multi-objective evolutionary algorithms,” Expert Systems with Applications, vol. 126, pp. 144–157, Jul. 2019, doi: https://doi.org/10.1016/j.eswa.2019.01.078.

S. Marso and M. El Merouani, “Predicting financial distress using hybrid feedforward neural network with cuckoo search algorithm,” Procedia Computer Science, vol. 170, pp. 1134–1140, 2020, doi: https://doi.org/10.1016/j.procs.2020.03.054.

M. Satheesh and S. Nagaraj, “Applications of Artificial Intelligence on Customer Experience and Service Quality of the Banking Sector,” International Management Review, vol. 17, no. 1, p. 2021, 2021, Available: http://www.americanscholarspress.us/journals/IMR/pdf/IMR-1-2021/V17n121-art2.pdf

Y. Lu, “Artificial intelligence: a survey on evolution, models, applications and future trends,” Journal of Management Analytics, vol. 6, no. 1, pp. 1–29, Jan. 2019, doi: https://doi.org/10.1080/23270012.2019.1570365.

D. van Thiel and W. F. van Raaij, “Artificial Intelligent Credit Risk Prediction: An Empirical Study of Analytical Artificial Intelligence Tools for Credit Risk Prediction in a Digital Era,” Journal of Accounting and Finance, vol. 19, no. 8, Dec. 2019, Available: https://articlearchives.co/index.php/JAF/article/view/68

M. T. Keane and E. M. Kenny, “How Case-Based Reasoning Explains Neural Networks: A Theoretical Analysis of XAI Using Post-Hoc Explanation-by-Example from a Survey of ANN-CBR Twin-Systems,” Case-Based Reasoning Research and Development, pp. 155–171, 2019, doi: https://doi.org/10.1007/978-3-030-29249-2_11.

V. Beaudouin et al., “Flexible and Context-Specific AI Explainability: A Multidisciplinary Approach,” arXiv.org, Mar. 13, 2020. https://arxiv.org/abs/2003.07703

S. Bramhall, H. Horn, M. Tieu, and N. Lohia, “QLIME-A Quadratic Local Interpretable Model-Agnostic Explanation Approach,” SMU Data Science Review, vol. 3, no. 1, Apr. 2020, Available: https://scholar.smu.edu/datasciencereview/vol3/iss1/4/

C. S. Bojer and J. P. Meldgaard, “Kaggle Forecasting competitions: an Overlooked Learning Opportunity,” International Journal of Forecasting, vol. 37, no. 2, Sep. 2020, doi: https://doi.org/10.1016/j.ijforecast.2020.07.007.

D. Chicco, L. Oneto, and E. Tavazzi, “Eleven quick tips for data cleaning and feature engineering,” PLOS Computational Biology, vol. 18, no. 12, p. e1010718, Dec. 2022, doi: https://doi.org/10.1371/journal.pcbi.1010718.

Pronaya Prosun Das and L. Wiese, “Explainability Based on Feature Importance for Better Comprehension of Machine Learning in Healthcare,” Communications in computer and information science, pp. 324–335, Jan. 2023, doi: https://doi.org/10.1007/978-3-031-42941-5_28.

M. Belgiu and L. Drăguţ, “Random forest in remote sensing: A review of applications and future directions,” ISPRS Journal of Photogrammetry and Remote Sensing, vol. 114, no. 114, pp. 24–31, Apr. 2016, doi: https://doi.org/10.1016/j.isprsjprs.2016.01.011.

G. A. Lujan-Moreno, P. R. Howard, O. G. Rojas, and D. C. Montgomery, “Design of experiments and response surface methodology to tune machine learning hyperparameters, with a random forest case-study,” Expert Systems with Applications, vol. 109, pp. 195–205, Nov. 2018, doi: https://doi.org/10.1016/j.eswa.2018.05.024.

H. Lingjun, R. Levine, Fan, Juanjuan, J. Beemer, and J. Stronach, “Random Forest as a Predictive Analytics Alternative to Regression in Institutional Research,” Practical Assessment, Research, and Evaluation, vol. 23, 2018, doi: https://doi.org/10.7275/1wpr-m024.

Y. Geng, Q. Li, G. Yang, and W. Qiu, “Logistic Regression,” pp. 99–132, Jan. 2024, doi: https://doi.org/10.1007/978-981-97-3954-7_4.

M. Mahbobi, S. Kimiagari, and M. Vasudevan, “Credit risk classification: an integrated predictive accuracy algorithm using artificial and deep neural networks,” Annals of Operations Research, Jul. 2021, doi: https://doi.org/10.1007/s10479-021-04114-z.

R. Moradi, R. Berangi, and B. Minaei, “A survey of regularization strategies for deep models,” Artificial Intelligence Review, vol. 53, no. 6, pp. 3947–3986, Dec. 2019, doi: https://doi.org/10.1007/s10462-019-09784-7.

S. Kost, O. Rheinbach, and H. Schaeben, “Using logistic regression model selection towards interpretable machine learning in mineral prospectivity modeling,” Geochemistry, p. 125826, Oct. 2021, doi: https://doi.org/10.1016/j.chemer.2021.125826.

S. Ghosh, A. Dasgupta, and A. Swetapadma, “A Study on Support Vector Machine based Linear and Non-Linear Pattern Classification,” IEEE Xplore, Feb. 01, 2019. https://ieeexplore.ieee.org/abstract/document/8908018

B. Ghaddar and J. Naoum-Sawaya, “High dimensional data classification and feature selection using support vector machines,” European Journal of Operational Research, vol. 265, no. 3, pp. 993–1004, Mar. 2018, doi: https://doi.org/10.1016/j.ejor.2017.08.040.

R. Mitchell and E. Frank, “Accelerating the XGBoost algorithm using GPU computing,” PeerJ Computer Science, vol. 3, p. e127, Jul. 2017, doi: https://doi.org/10.7717/peerj-cs.127.

N.-H. Nguyen, J. Abellán-García, S. Lee, E. Garcia-Castano, and T. P. Vo, “Efficient estimating compressive strength of ultra-high performance concrete using XGBoost model,” Journal of Building Engineering, vol. 52, p. 104302, Jul. 2022, doi: https://doi.org/10.1016/j.jobe.2022.104302.

T. Kavzoglu and A. Teke, “Advanced hyperparameter optimization for improved spatial prediction of shallow landslides using extreme gradient boosting (XGBoost),” Bulletin of Engineering Geology and the Environment, vol. 81, no. 5, Apr. 2022, doi: https://doi.org/10.1007/s10064-022-02708-w.

Z. Xu, C. Sun, T. Ji, J. H. Manton, and W. Shieh, “Feedforward and Recurrent Neural Network-Based Transfer Learning for Nonlinear Equalization in Short-Reach Optical Links,” Journal of Lightwave Technology, vol. 39, no. 2, pp. 475–480, Jan. 2021, Available: https://opg.optica.org/abstract.cfm?uri=jlt-39-2-475

S. Moldovanu, C.-D. Obreja, K. C. Biswas, and L. Moraru, “Towards Accurate Diagnosis of Skin Lesions Using Feedforward Back Propagation Neural Networks,” Diagnostics, vol. 11, no. 6, p. 936, May 2021, doi: https://doi.org/10.3390/diagnostics11060936.

F. Farhadi, Nia, Vahid Partovi, and A. Lodi, “Activation Adaptation in Neural Networks,” arXiv.org, 2019. https://arxiv.org/abs/1901.09849 (accessed Sep. 12, 2024).

V. Asghari, Y. F. Leung, and S.-C. Hsu, “Deep neural network based framework for complex correlations in engineering metrics,” Advanced Engineering Informatics, vol. 44, p. 101058, Apr. 2020, doi: https://doi.org/10.1016/j.aei.2020.101058.

I. H. Sarker, “Deep Learning: a Comprehensive Overview on Techniques, Taxonomy, Applications and Research Directions,” SN Computer Science, vol. 2, no. 6, Aug. 2021, doi: https://doi.org/10.1007/s42979-021-00815-1.