Articles Open Access
Financial Risk Assessment and Prediction Using Machine Learning and Big Data Technologies
Senior Technical Program Manager, USA
Abstract
Financial risk assessment and prediction have become critical components in modern banking, investment management, insurance, and financial decision-making systems. The increasing volume of digital financial transactions and market complexity has made traditional risk analysis techniques insufficient for identifying emerging financial threats and uncertainties. Machine learning and big data technologies provide intelligent, scalable, and data-driven solutions for improving financial risk management processes. This study examines the application of machine learning algorithms and big data analytics in predicting and assessing financial risks across various financial sectors. The proposed framework utilizes big data technologies to process large-scale structured and unstructured financial datasets while employing machine learning models such as Logistic Regression, Decision Trees, Random Forest, Support Vector Machines, and Deep Learning techniques for predictive analysis. Big data platforms enhance real-time data collection, storage, processing, and analytical efficiency, enabling financial institutions to make informed decisions. The research also discusses data preprocessing, feature engineering, predictive modeling, and risk evaluation methods used in intelligent financial systems. Furthermore, the study highlights the advantages and challenges associated with machine learning-based financial risk assessment, including scalability, accuracy, data privacy, computational complexity, and interpretability issues. The findings indicate that integrating machine learning with big data technologies significantly improves financial risk prediction accuracy and supports effective financial decision-making.
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Publication Information
| Journal | International Research Journal of Innovative Engineering |
|---|---|
| Volume | 2 |
| Number | 6 |
| Pages | 2891-2899 |
| Published | 2018-11-05 |
| Issue | Vol. 2 No. 6 (2018): International Research Journal of Innovative Engineering (IRJIE) |
References
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35. Mathew, A. R. (2017). Cloud Technology and the Challenges for Forensics Investigators. DEStech Transactions on Computer Science and Engineering.
36. Begum, R. S., & Sugumar, R. (2015). A Conceptual Comparison of Artificial Bee Colony and Particle Swarm Optimization.
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39. Sugumar, R., Rengarajan, A., & Jayakumar, C. (2018). Trust based authentication technique for cluster based vehicular ad hoc networks (VANET). Wireless Networks, 24(2), 373-382.
40. Sudhan, S. K. H. H., & Kumar, S. S. (2015). An innovative proposal for secure cloud authentication using encrypted biometric authentication scheme. Indian Journal of Science and Technology, 8(35), 1-5.
41. Priya, P. S., & Sugumar, R. (2014). Multi Keyword Searching Techniques over Encrypted Cloud Data. In IJSR.
42. Chiranjeevi, K. G., Latha, R., & Kumar, S. S. (2016). Enlarge Storing Concept in an Efficient Handoff Allocation during Travel by Time Based Algorithm. Indian Journal of Science and Technology, 9, 40.
43. Amutha, M., & Sugumar, R. (2015). A survey on dynamic data replication system in cloud computing. International Journal of Innovative Research in Science, Engineering and Technology, 4(4), 1454-1467.
44. Sugumar, R. (2014). A technique to stock market prediction using fuzzy clustering and artificial neural networks.
45. Brijet, Z., Kumar, B. S., & Bharathi, N. (2017). Vehicle anti-theft system using fingerprint recognition technique. Open Academic Journal of Advanced Science and Technology, 1(1), 36-41.
46. Z. Brijet, N. Bharathi, G. Shanmugapriya. (2015). Design of Model Predictive Controller for Fluid Catalytic Cracking Unit. Fluid-IJCTA, 8(5), 1917-1926.
2. Bahnsen, A. C., Aouada, D., & Ottersten, B. (2015). Example-dependent cost-sensitive decision trees for financial risk prediction. Expert Systems with Applications, 42(19), 6609–6619.
3. Barboza, F., Kimura, H., & Altman, E. (2017). Machine learning models and bankruptcy prediction. Expert Systems with Applications, 83, 405–417.
4. Bhattacharyya, S., Jha, S., Tharakunnel, K., & Westland, J. C. (2011). Data mining for financial fraud detection. Decision Support Systems, 50(3), 602–613.
5. Chen, H., Chiang, R., & Storey, V. (2012). Business intelligence and analytics: From big data to big impact. MIS Quarterly, 36(4), 1165–1188.
6. Dhar, V. (2013). Data science and prediction. Communications of the ACM, 56(12), 64–73.
7. Fan, J., Han, F., & Liu, H. (2014). Challenges of big data analysis. National Science Review, 1(2), 293–314.
8. Gandomi, A., & Haider, M. (2015). Beyond the hype: Big data concepts and analytics. International Journal of Information Management, 35(2), 137–144.
9. Hand, D. J., & Henley, W. E. (2010). Statistical classification methods in consumer credit scoring. Journal of the Royal Statistical Society, 160(3), 523–541.
10. Huang, C. L., Chen, M. C., & Wang, C. J. (2011). Credit scoring with a data mining approach. Expert Systems with Applications, 33(4), 847–856.
11. Khandani, A. E., Kim, A. J., & Lo, A. W. (2010). Consumer credit-risk models via machine-learning algorithms. Journal of Banking & Finance, 34(11), 2767–2787.
12. Kotu, V., & Deshpande, B. (2015). Predictive analytics and data mining. Morgan Kaufmann.
13. Lessmann, S., Baesens, B., Seow, H. V., & Thomas, L. (2015). Benchmarking state-of-the-art classification algorithms for credit scoring. European Journal of Operational Research, 247(1), 124–136.
14. Ngai, E. W. T., Hu, Y., Wong, Y. H., et al. (2011). Data mining techniques in financial fraud detection. Decision Support Systems, 50(3), 559–569.
15. Provost, F., & Fawcett, T. (2013). Data science for business. O’Reilly Media.
16. Raghupathi, W., & Raghupathi, V. (2014). Big data analytics in financial services. Health Information Science and Systems, 2(3), 1–10.
17. Tsai, C. F., & Chen, M. L. (2010). Credit rating by hybrid machine learning techniques. Applied Soft Computing, 10(2), 374–380.
18. Wang, Y., Kung, L., & Byrd, T. (2016). Big data analytics: Understanding its capabilities and potential benefits. Information & Management, 53(1), 13–26.
19. West, D. (2013). Neural network credit scoring models. Computers & Operations Research, 27(11–12), 1131–1152.
20. Zhou, L., Lai, K. K., & Yen, J. (2012). Credit scoring models with data mining techniques. International Journal of Information Technology & Decision Making, 8(4), 647–674.
21. Mathew, A., & Asari, V. K. (2014, March). Rotation-invariant histogram features for threat object detection on pipeline right-of-way. In Video Surveillance and Transportation Imaging Applications 2014 (Vol. 9026, pp. 19-26). SPIE.
22. Sugumar, R., Rengarajan, A., & Jayakumar, C. (2015). Design a Weight Based Sorting Distortion Algorithm for Privacy Preserving Data Mining. Middle-East Journal of Scientific Research, 23(3), 405-412.
23. Karthika, V., Brijet, Z., & Bharathi, N. (2012). Design of optimal controller for fluid catalytic cracking unit. Procedia Engineering, 38, 1150-1160.
24. Amutha, M., & Sugumar, R. (2015). A survey on dynamic data replication system in cloud computing. International Journal of Innovative Research in Science, Engineering and Technology, 4(4), 1454-1467.
25. Sugumar, R. B., & Anandharaj, K. (2016). Assessment of Bacterial Load in the Fresh Water Lake System of Tamil Nadu. Interl J Curr Microbiol App Sci, 5(6), 236-246.
26. Rengarajan, R. S. A. (2016). Secure verification technique for defending IP spoofing attacks.
27. Mathew, A. R., & Al Hajj, A. (2017). Secure communications on IoT and big data. Indian Journal of Science and Technology, 10(11).
28. Alex, A. T., Asari, V. K., & Mathew, A. (2012, October). Gradient feature matching for expression invariant face recognition using single reference image. In 2012 IEEE International Conference on Systems, Man, and Cybernetics (SMC) (pp. 851-856). IEEE.
29. Sasidevi, J., Sugumar, R., & Priya, P. S. (2015). New-Multi-Phase Distribution Network Intrusion Detection. International Journal, 5(3).
30. Mathew, A., & Asari, V. K. (2013, March). Tracking small targets in wide area motion imagery data. In Video Surveillance and Transportation Imaging Applications (Vol. 8663, pp. 69-78). SPIE.
31. Sudhan, S. K. H. H., & Kumar, S. S. (2016). Gallant Use of Cloud by a Novel Framework of Encrypted Biometric Authentication and Multi Level Data Protection. Indian Journal of Science and Technology, 9, 44.
32. Alex, A. T., Asari, V. K., & Mathew, A. (2013, March). Gradient feature matching for in-plane rotation invariant face sketch recognition. In Image Processing: Machine Vision Applications VI (Vol. 8661, pp. 46-58). SPIE.
33. Natarajan, R., & Sugumar, R. (2014). A survey on attacks in web usage mining. International Journal of Innovative Research in Computer and Communication Engineering, 2(5), 4470-5.
34. Satyanarayana, D., Mathew, A. R., & Sathyashree, S. (2016). An Architecture for Wireless Communication Systems using Li-Fi technology. In 8th International Conference on Latest Trends in Engineering and Technology (ICLTET’2016) (pp. 37-41).
35. Mathew, A. R. (2017). Cloud Technology and the Challenges for Forensics Investigators. DEStech Transactions on Computer Science and Engineering.
36. Begum, R. S., & Sugumar, R. (2015). A Conceptual Comparison of Artificial Bee Colony and Particle Swarm Optimization.
37. Soundappan, S. J., & Sugumar, R. (2016). Optimal knowledge extraction technique based on hybridisation of improved artificial bee colony algorithm and cuckoo search algorithm. International Journal of Business Intelligence and Data Mining, 11(4), 338-356.
38. Singh, T. J., & Sugumar, R. (2012, December). An extensibility of THEMIS billing system for the cloud computing environment. In 2012 Fourth International Conference on Advanced Computing (ICoAC) (pp. 1-6). IEEE.
39. Sugumar, R., Rengarajan, A., & Jayakumar, C. (2018). Trust based authentication technique for cluster based vehicular ad hoc networks (VANET). Wireless Networks, 24(2), 373-382.
40. Sudhan, S. K. H. H., & Kumar, S. S. (2015). An innovative proposal for secure cloud authentication using encrypted biometric authentication scheme. Indian Journal of Science and Technology, 8(35), 1-5.
41. Priya, P. S., & Sugumar, R. (2014). Multi Keyword Searching Techniques over Encrypted Cloud Data. In IJSR.
42. Chiranjeevi, K. G., Latha, R., & Kumar, S. S. (2016). Enlarge Storing Concept in an Efficient Handoff Allocation during Travel by Time Based Algorithm. Indian Journal of Science and Technology, 9, 40.
43. Amutha, M., & Sugumar, R. (2015). A survey on dynamic data replication system in cloud computing. International Journal of Innovative Research in Science, Engineering and Technology, 4(4), 1454-1467.
44. Sugumar, R. (2014). A technique to stock market prediction using fuzzy clustering and artificial neural networks.
45. Brijet, Z., Kumar, B. S., & Bharathi, N. (2017). Vehicle anti-theft system using fingerprint recognition technique. Open Academic Journal of Advanced Science and Technology, 1(1), 36-41.
46. Z. Brijet, N. Bharathi, G. Shanmugapriya. (2015). Design of Model Predictive Controller for Fluid Catalytic Cracking Unit. Fluid-IJCTA, 8(5), 1917-1926.
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