Explainable AI for Financial Decision-Making: Building Trust in Complex Models
Main Article Content
Abstract
As financial institutions increasingly adopt AI for decision-making, the need for transparency and interpretability has become critical. This paper examines the role of Explainable AI (XAI) in enhancing trust and accountability in financial systems. Techniques such as SHAP values, counterfactual explanations, and surrogate models are evaluated for their effectiveness in explaining credit scoring, fraud detection, and portfolio management algorithms. The study discusses the trade-offs between interpretability and model performance and emphasizes the importance of regulatory compliance. Practical case studies and future research directions are also presented.
Article Details
How to Cite
References
Brown, T. (2020). Artificial intelligence in healthcare: The future of medicine. Cambridge University Press.
Chandra, R., & Sharma, P. (2021). Machine learning for predictive analytics in education. Journal of Educational Technology, 18(3), 45–60. https://doi.org/10.1016/j.jedtech.2021.03.002
Davis, K. (2019). Ethical considerations in AI development. In S. Smith (Ed.), Advances in artificial intelligence research (pp. 123–145). Springer.
Dey, A., & Das, S. (2020). Generative adversarial networks: Applications and challenges. International Journal of Computer Science Research, 12(4), 67–78.
Adusumilli, S., Damancharla, H., & Metta, A. (2020). Artificial Intelligence-Driven Predictive Analytics for Educational Behavior Assessment. Transactions on Latest Trends in Artificial Intelligence, 1(1). Retrieved from https://www.ijsdcs.com/index.php/TLAI/article/view/638
Adusumilli, S., Damancharla, H., & Metta, A. (2020). Machine Learning Algorithms for Fraud Detection in Financial Transactions. International Journal of Sustainable Development in Computing Science, 2(1). Retrieved from https://www.ijsdcs.com/index.php/ijsdcs/article/view/639
Adusumilli, S., Damancharla, H., & Metta, A. (2020). Leveraging AI for Real-Time Sentiment Analysis in Social Media Networks. (2020). International Numeric Journal of Machine Learning and Robots, 4(4). https://injmr.com/index.php/fewfewf/article/view/182
AI-Powered Cybersecurity Solutions for Threat Detection and Prevention (S. B. K. Adusumilli, H. Damancharla, & A. R. Metta , Trans.). (2021). International Journal of Creative Research In Computer Technology and Design, 3(3). https://jrctd.in/index.php/IJRCTD/article/view/74
Adusumilli, S., Damancharla, H., & Metta, A. (2021). Deep Learning Techniques for Image Recognition in Autonomous Vehicles. (2021). International Meridian Journal, 3(3). https://meridianjournal.in/index.php/IMJ/article/view/94
Gartner. (2021). Top strategic technology trends for 2022. Retrieved from https://www.gartner.com
Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep learning. MIT Press.
Gupta, R., & Singh, H. (2021). Explainable AI: A roadmap for transparency. Artificial Intelligence Review, 54(2), 195–212. https://doi.org/10.1007/s10462-020-09829-3
Hinton, G., LeCun, Y., & Bengio, Y. (2018). Deep learning. Nature, 521(7553), 436–444. https://doi.org/10.1038/nature14539
IBM. (2020). AI ethics: Guiding principles for responsible AI. Retrieved from https://www.ibm.com/ethics
Adusumilli, S., Damancharla, H., & Metta, A. (2021). Integrating Machine Learning and Blockchain for Decentralized Identity Management Systems. (2021). International Journal of Machine Learning and Artificial Intelligence, 2(2). https://jmlai.in/index.php/ijmlai/article/view/46
Adusumilli, S., Damancharla, H., & Metta, A. (2022). Blockchain-Based Secure Framework for IoT Data Management. International Journal of Sustainable Development in Computing Science, 4(1). Retrieved from https://www.ijsdcs.com/index.php/ijsdcs/article/view/640
Adusumilli, S., Damancharla, H., & Metta, A. (2022). Optimizing Supply Chain Efficiency Through Blockchain and Smart Contracts. (2022). International Numeric Journal of Machine Learning and Robots, 6(6). https://injmr.com/index.php/fewfewf/article/view/183
Adusumilli, S., Damancharla, H., & Metta, A. (2023). Enhancing Data Privacy in Healthcare Systems Using Blockchain Technology. Transactions on Latest Trends in Artificial Intelligence, 4(4). Retrieved from https://www.ijsdcs.com/index.php/TLAI/article/view/637
Raghunath, V., Kunkulagunta, M., & Nadella, G. S. (2020). Optimizing SAP Data Processing with Machine Learning Algorithms in Cloud Environments. International Transactions in Artificial Intelligence, 4(4).
Raghunath, V., Kunkulagunta, M., & Nadella, G. S. (2020). Artificial Intelligence in Business Analytics: Cloud-Based Strategies for Data Processing and Integration. International Journal of Sustainable Development in Computing Science, 2(4).
Raghunath, V., Kunkulagunta, M., & Nadella, G. S. (2020). Scalable Data Processing Pipelines: The Role of AI and Cloud Computing. International Scientific Journal for Research, 2(2).
Raghunath, V., Kunkulagunta, M., & Nadella, G. S. (2021). Leveraging Cloud Computing for Efficient Data Processing in SAP Enterprise Solutions. International Journal of Machine Learning for Sustainable Development, 3(4).
Raghunath, V., Kunkulagunta, M., & Nadella, G. S. (2021). Machine Learning in SAP Workflows: A Study of Predictive Analytics and Automation. Transactions on Latest Trends in Artificial Intelligence, 2(2).
Johnson, M., & Kumar, A. (2021). Reinforcement learning for autonomous vehicles. Journal of Robotics and Automation, 15(1), 33–47.
Kaur, J., & Sharma, V. (2020). AI in education: Transforming the learning landscape. Educational Research Review, 29, 100–120. https://doi.org/10.1016/j.edurev.2020.100123
Kingma, D. P., & Welling, M. (2014). Auto-encoding variational Bayes. Proceedings of the International Conference on Machine Learning (ICML), 10, 12–18.
Koul, A., & Ganju, R. (2021). Practical natural language processing: A comprehensive guide. O’Reilly Media.
Raghunath, V., Kunkulagunta, M., & Nadella, G. S. (2021). Machine Learning Models for Optimizing SAP-Based Data Processing in Cloud Environments. International Journal of Sustainable Development in Computing Science, 3(3).
Raghunath, V., Kunkulagunta, M., & Nadella, G. S. (2022). Advanced Business Analytics Using Machine Learning and Cloud-Based Data Integration. International Scientific Journal for Research, 4(4).
Raghunath, V., Kunkulagunta, M., & Nadella, G. S. (2023). AI-Driven Business Analytics Framework for Data Integration Across Hybrid Cloud Systems. Transactions on Latest Trends in Artificial Intelligence, 4(4).
LeCun, Y., Bottou, L., Bengio, Y., & Haffner, P. (1998). Gradient-based learning applied to document recognition. Proceedings of the IEEE, 86(11), 2278–2324. https://doi.org/10.1109/5.726791
Li, X., & Zhang, Y. (2020). AI-powered adaptive learning systems: A review. Computers in Education, 90, 75–89. https://doi.org/10.1016/j.compedu.2020.103852
McCarthy, J. (2007). What is artificial intelligence? Stanford AI Lab. Retrieved from https://ai.stanford.edu
Minsky, M. (1986). The society of mind. Simon & Schuster.
National Institute of Standards and Technology (NIST). (2021). Artificial intelligence risk management framework. Retrieved from https://www.nist.gov
Silver, D., Huang, A., Maddison, C. J., Guez, A., Sifre, L., & van den Driessche, G. (2016). Mastering the game of Go with deep neural networks and tree search. Nature, 529(7587), 484–489. https://doi.org/10.1038/nature16961
Smith, P., & Jones, R. (2022). AI and climate change: Opportunities and challenges. Environmental Research Letters, 17(1), 123–135. https://doi.org/10.1088/1748-9326/ac1abc
Raghunath, V., Kunkulagunta, M., & Nadella, G. S. (2023). Integrating AI and Cloud Computing for Scalable Business Analytics in Enterprise Systems. International Journal of Sustainable Development in Computing Science, 5(3).
Raghunath, V., Kunkulagunta, M., & Nadella, G. S. (2023). Enhancing Data Integration Using AI and ML Techniques for Real-Time Analytics. International Journal of Machine Learning for Sustainable Development, 5(3).