Human-Centered Explainable Ai for Improving Trust in Automated Financial Decision-Making Systems

  • Authors

    • Muthukumaran Research Scholar, India. Author

    DOI:

    https://doi.org/10.67228/30715628/IJMIET-V9I3P103

    Published 07-12-2026

  • Explainable Artificial Intelligence, Human-Cantered AI, Financial Decision-Making, Algorithmic Transparency, Trust In AI, Credit Scoring, Algorithmic Fairness, Responsible AI, Counterfactual Explanations, Financial Technology

    Issue

    Vol. 9 No. 3 (2026)

    Section

    Articles

  • Abstract

    This article proposes a human-cantered Explainable Artificial Intelligence (XAI) framework to improve trust in AI-driven financial decision-making systems, including credit scoring, loan approvals, fraud detection, and risk assessment. Although AI enhances efficiency and predictive accuracy, its lack of transparency raises concerns about fairness, accountability, and user confidence. The proposed framework integrates interpretable machine learning, explanation quality assessment, fairness evaluation, human oversight, and continuous monitoring. It examines various explanation methods and emphasizes that explanations should be understandable, accurate, fair, and relevant to affected individuals. The study provides a conceptual foundation for responsible AI governance in financial services, with further empirical research required to validate the framework’s effectiveness.

  • References

    [1] Barocas, S., & Selbst, A. D. (2016). Big data's disparate impact. California Law Review, 104(3), 671–732. https://doi.org/10.15779/Z38BG31

    [2] Obannagari, C. K. R. N., & Nangi, P. R. (2021). Predictive Database Performance Optimization Using Machine Learning-Driven Query Workload Modeling. International Journal of AI, BigData, Computational and Management Studies, 2(4), 107-116.

    [3] Mullapudi, P. N. (2025). Pricing optimization across domains: A comparative review. International Journal of Emerging Research in Engineering and Technology, 6(3), 16-33.

    [4] Y. Syed, “Enterprise Observability and Software Delivery Performance: From Telemetry to Engineering Intelligence”, IJADSMC, vol. 5, no. 2, pp. 01–08, Sep. 2022, doi: 10.67228/30713498/IJADSMC-2022PIIKP2M.

    [5] Nangi, P. R. (2022). Multi-Cloud Resource Stability Forecasting Using Temporal Fusion Transformers. International Journal of Artificial Intelligence, Data Science, and Machine Learning, 3(3), 123-135.

    [6] Doshi-Velez, F., & Kim, B. (2017). Towards a rigorous science of interpretable machine learning. arXiv. https://arxiv.org/abs/1702.08608

    [7] Fung, W., Guo, L., Hilliard, D., Casadei, A., Ratan, R., Bhaduri, S., ... & Rakshit, A. (2025). OpComm: A Reinforcement Learning Framework for Adaptive Buffer Control in Warehouse Volume Forecasting. arXiv preprint arXiv:2512.19738.

    [8] Nangi, P. R., & Obannagari, C. K. R. N. (2026). Federated ai-driven query optimization for distributed cloud databases. International Journal of Artificial Intelligence, Data Science, and Machine Learning, 7(1), 91-99.

    [9] Syed, Y. (2024). DevSecOps and Enterprise-Scale CI/CD Pipeline Engineering: Governing Delivery Velocity, Embedded Security, and Release Automation Across Polyglot Environments. International Journal of Scientific Research in Science, Engineering and Technology, 11(5), 475-484. DOI: https://doi.org/10.32628/IJSRSET2513156

    [10] Glikson, E., & Woolley, A. W. (2020). Human trust in artificial intelligence: Review of empirical research. Academy of Management Annals, 14(2), 627–660. https://doi.org/10.5465/annals.2018.0057

    [11] Nangi, P. R., & Obannagari, R. N. CK, & Settipi, S.(2022). Predictive SQL Query Tuning Using Sequence Modeling of Query Plans for Performance Optimization. International Journal of AI, BigData, Computational and Management Studies, 3(2), 104-113.

    [12] Lee, J. D., & See, K. A. (2004). Trust in automation: Designing for appropriate reliance. Human Factors, 46(1), 50–80. https://doi.org/10.1518/hfes.46.1.50_30392

    [13] Obannagari, C. K. R. N., & Nangi, P. R. (2021). Predictive Autoscaling for Kubernetes Using Multivariate Time-Series Forecasting. International Journal of Emerging Research in Engineering and Technology, 2(2), 73-83.

    [14] Sunkara, R. (2023). Cost-Optimized Energy Compliance Testing for Smart TV Streaming Devices: Achieving Milliwatt-Precision Power Measurement at Sub-One-Thousand-Dollar per Setup. American International Journal of Computer Science and Technology, 5(6), 54-59. https://doi.org/10.63282/3117-5481/AIJCST-V5I6P105

    [15] Nangi, P. R., & Obannagari, C. K. R. N. (2026). LLM-Driven Database Administration and Natural Language Query Interfaces. International Journal of Artificial Intelligence, Data Science, and Machine Learning, 7(1), 410-418.

    [16] Syed, Y. (2025). Generative AI Integration and RAG-Based Enterprise Tooling: Architectures, Techniques, and Organizational Impact. International Journal of Scientific Research in Science, Engineering and Technology, 12(6), 600-606. DOI: https://doi.org/10.32628/IJSRSET2613255

    [17] Lundberg, S. M., & Lee, S.-I. (2017). A unified approach to interpreting model predictions. In Advances in Neural Information Processing Systems 30 (pp. 4765–4774). Curran Associates, Inc. https://papers.nips.cc/paper/7062-a-unified-approach-to-interpreting-model-predictions

    [18] Nangi, P. R., Obannagari, C. K. R. N., & Settipi, S. (2022). Enhanced Serverless Micro-Reactivity Model for High-Velocity Event Streams within Scalable Cloud-Native Architectures. International Journal of Emerging Research in Engineering and Technology, 3(3), 127-135.

    [19] Pavan Nithin Mullapudi (2024). An Adaptive Explainability Framework for Machine Learning Predictions of Deals in Cloud Computing . International Journal of Multidisciplinary Research and Growth Evaluation (IJMRGE), 5(3), 1027-1034. DOI: https://doi.org/10.54660/.IJMRGE.2024.5.3.1027-1034

    [20] Nangi, P. R., Obannagari, C. K. R. N., & Settipi, S. (2025). AI-Driven Data Engineering Pipelines for High-Velocity Analytics Using Serverless Architectures and Event-Stream Intelligence. International Journal of AI, BigData, Computational and Management Studies, 6(3), 74-83.

    [21] National Institute of Standards and Technology. (2023). Artificial intelligence risk management framework (AI RMF 1.0) (NIST AI 100-1). U.S. Department of Commerce. https://doi.org/10.6028/NIST.AI.100-1

    [22] Nangi, P. R., Obannagari, C. K. R. N., & Settipi, S. (2022). Self-auditing deep learning pipelines for automated compliance validation with explainability, traceability, and regulatory assurance. International Journal of Artificial Intelligence, Data Science, and Machine Learning, 3(1), 133-142.

    [23] Ribeiro, M. T., Singh, S., & Guestrin, C. (2016). “Why should I trust you?” Explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (pp. 1135–1144). Association for Computing Machinery. https://doi.org/10.1145/2939672.2939778

    [24] Nangi, P. R., Obannagari, C. K. R. N., & Settipi, S. (2025). Quantum-Enhanced Optimization Models for Large-Scale Security Policy Evaluation in Distributed Cloud-Native Systems. American International Journal of Computer Science and Technology, 7(6), 46-56.

    [25] Syed, Y. (2023). Polyglot Persistence and Enterprise Data Migration: Architectures, Consistency, and Practice. American International Journal of Computer Science and Technology, 5(3), 61-66. https://doi.org/10.63282/3117-5481/AIJCST-V5I3P106

    [26] Wachter, S., Mittelstadt, B., & Russell, C. (2017). Counterfactual explanations without opening the black box: Automated decisions and the GDPR. Harvard Journal of Law & Technology, 31(2), 841–887. https://jolt.law.harvard.edu/assets/articlePDFs/v31/Counterfactual-Explanations-without-Opening-the-Black-Box-Sandra-Wachter-et-al.pdf

    [27] Nangi, P. R., & Nala Obannagari, C. K. R. (2026). Quantum-Classical Hybrid Algorithms for Complex Query Planning: A Scalable Computational Framework for High-Dimensional Decision Optimization in Heterogeneous Data Ecosystems. International Journal of AI, BigData, Computational and Management Studies, 7(2), 328-336. https://doi.org/10.63282/3050-9416.IJAIBDCMS-V7I2P143

    [28] Virendra Jangid. (2025). AI-Powered Cross-Border Securities Processing and ADR Platform Optimization. International Journal of Leading Research Publication, 6(11). https://doi.org/10.70528/IJLRP.v6.i11.2233

    [29] Nangi, P. R., & Obannagari, C. K. R. N. (2024). High-Performance Distributed Database Partitioning Using Machine Learning-Driven Workload Forecasting and Query Optimization. American International Journal of Computer Science and Technology, 6(2), 11-21.

    [30] Gantikota, S. (2023). Integrating SonarQube and IBM AppScan into Enterprise CI/CD Pipelines: A Vulnerability Mitigation Framework Achieving Over Eighty Percent Risk Reduction. International Journal of Emerging Trends in Computer Science and Information Technology, 4(3), 240-244. https://doi.org/10.63282/3050-9246.IJETCSIT-V4I3P124

    [31] Brahmandam, L. M. K. (2024). An Empirical Evaluation of the Medallion Architecture on Databricks and Apache Spark with Snowflake: Throughput, Latency, and Cost for Batch and Real-Time Ingestion Patterns. International Journal of AI, BigData, Computational and Management Studies, 5(3), 197-206. https://doi.org/10.63282/3050-9416.IJAIBDCMS-V5I3P122

    [32] Nangi, P. R., Obannagari, C. K. R. N., & Settipi, S. (2025). Large Language Model–Driven Intelligent Observability Frameworks for Serverless Applications in Event-Driven Cloud Architectures. International Journal of Emerging Research in Engineering and Technology, 6(1), 100-110.

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