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Abstract

This research investigates the role of agentic AI in enhancing financial decision-making within digital banking, concentrating on customer risk profiling, loan approval predictions, and the automation of treasury management. Utilizing a literature review approach, the study examines relevant sources on artificial intelligence, machine learning, credit risk, loan analysis, and banking risk management. The results reveal that agentic AI enables banks to identify transaction patterns, evaluate repayment capabilities, and estimate credit risks while offering initial recommendations for loan approvals. Furthermore, in treasury operations, agentic AI facilitates cash flow monitoring, liquidity forecasting, and agile funding management. However, the integration of this technology brings forth challenges, including data bias, information security concerns, accountability in decision-making, and the necessity for human oversight. It is crucial to view agentic AI as a tool that complements rather than replaces human analysts and management. To align its use with prudent banking practices, it is vital to enhance data governance, conduct thorough model audits, safeguard customer information, and clearly define system authority limits.

Keywords

Agentic AI Digital banking credit risk Loan approval Treasury management

How to Cite

Citation Format
Susilawati, M. (2026). Utilization of Agentic AI in Financial Decision-Making: Optimizing Risk Profiles, Predicting Loan Approvals, and Automating Treasury Management in Digital Banking. International Journal of Management Science and Information Technology, 6(1), 1014-1021. https://doi.org/10.35870/ijmsit.v6i1.7368

References

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Author Biography

Made Susilawati, Universitas Persatuan Guru 1945 NTT

Accounting Study Program, Faculty of Economics, Universitas Persatuan Guru 1945 NTT, Kupang City, East Nusa Tenggara Province, Indonesia

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Copyright (c) 2026 Made Susilawati

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