AI-Driven Database Systems in FinTech: Enhancing Fraud Detection and Transaction Efficiency

Authors

  • Deekshith Narsina Programmer Analyst, Capital One, 1600 Capital One Dr, Mclean, VA- 22102, USA
  • Jaya Chandra Srikanth Gummadi Programmer Analyst, Pioneer Global Inc., Ashburn, Virginia, USA
  • Satya Surya MKLG Gudimetla Naga Venkata IAM Engineer, HCL Global Systems INC, Farmington Hills, Michigan- 48335, USA
  • Aditya Manikyala Java Developer, Dynamic Technology Inc.,  4335 Premier Plaza, Ashburn, VA 20147, USA
  • Srinikhita Kothapalli Software Engineer, UPS, 825 Lotus Ave, Louisville, Kentucky- 40213, USA
  • Krishna Devarapu Senior Software Data Engineer, HP Inc., 10300 Energy Dr, Spring, TX 77389, USA
  • Marcus Rodriguez Princeton Institute for Computational Science and Engineering (PICSciE), Princeton University, NJ, USA
  • Rajasekhar Reddy Talla SAP SD OTC Functional Consultant, McKesson, Texas, USA

Keywords:

Artificial Intelligence (AI), Database Systems, FinTech, Fraud Detection, Transaction Efficiency, Machine Learning, Deep Learning, Real-Time Processing, Predictive Analytics, Automation

Abstract

This research examines how AI-driven database systems change FinTech fraud detection and transaction efficiency. The main goals are to discuss how machine learning, deep learning, and natural language processing improve fraud detection and transaction processes. The research synthesizes existing literature and industry reports to assess financial services AI integration performance via secondary data evaluation. Significant results show that AI improves fraud detection by identifying complex patterns, reacting to new risks, and increasing transaction efficiency via automation, intelligent routing, and real-time optimization. These innovations speed up transaction processing, save operating expenses, and minimize fraud losses. The research also finds data reliance and model biases, which need robust regulatory frameworks. Policy implications stress openness in AI decision-making, bias checks, and security measures to prevent adversarial assaults. Addressing these difficulties allows financial institutions to realize the advantages of AI-driven technologies, creating a more secure and efficient FinTech ecosystem that satisfies digital economy expectations.

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Published

2019-12-31

How to Cite

Narsina, D., Gummadi, J. C. S., Venkata, S. S. M. G. N., Manikyala, A., Kothapalli, S., Devarapu, K., Rodriguez, M., & Talla, R. R. (2019). AI-Driven Database Systems in FinTech: Enhancing Fraud Detection and Transaction Efficiency. Asian Accounting and Auditing Advancement, 10(1), 81–92. Retrieved from https://4ajournal.com/article/view/98

Issue

Vol. 10 No. 1 (2019): January - December

Section

Articles

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Copyright (c) 2019 Asian Accounting and Auditing Advancement

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This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.

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