AI IN CONSUMER DIGITAL BANKING: ENABLING SMART PERSONALIZATION AND FRAUD DETECTION

Authors

  • Mutha Ravi Tej Kotla Integration/Solution Architect, USA. Author

DOI:

https://doi.org/10.15662/y6nvry65

Keywords:

Artificial Intelligence (AI), Digital Banking, Smart Personalization, Fraud Detection, Machine Learning (ML), Consumer Behavior Analytics, Financial Technology (FinTech), Behavioral Biometrics, Anomaly Detection, Customer Experience (CX), Real-Time Analytics, AI Ethics in Banking

Abstract

The rapid digital transformation of the banking sector has led to a paradigm shift in how financial institutions engage with consumers. Artificial Intelligence (AI) has emerged as a critical enabler, driving both operational efficiency and enhanced customer experiences. This paper explores the dual application of AI in consumer digital banking: smart personalization and fraud detection. On one front, AI-powered personalization tailors services, recommendations, and interfaces based on user behavior, transaction history, and preferences, thereby fostering deeper customer engagement and loyalty. On the other front, advanced AI algorithms detect fraudulent activities in real time by identifying anomalous patterns and behaviors across massive data streams—significantly reducing financial crime and loss.

This research analyzes current AI methodologies including machine learning, deep learning, and natural language processing within the context of banking applications. Through a combination of literature review, market data analysis, and illustrative case studies, we present key findings that highlight the transformative impact of AI on both personalization and fraud management. Quantitative data supports the effectiveness of AI models, demonstrating improvements such as a 30–50% increase in customer satisfaction and up to a 90% reduction in false positives in fraud alerts.

Moreover, the paper discusses the challenges of AI implementation in digital banking, including data privacy, ethical concerns, regulatory compliance, and model interpretability. The study concludes by offering strategic insights into the future of AI in banking, including the integration of federated learning, explainable AI, and generative models for more intuitive customer experiences. By synthesizing technical, operational, and ethical considerations, this paper aims to provide a comprehensive framework for financial institutions seeking to responsibly harness AI for enhanced consumer trust and digital innovation.

References

[1] McKinsey & Co., "AI Banking Survey 2024,"

[2] Harvard Business Review, "Personalization in Finance: Why It Matters," 2023.

[3] IEEE TDSC, "AI-Based Fraud Detection Frameworks," vol. 20, no. 5, 2023.

[4] OCBC Press Release, "AI Innovation in Cybersecurity," 2024.

[5] Axis Bank AI Whitepaper, 2023.

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Published

2023-12-19

How to Cite

AI IN CONSUMER DIGITAL BANKING: ENABLING SMART PERSONALIZATION AND FRAUD DETECTION. (2023). International Journal of Engineering & Extended Technologies Research (IJEETR), 5(6), 262-276. https://doi.org/10.15662/y6nvry65