AI-Powered Banking in Revolutionizing Fraud Detection - Enhancing Machine Learning to Secure Financial Transactions

Aug 5, 2026

With financial fraud evolving rapidly, banks face growing pressure to protect customer data and ensure transaction security. Artificial Intelligence and Machine Learning enable real-time anomaly detection, pattern identification, and predictive analytics that improve beyond rule-based and manual monitoring. The article examines AI-enabled platforms for cyber security, real-time transaction review, and risk management, proposing new preprocessing, feature-extraction, adaptive learning, and cryptographic techniques to enhance model precision and decision accuracy.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages22

Reading Time55 min

AccessFree to download

Introduction

AI and ML for Fraud Detection

Data-Driven Analysis

Learning Approaches (Supervised/Unsupervised)

Proposed Techniques and Algorithms

Cryptography for Secure Data Handling

Regulatory, Risks, and Future Trends

AI-Powered Banking in Revolutionizing Fraud Detection: Enhancing Machine Learning to Secure Financial Transactions SEEJPH Volume XX, 2023, ISSN: 2197-5248; Posted:04-01-2023 AI-Powered Banking in Revolutionizing Fraud Detection: Enhancing Machine Learning to Secure Financial Transaction Prem Kumar Sholapurapu Research Associate and Senior Consultant, CGI Katy, Texas, USA KEYWORDS ABSTRACT Artificial With the constant evolution of financial fraud techniques, the demand for advanced Intelligence, technological solutions to secure banking transactions and protect sensitive information Machine Learning, is at an all-time high. For this reason, Artificial Intelligence (AI) and Machine Learning Fraud Detection, Banking Sector, Cybersecurity, Adaptive Neuro Boosted Forest, Hash Blue Hellman Algorithm (ML) have been adopted as game-changing agents in improving fraud detection and prevention. These technologies allow for real-time anomaly detection, pattern identification, and predictive analytics, revolutionizing the conventional approach to fraud detection. This article explores how AI-enabled fraud detection platforms are transforming the banking industry, specifically in the areas of cyber security, real-time transaction review, and risk management in high-frequency trading. We present a suite of new techniques to enhance the precision and proficiency of fraud detection models. Specifically, it includes the Splaso Quash Filter, a data preprocessing approach designed to optimize raw data for machine learning models, and Ripe Horn Twin Fish Optimization, a lucid feature extraction technique that improves the ability of the model to detect the critical variables affecting fraud. It uses the Adaptive Neuro Boosted Forest (ANBF) algorithm which is a combination of the neural network's adaptability and the robustness of the use of error information from the decision forest to improve the decision accuracy more than the algorithm band in the decision. We also delve into the Hash Blue Hellman Algorithm, a form of cryptography that secures the storage of data and offers a protective approach for sensitive data transactions. We discuss the implications of using AI for fraudulent activity detection, including regulatory issues, potential negative consequences, and future trends in banking fraud prevention technologies. Experimentation was conducted on the Bank Fraud Detection Dataset under a Python environment. From the analysis, it was revealed that the suggested methodology offers secure financial transactions and maintains trust in the banking industry. I. INTRODUCTION Financial fraud comes with increasing sophistication which mandates the use of advanced technology solutions by banks and financial institutions to secure transactions and customer data. Conventional fraud detection approaches such as rule-based systems and manual monitoring are often insufficient in adapting to the ever-evolving nature of fraudulent behavior. Due to increasingly sophisticated attack techniques including but not limited to identity theft, account takeovers, and transaction laundering, financial institutions need more robust, adaptive, and intelligent fraud detection systems. Faced with this dynamic reality, Fraud Detection and Prevention (FDP) has increasingly turned to Artificial Intelligence (AI) and Machine Learning (ML) as indispensable weapons in the fight against fraud (Bolton & Hand, 2002) . AI-Powered Banking in Revolutionizing Fraud Detection: Enhancing Machine Learning to Secure Financial Transactions SEEJPH Volume XX, 2023, ISSN: 2197-5248; Posted:04-01-2023 Artificial Intelligence and Machine Learning: With access to vast amounts of historical transaction data, AI and ML algorithms can detect patterns and anomalies, enabling banks to identify potential fraud more rapidly and accurately. Figure 1: Fraud Detection Categories Financial fraud comes with increasing sophistication which mandates the use of advanced tec

FAQs

Why are traditional fraud detection approaches insufficient?

Rule-based systems and manual monitoring struggle to adapt to continuously changing fraud behavior and increasingly sophisticated attack techniques such as identity theft, account takeovers, and transaction laundering.

How do AI and machine learning improve fraud detection?

With access to historical and real-time transaction data, AI/ML detect patterns and anomalies, enabling faster and more accurate identification of potential fraud through real-time anomaly detection and predictive analytics.

Which techniques and algorithms are proposed to enhance model performance?

The article describes a data preprocessing approach (Splaso Quash Filter), a feature extraction method (Ripe Horn Twin Fish Optimization), and an Adaptive Neuro Boosted Forest (ANBF) algorithm to improve decision accuracy, plus cryptographic protection for sensitive data storage and transactions.

Aug 5, 2026

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