Artificial Intelligence in E-commerce and Banking: Enhancing Customer Experience and Fraud Prevention (2025)

Enhancing Security for Online Transactions through Supervised Machine Learning and Block Chain Technology in Credit Card Fraud Detection

This research probed the capabilities of supervised machine learning algorithms in credit card fraud detection, an essential facet of contemporary digital finance, and integrated the transformative idea of blockchain for added security and transparency. Findings highlighted the predominant efficiency of the XGBoost algorithm, which recorded an accuracy of 97%, a precision of 94%, and an AUC of 0.97. Other notable performers included the Gradient Boosting Machine and Random Forest. The study underscores the potential of integrating advanced machine learning techniques and blockchain technology to significantly enhance fraud detection systems, ultimately enhancing user trust and the security of online transactions. Recommendations for the adoption of algorithms like XGBoost and the utilization of blockchain ensures immutable transaction records and continuous monitoring for adaptive defense against evolving cyber threats. Future research directions might encompass the exploration of hybrid models, blockchain integration, and deep learning techniques to further enhance fraud detection mechanisms. This research provides research reference for financial institutions that strive to optimize the digital transaction standards.

ANALYZING THE IMPACT OF ARTIFICIAL INTELLIGENCE AND MACHINE LEARNING IN DETECTING AND PREVENTING FRAUDULENT TRANSACTIONS IN REALTIME

Multidisciplinary Sciences Journal, 2024

The rise in electronic payment systems has increased cases of fraud and this makes real time fraud detection highly critical to the success of financial organizations. AI and ML technologies have become potent tools for realtime fraud detection and prevention by analyzing large datasets, detecting patterns, and predicting suspicious behavior. This research investigates the role of AI and ML in improving fraud detection and prevention systems, specifically their ability to be effective, and to scale, and to adapt to a dynamic environment. It explores the application of supervised and unsupervised learning models such as decision trees, neural networks, and clustering algorithms, to identify anomalies and block fraudulent transactions. The study further discusses challenges like false positives, data privacy, and the adaptability of models to changing patterns in fraud. The importance of AI/ML for preventing fraud in the future is supported by the findings in its ability to dramatically reduce the number of fraudulent transactions (more than 25%) and increase detection accuracy (90%). The paper closes by making recommendations to better fit AI/ML frameworks for fraud detection with ethical standards and user trust.

Enhancing Data Security with Machine Learning: A Study on Fraud Detection Algorithms

As cyber threats and financial fraud continue to evolve, organizations are increasingly leveraging machine learning (ML) to enhance data security and detect fraudulent activities in real time. Traditional rule-based fraud detection systems struggle to adapt to sophisticated fraud patterns, necessitating the adoption of ML-driven approaches. This paper explores how machine learning algorithms improve fraud detection by analyzing large datasets, identifying anomalies, and mitigating security risks with greater accuracy and efficiency. The study examines various machine learning techniques employed in fraud detection, including supervised learning (e.g., logistic regression, decision trees, support vector machines), unsupervised learning (e.g., clustering, anomaly detection), and deep learning models (e.g., neural networks, autoencoders). These models enhance fraud detection by continuously learning from transactional data, reducing false positives, and improving detection rates. Feature engineering, data preprocessing, and model interpretability are also discussed as critical components in developing effective fraud detection systems. The integration of real-time analytics and artificial intelligence (AI) in fraud detection enables organizations to respond proactively to security threats. Techniques such as ensemble learning, reinforcement learning, and hybrid models further optimize fraud detection by combining multiple algorithms for higher accuracy. Additionally, big data analytics supports fraud detection by processing vast amounts of structured and unstructured data, improving decision-making speed and precision. Despite the advantages of machine learning in fraud detection, challenges such as data imbalance, adversarial attacks, and privacy concerns remain critical. This paper highlights strategies for addressing these challenges, including data augmentation, secure federated learning, and robust encryption techniques. Regulatory compliance and ethical considerations, such as bias in ML models, are also discussed to ensure responsible AI deployment in fraud prevention. Through case studies of ML-driven fraud detection in finance, e-commerce, and cybersecurity, this research demonstrates the effectiveness of intelligent fraud detection systems in safeguarding sensitive information and financial assets. Future research should explore the role of quantum computing and explainable AI (XAI) in advancing fraud detection technologies. By leveraging machine learning, organizations can enhance data security, improve fraud detection accuracy, and reduce financial losses, ensuring a more secure digital environment.

An efficient fraud detection mechanism based on machine learning and blockchain technology

With fraud becoming more sophisticated, conventional detection methods are no longer effective, resulting in a worldwide impact on customers and organizations. To tackle this, cutting-edge technologies like machine learning and blockchain are being utilized by several institutions. This article assesses the efficacy of XGBoost, KNN, CatBoost, and Random Forest in detecting real-time fraud during financial transactions. Additionally, the paper discusses how blockchain technology can create a secure and tamper-proof database for financial transactions used in fraud detection. Our proposed financial fraud detection approach was analyzed using the "Synthetic data from a financial payment system," revealing that 98.79% of the dataset comprised genuine transactions, while 1.212% were fraudulent. The results showed that CatBoost had the highest accuracy rate, exceeding 99.46%, while Random Forest had the lowest accuracy rate of 98.31% among all algorithms. A machine learning and blockchain technique has finally been proposed to identify fraudulent bank transactions.

Cyber Defense in the Age of Artificial Intelligence and Machine Learning for Financial Fraud Detection Application

IJEER, 2022

Cyber security comes with a combination of various security policies, AI techniques, network technologies that work together to protect various computing resources like computing networks, intelligent programs, and sensitive data from attacks. Nowadays, the shift to digital freedom had led to opened many new challenges for financial services. Cybercriminals have found the ability to leverage e- currency exchanges and other financial transactions to perform their fraudulent activities. The unregulated channel makes it essential for banks and financial institutions to deploy advanced AI & ML (DL) techniques to fight cybercrime. This can be implemented by deploying AI & ML (DL) techniques. Customers are experiencing an increase in the fraud-hit rate in financial banking operations. It is difficult to defend against dynamic cyber-attacks using conventional non- dynamic algorithms. Therefore, AI with machine learning techniques has been set up with cyber security to build intelligent models for malware categorization & intelligently sensing the fraught with danger. This paper introduces the cyber security defense mechanism by using artificial intelligence (AI), machine learning (ML)) techniques with the current Feedzai security model to identifying fraudulent banking transaction. We have given a preface to the popular ML & AI model with random forest algorithm and Feedzai’s Open ML fraud detection software tool, which provides automatic fraud-recognition to the current intelligent framework for solving Financial Fraud Detection.

Developing Machine Learning Models for Real-Time Fraud Detection in Online Transactions

American Journal of IR 4.0 and Beyond(AJIRB), 2025

This paper offers a detailed discussion of a large–scale, real-time architecture for fraud detection specifically for use in financial organizations to combat fraudulent activities in online transactions. The proposed system in this paper uses big data capabilities and a multi-stage fraud detection pipeline to detect and combat fraudulent activities efficiently. The implemented technologies include Apache Kafka, KSQL, and Spark alongside Isolation Forest algorithm for behavioral analysis of customer transactions. The presentation of the fraud detection pipeline as a series of layers exemplifies how a transaction goes through an exacting sequence of detection algorithms with very little delay and maximum precision. Verification by simulation uses the dataset of more than one hundred million Internet transactions, the performance indicators of which are a rather high F1-score of 91% and a recall rate of 97%. The results stress the advantage of the proposed methodology over conventional techniques, suggesting the possibility of real-time fraud identification. Furthermore, the paper outlines research directions where future work should focus, such as reducing computational complexity and applying deep learning solutions to enhance the detection of new types of fraud.

AI-Driven Fraud Detection in Banking: Enhancing Transaction Security

Journal of Informatics Education and Research, 2024

An important step forward in risk management and fraud detection has been achieved with the integration of Artificial Intelligence (AI) in the banking sector. In this paper, we take a look at how AI has revolutionized various fields, shedding light on the benefits and drawbacks of this technology. The effects of AI on risk management are complex. More complex credit risk assessment models are made possible by algorithms that can see patterns in massive datasets that people might miss. When it comes to market and liquidity issues, real-time transaction monitoring is absolutely essential for quick risk mitigation. Automating compliance with regulatory norms is another critical function of AI, which helps to decrease human mistake and assures quick adaptability to changes in regulations. The automation of mundane processes and the reinforcement of cybersecurity measures further reduce operational risks. By examining client behaviour and transaction data, enhanced algorithms may adeptly spot anomalies that could indicate fraud. Artificial intelligence's capacity to foresee future events enables it to foil possible fraud attempts. The systems are designed to respond to changing fraudster strategies with its adaptive learning feature.

AI-POWERED RISK MANAGEMENT IN FINTECH: LEVERAGING BIG DATA FOR FRAUD DETECTION

EPH-International Journal of Science And Engineering, 2024

By allowing actual time analysis of vast amounts of information, AI is transforming risk management in FinTech, particularly in the fraud detection. Often depending on rule-based algorithms, conventional fraud detection methods find it difficult to fit the sophisticated methodologies utilized by the modern fraudsters. Big data analytics is used in AI-driven systems to find the anomalies, identify suspicious patterns & the react to emerging risks with more efficiency. By means of supervised learning for transaction categorization & the unsupervised learning for anomaly detection, ML approaches improves financial institutions' capacity to detect the fraudulent behavior with higher accuracy & the efficiency. Deep learning techniques study complex behavioral patterns across many data sources, hence improving fraud detection. Apart from technical proficiency, AI-driven fraud detection has to solve legal challenges like data security laws, compliance requirements, and ethical problems with bias in artificial intelligence models. Big data and artificial intelligence together are transforming fraud prevention methods, lowering false positives, and allowing proactive threat minimizing. Financial institutions are progressively protecting the consumer transactions, fostering trust & the lowering financial losses by means of AI based risk management solutions. Given the increasing cyberthreats, artificial intelligence's capacity for actual time learning & the flexibility make it a required component of contemporary FinTech security.

AI-Driven Fraud Detection in the U.S. Financial Sector: Enhancing Security and Trust

2023

The rapid advancement of artificial intelligence (AI) has significantly transformed the landscape of fraud detection in the U.S. financial sector. This study explores the application of AIdriven techniques, particularly machine learning algorithms, to enhance the efficiency and effectiveness of fraud detection systems. Traditional methods often fall short in adapting to evolving fraud patterns, leading to substantial financial losses and eroded consumer trust. By leveraging AI's capabilities, financial institutions can analyze vast datasets in real-time, identifying anomalous transactions and patterns that signify fraudulent activity. This research evaluates various AI methodologies, including supervised learning, unsupervised learning, and deep learning, assessing their effectiveness in detecting fraud across different financial products and services. The findings reveal that AI-driven approaches significantly reduce false positives and enhance detection rates compared to traditional systems. Moreover, the integration of explainable AI (XAI) techniques fosters transparency and trust in the detection process, ensuring that stakeholders can understand and justify decisions made by AI systems. This study emphasizes the critical role of AI in enhancing security measures and restoring consumer confidence in the financial sector, ultimately leading to more resilient and trustworthy financial ecosystems.

Fraud Detection in E-Transactions using Deep Neural Networks -A Case of Financial Institutions in Zimbabwe

Due to advancement in E-Commerce, the most common method of payment is credit card for both online and offline. It has become the most convenient way of online shopping, paying bills and money transfers. Hence, the credit card industry is investing vast amounts of money to secure credit card transactions. Financial institutions that have adopted credit card as a payment method are prone to credit card fraud attacks. The objective of this study was to develop a distributed application that analyses financial datasets to detect the possibility fraudulent activities in financial transactions. The researchers used the Hidden Markov Models (HMM) to analyze the datasets so as to generate the spending profile of a cardholder. The results generated from the HMM are then fed into the Multilayer Perceptron (MLP) that classifies the transaction into suspicious and non-suspicious classes. Since the researchers could not obtain a real dataset from the bank, one that resembles a bank dataset has been developed to train and test the MLP.