Explainable Ai Financial Transaction Fraud Detection Using Machine

Explainable Ai Financial Transaction Fraud Detection Using Machine As digital transactions grow exponentially, so do concerns about online financial fraud, which continues to threaten both consumers and financial institutions. In this research, we propose a fraud detection framework that combines a stacking ensemble of well known gradient boosting models: xgboost, lightgbm, and catboost.

Advanced Fraud Detection In Financial Transactions Using Machine Pdf | on apr 15, 2025, mainul islam and others published ai driven fraud detection in financial transactions using machine learning and deep learning to detect anomalies and. The proposed study will construct a robust model of fraud detection usable in regulation and compliance by using an ensemble of learning and model agnostic explainability techniques to boost stakeholder trust in the models. As digital transactions grow exponentially, so do concerns about online financial fraud, which continues to threaten both consumers and financial institutions. a highly scalable, data lean approach of the machine learning solution for fraud detection against online attacks is proposed herein. This research study aims to implement an “explainable ai (xai) driven interface and a proof of concept (poc) web application for financial transaction fraud detection using machine learning and deep neural networks” in the financial services and banking industry.

Github Rushi 31 Fraud Transaction Detection Using Machine Learning As digital transactions grow exponentially, so do concerns about online financial fraud, which continues to threaten both consumers and financial institutions. a highly scalable, data lean approach of the machine learning solution for fraud detection against online attacks is proposed herein. This research study aims to implement an “explainable ai (xai) driven interface and a proof of concept (poc) web application for financial transaction fraud detection using machine learning and deep neural networks” in the financial services and banking industry. The field of "explainable ai" (xai) seeks to provide techniques and instruments that can offer reliable, intelligible justifications on behalf of choices and actions of machine learning models. in this case study, we illustrate how to use xai to detect financial transaction fraud. In this study, we introduce an advanced machine learning model integrated with explainable ai techniques to enhance the detection of payment fraud in real time scenarios within the digital finance sector. Being interested in ai systems and financial technology, i have been doing research on how explainable ai can transform fraud detection systems into more transparent, trustworthy, and production. The document discusses the use of explainable ai (xai) and machine learning to enhance the detection of fraudulent financial transactions. it emphasizes the importance of making the decision making process transparent and interpretable, which improves accuracy and trust for stakeholders.