Machine Learning Approach to Detect Fraudulent Banking Transactions


Master's Thesis, 2022

69 Pages, Grade: 3


Abstract or Introduction

The study investigates whether a machine learning algorithm can be used to detect fraud attempts and how a fraud management system based on machine learning might work. For fraud detection, most institutions rely on rule-based systems with manual evaluation. Until recently, these systems had been performing admirably. However, as fraudsters become more sophisticated, traditional systems' outcomes are becoming inconsistent.

Fraud usually comprises many methods that are used repeatedly that's why looking for patterns is a common emphasis for fraud detection. Data analysts can, for example, avoid insurance fraud by developing algorithms that recognize trends and abnormalities. AI techniques used to detect fraud include Data mining classifies, groups, and segments data to search through millions of transactions to find patterns and detect fraud.

The scientific paper discusses machine learning methods to detect fraud detection with a case study and analysis of Kaggle datasets.

Details

Title
Machine Learning Approach to Detect Fraudulent Banking Transactions
College
University of Applied Sciences Berlin
Course
Project management and Data Science
Grade
3
Author
Year
2022
Pages
69
Catalog Number
V1275894
ISBN (Book)
9783346728951
Language
English
Keywords
Machine learning, Fraud detection, Banking fraud
Quote paper
Riwaj Kharel (Author), 2022, Machine Learning Approach to Detect Fraudulent Banking Transactions, Munich, GRIN Verlag, https://www.grin.com/document/1275894

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