Measures for Financial Fraud Detection Using Data Analytics and Machine Learning

Abstract

Financial fraud analysis, recognition and threat estimation are the spine of financial organizations. Impact of fake exchanges, fraudulent event or triggers can rapidly transform into huge dollar losses because of the lack of early identifiers or frameworks to notify fraudulent classifiers. Fraudulent activities are on the rise in various sectors like Investment market, Insurance, Mortgage, Credit card services, Money laundering. Fraud detection, persistently gets focus and consideration from various stakeholders, including financial institutions, controllers, specialists, scientists, network and dealers. Currently, deduction mechanisms apply rule-based frameworks as key means for identification of frauds or misrepresentations. This approach can work admirably by revealing known instances but the challenge lies in its ability to scale up with technologically updated fraud designs. As an alternate and scalable measure, Data analytics and Artificial Intelligence (AI) methods using Machine Learning are aimed to provide a proficient solution. Objective of this survey paper is to investigate current methods associated to financial fraud detection system, by utilizing Data Analytics along with AI strategies. It recognizes the principle highlights of current smart solutions for financial fraud detection and outlines the fundamental patterns as ground base for detection of fraudulent transactions.