Data Mining for Fraud Detection in Large Scale Financial Transactions

EasyChair Preprint 1729

Abstract

Financial Institutions are involved with generating and handling million records of transactions across their platforms. These transactions contain significant patterns and trends which are hidden but needed for knowledge discovery and actionable insight into either fraudulent or non-fraudulent events. Uncovering these patterns and trends has always been a challenge to most financial institutions as they are ever-changing at an unknown frequency and handling the large scale financial transactions is not easy. This study uses data mining to uncover significant patterns and trends in large scale financial transactions and construct a model to predict and forecast fraud on the basis of uncovered patterns and trends.

Keyphrases: Data Mining, forecast, fraud, patterns, trends

@booklet{EasyChair:1729,
  author    = {Knox Kamusweke and Mayumbo Nyirenda and Monde Kabemba},
  title     = {Data Mining for Fraud Detection in Large Scale Financial Transactions},
  howpublished = {EasyChair Preprint 1729},
  year      = {EasyChair, 2019}}