click here to download project abstract
click here to download project base paper
ABSTRACT
The banking sector is a very important sector in our present day generation where almost every human has to deal with the bank either physically or online. In dealing with the banks, the customers and the banks face the chances of been trapped by fraudsters. Examples of fraud include insurance fraud, credit card fraud, accounting fraud, etc. Detection of fraudulent activity is thus critical to control these costs. The most common types of bank fraud include debit and credit card fraud, account fraud, insurance fraud, money laundering fraud, etc. Bankers are obliged to safeguard their financial assets as well as institutional integrity to armored the global financial system. Anti-fraud guard systems are regularly circumvented by fraudsters’ dodging techniques. This paper hereby addresses bank fraud detection via the use of machine learning techniques; association, clustering, forecasting, and classification to analyze the customer data in order to identify the patterns that can lead to frauds. Upon identification of the patterns, adding a higher level of verification/authentication to banking processes can be added.
INTRODUCTION
According to The American Heritage dictionary, second college edition, fraud is defined as a deception deliberately practiced to secure unfair unlawful gain. Fraud detection is the recognition of symptoms of
fraud where no prior suspicion or tendency to fraud exists. Examples include insurance fraud, credit card fraud and accounting fraud. Data from the Nigeria Inter-Bank Settlement System (NIBSS) has revealed that fraudulent transactions in the banking sector at its peak. Fraud has evolved from being committed by casual fraudsters to being committed by organized crime and fraud rings that use sophisticated methods to take over control of accounts and commit fraud. Some 6.8 million Americans were victimized by card fraud in 2007, according to Javelin research. Such fraud on existing accounts accounted for more than $3 billion in losses in 2007. The Nilson Report estimates the cost to the industry to be $4.84 billion. Javelin estimates the losses at more than six times that amount – some $30.6 billion in 2007. Of course, fraud is not a domestic product as it‘s everywhere. For instance, card fraud losses cost UK economy GBP 423 million in 2006. Credit card fraud accounts for the biggest cut of the $600 million that airlines lose each year globally.
