Question

What is Fraud Detection and how can Decision Tree be used to discover and prevent Fraudulent Activity?

Answer 1

Solution:-

What is Fraud Detection:-

Fraud detection is a topic applicable to many industries including banking and financial sectors, insurance, government agencies and law enforcement, and more. Fraud attempts have seen a drastic increase in recent years, making fraud detection more important than ever. Despite efforts on the part of the affected institutions, hundreds of millions of dollars are lost to fraud every year. Since relatively few cases show fraud in a large population, finding these can be tricky.

In banking, fraud can involve using stolen credit cards, forging checks, misleading accounting practices, etc. In insurance, 25% of claims contain some form of fraud, resulting in approximately 10% of insurance payout dollars. Fraud can range from exaggerated losses to deliberately causing an accident for the payout. With all the different methods of fraud, finding it becomes harder still.  

Data mining and statistics help to anticipate and quickly detect fraud and take immediate action to minimize costs. Through the use of sophisticated data mining tools, millions of transactions can be searched to spot patterns and detect fraudulent transactions.

An important early step in fraud detection is to identify factors that can lead to fraud. What specific phenomena typically occur before, during, or after a fraudulent incident?  What other characteristics are generally seen with fraud? When these phenomena and characteristics are pinpointed, predicting and detecting fraud becomes a much more manageable task.

Using sophisticated data mining tools such as decision trees (Boosting trees, Classification trees, CHAID and Random Forests),  machine learning,  association rules, cluster analysis and neural networks , predictive models can be generated to estimate things such as probability of fraudulent behavior or the dollar amount of fraud.  These predictive models help to focus resources in the most efficient manner to prevent or recuperate fraud losses.

How can Decision Tree be used to discover and prevent Fraudulent Activity:-

With the developments in the information technology, fraud is spreading all over the world, resulting in huge financial losses. Though fraud prevention mechanisms such as CHIP&PIN are developed for credit card systems, these mechanisms do not prevent the most common fraud types such as fraudulent credit card usages over virtual POS (Point Of Sale) terminals or mail orders so called online credit card fraud. As a result, fraud detection becomes the essential tool and probably the best way to stop such fraud types. In this study, a new cost-sensitive decision tree approach which minimizes the sum of misclassification costs while selecting the splitting attribute at each non-terminal node is developed and the performance of this approach is compared with the well-known traditional classification models on a real world credit card data set. In this approach, misclassification costs are taken as varying. The results show that this cost-sensitive decision tree algorithm outperforms the existing well-known methods on the given problem set with respect to the well-known performance metrics such as accuracy and true positive rate, but also a newly defined cost-sensitive metric specific to credit card fraud detection domain. Accordingly, financial losses due to fraudulent transactions can be decreased more by the implementation of this approach in fraud detection systems.