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Capstone project credit card fraud detection

This has become a major problem in the modern era, as all transactions can easily be completed online by only entering your credit card information. Even in the s, many American retail website users were the victims of online transaction fraud right before two-step verification was used for shopping online. Unauthorized card operations hit an astonishing amount of There were around 13, reported cases in California and 8, in Florida, which are the largest states per capita for such type of crime. Here are some credit card fraud statistics:.
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Credit Card Fraud Detection Case Study: Improving Safety and Customer Satisfaction

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GitHub - bukhbayar/capstone-finance: Capstone Project - BlackCatData

One of the major pain points for the credit card industry has been to accurately find potential fraudulent transactions and to process them to completion. Every credit card transaction that requires the involvement of a customer service representative costs the company money. Credit card is often a payment method of convenience for the user. The more the genuine transactions get delayed due to checks in place to prevent fraud, the greater is the chance to alienate the consumer. Similarly, credit card firms will need to built a larger customer service workforce to ensure timely processing of transactions. The data that has been used as part of this project is from kaggle. This dataset presents transactions that occurred in two days, where we have frauds out of , transactions.
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Companies are looking for credit card fraud detection software that will help to eliminate this problem or at least reduce the possible dangers. It is a set of activities undertaken to prevent money or property from being obtained through false pretenses. Models make predictions based on information about a transaction and some context historical information.
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A team of analytics students carefully studied credit card fraud by creating synthetic data that represented a large population of credit-card users and then were able to build a model that catches credit card fraud in real time. Students at the University of Chicago set out to develop a real-time anomaly detection process to detect abnormal behavior within credit card transaction data. First, the students developed accurate consumer segments that consisted of unique, synthetic customers in order to represent diversified spending habits. Their approach was based on finding the anomalies in transactional behavior by defining a region representing normal behavior and declaring any data occurrences that lie outside of that region as an anomaly.
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