Background: Business Understanding
In recent times, the number of fraud transactions has increased drastically due to which credit card companies are facing a lot of challenges. For many banks, retaining high profitable customers is the most important business goal. Banking fraud, however, poses a significant threat to this goal. In terms of substantial financial loss, trust, and credibility, banking fraud is a concerning issue for both banks and customers alike. With the rise in digital payment channels, the number of fraudulent transactions is also increasing as fraudsters are finding new and different ways to commit such crimes.
The Federal Trade Commission (US) has estimated that around 10 million people become victims of credit card theft each year. Credit card companies lose close to $50 billion per year to fraud. Finex is a leading financial service provider based out of Florida, US. It offers a wide range of products and business services to customers through different channels, ranging from in-person banking and ATMs to online banking. Over the last few years, Finex has observed that a significantly large number of unauthorised transactions are being made, due to which the bank has been facing a huge revenue and profitability crisis. Many customers have been complaining about unauthorised transactions being made through their credit/debit cards. It has been reported that fraudsters use stolen/lost cards and hack private systems to access the personal and sensitive data of many cardholders. They also indulge in ATM skimming at various POS terminals such as gas stations, shopping malls, and ATMs that do not send alerts or do not have OTP systems through banks. Such fraudulent activities have been reported to happen during non-peak and odd hours of the day leaving no room for suspicion.
In most cases, customers get to know of such unauthorised transactions happening through their cards quite late as they are unaware of such ongoing credit card frauds or they do not monitor their bank account activities closely. This has led to late complaint registration with Finex and by the time the case is flagged fraudulent, the bank incurs heavy losses and ends up paying the lost amount to the cardholders.
Now, Finex is also not really equipped with the latest financial technologies, and it is becoming difficult for the bank to track these data breaches on time to prevent further losses. The Branch Manager is worried about the ongoing situation and wants to identify the possible root causes and action areas to come up with a long-term solution that would help the bank generate high revenue with minimal losses.
Apprehending fraudsters is outside the scope of banking operations. So, the problem that we need to solve is to not stop the fraudsters but to identify and stop the fraudulent transactions that they are making. Several government entities are involved in trying to prevent or stop this crime.
Now, suppose you are consulting Finex for solving the issue of the rise in unauthorised transactions made through credit cards/debit cards. As the first step to solving this problem, you are required to understand the pipeline for a typical transaction and the challenges at each of these steps of the transaction process so that you can make appropriate interventions to solve the problem.
Given all possible hypotheses and considering the feasibility and customer time, the most suitable solution is to implement a fraud detection system. This does not affect the customer’s time with extra OTP checks on all transactions and is also quite feasible, as educating all customers on various fraudulent techniques is a challenging task. Building a fraud detection system is a one time procedure and deploying this would be a permanent resolution to the long time blocker that the banks have been facing since years.
In the banking industry, detecting credit card fraud using machine learning is not just a trend; it is a necessity for the banks, as they need to put proactive monitoring and fraud prevention mechanisms in place. Machine learning helps these institutions reduce time-consuming manual reviews, costly chargebacks and fees, and denial of legitimate transactions.
Suppose you are part of the analytics team working on a fraud detection model and its cost-benefit analysis. You need to develop a machine learning model to detect fraudulent transactions based on the historical transactional data of customers with a pool of merchants. Based on your understanding of the model, you have to analyse the business impact of these fraudulent transactions and recommend the optimal ways that the bank can adopt to mitigate the fraud risks.