The scale of the fraud problem
The numbers are stark. According to a Nilson report, card fraud was over $24 billion globally in 2016. According to a recent study by Javelin, financial fraud is costing retailers around 7.5% of their annual revenue, with digital retailers feeling the greatest impact. While the size of this number will raise some eyebrows (it includes the cost of combatting fraud) at its size what is not in question is that the volume, variety and complexity of the fraud attacks are all growing. The pressures therefore on the shoulders of the fraud analyst grow ever weightier.
The fraud analyst is the central player in this world. They are the saviors of those of us who have had our identity stolen, only to find that a large loan has been taken out in our name. The fraud analyst’s basic role is to spot the bad guys - which transactions are real and which fraudulent. Fraud prevention is vital for online merchants because if fraud happens, it's the merchant who pays.
Fraud analysis is multi-disciplinary, using know-how from behavioral and forensics specialisms. A fraud analyst can wear multiple hats too, partly identifying fraudulent transactions from data, partly doing research to identify trends in fraud. They often interact with other areas of the business to offer insight and help to optimize processes. In today’s cyber crime ridden world of ecommerce, the fraud analyst is our super hero.
But to produce those heroics the analysts need support. Ecommerce reached $2 trillion in 2016. That’s a lot of transactions to create rules for, to manually review for, and to create future strategies for. To manage this scale, it’s clear that machine learning has an important role to play. But what does the introduction of these number-crunching technologies mean for day-to-day working life of an analyst?