Identity fraud, in which a slice of your identity ranging from new credit cards to entire bank accounts is taken over by criminals, rose by 49 per cent in 2015 on the previous year. That totalled almost 170,000 cases, according to data collected by Cifas, the financial industry's non-profit fraud advisory service. The reason for the rise is that more and more we use the internet for financial transactions, but have very few ways to verify our identity without cumbersome systems involving human interaction, which are also vulnerable to fraud. Cifas' 2015 Fraudscape report shows that 86 per cent of identity fraud happened online, with bank accounts and credit or debit cards most targeted, closely followed by loans and communications, typically mobile phone accounts. Traditionally, companies dealing with such problems have acted after the fact, trying to unravel complex or opportunistic frauds by working back through audit trails.
Any business that sells goods or services online is vulnerable to attack by fraudsters. This can be using stolen credit card details for purchases online, creating false accounts and even voucher code abuse. The cost of this fraud can be calculated in the multi millions, with chargebacks and related costs plaguing online businesses. In the UK it is the most common crime of all, with 2.47M offences in 2015/16 alone. The traditional approach to tackling this problem is to use heuristic rules and business logic to try to'predict' whether a new transaction that the business is seeing is fraudulent or not.
Machine learning has been instrumental in solving some of the important business problems such as detecting email spam, focused product recommendation, accurate medical diagnosis etc. The adoption of machine learning (ML) has been accelerated with increasing processing power, availability of big data and advancements in statistical modeling. Fraud management has been painful for banking and commerce industry. The number of transactions has increased due to a plethora of payment channels – credit/debit cards, smartphones, kiosks. At the same time, criminals have become adept at finding loopholes.
It is hard to underestimate the role of E-commerce in a world where most communications happen on the web and our virtual environment is full of advertisements with attractive products and services to buy. Meanwhile, it is obvious that many criminals are trying to take advantage of it, using scams and malware to compromise users' data. The level of E-commerce fraud is high, according to the statistics. With E-commerce sales estimated to reach $630 billion (or more) in 2020, an estimated $16 billion will be lost because of fraud. Amazon accounts for almost a third of all E-commerce deals in the United States; Amazon's sales numbers increase by about 15% to 20% each year. From 2018 to 2019, E-commerce spending increased by 57% -- the third time in U.S. history that the money spent shopping online exceeded the amount of money spent in brick-and-mortar stores. The Crowe UK and Centre for Counter Fraud Studies (CCFS) created Europe's most complete database of information on fraud, with data from more than 1,300 enterprises from almost every economic field.
Machine Learning and Artificial Intelligence are offering an entirely new level of possibilities to businesses worldwide, one of those possibilities is Fraud Detection. Financial institutions and banks will never be the same with the opportunities technology offers to deal with criminal activities and fight internet fraud. Learn how it works in this post! The things people used to buy at shops years ago are now purchased online, no matter what they are: furniture, food, or clothes. As a result, the global E-Commerce market is rapidly rising and estimated to reach $4.9 trillion by 2021. This undoubtedly triggers members of the criminal world to find paths to victims' wallets through the Web. Federal, local, and state law enforcement agencies along with private organizations reported 3 million cases of identity theft in 2019. Money was lost in about 25% of these cases.