Cases of fraud have increased with the growing numbers of transactions and amounts of money transferred online.
We are seeing that criminals are becoming more and more sophisticated in their approaches to committing fraud and this is having an effect on companies across the world. From data hacks to stealing money, fraud is a serious problem.
Companies do have a weapon that they are using against this though; Big Data.
Through the use of Big Data and the elements of each transaction that can be tracked, companies have the capability to see what, where and who are committing fraud.
With the millions of transactions that take place every day, without a system that can flag up suspect activity criminals could easily manipulate the system to steal money. This is not only from companies either. E-Commerce in the US alone has grown from $30 billion to over $60 billion, so it is easier than ever to either steal or clone a credit card and use it online without it being noticed.
Analytics are allowing companies to see how people normally act, what they buy and the amount they normally spend. When a transaction moves outside of this acceptable deviance, it is flagged up as a potential fraud and action can be taken.
This is relatively simple and is something that financial companies have been doing for a long time, but has needed to become more effective and wide ranging with both the numbers of transactions taking place and the variety of retailers where money can be spent. It has meant that models need to be re-written and the speed of these checks needs to increase.
However, as these improve, so do the methods being used by fraudsters. It means that although these methods may stop most fraud, they can never stop it all.
Despite this, data has a major part to play in finding historic fraud.
It can be used to not only look through the history of one particular fraud, but use the ways in which that fraud has been found to find others in the past. This means that trends can be found and this rule can be applied to others, helping companies to recover loses and learn from mistakes in the future.
Using this data in a model where new fraud patterns can be input, improves actions against similar fraud in the future, essentially immunising against it happening again.