Hadoop in finance: big data in the pursuit of big bucks

Apache’s premier invention, Hadoop has been doing rounds in the world of big data, revolutionizing various fields and their processes. However, its recent impact on financial sectors has made it a possible risk mitigating service that can surely save the world from financial disasters. If you are dealing with a client with troubling credit history and past relationships have been sticky with the client, then Hadoop can ensure that the client does not get an upper hand.

Insurance and banking sectors have the major chunk of such problems and the problem is at large with more and more digitization of financial factors. As money flows digitally across the world, the value of data suddenly gets a lift because now they are often synonymous.

Why the intervention is important?

2008 saw the world succumb to the worst crisis ever in terms of finance and from then, regulation and frequent reporting has become more important than ever. The banks must be constantly updated regarding capital requirements and liquidity reserve too. Such knowledge however can be clouded by frauds, launderers and various other forces.

There are thousands, if not millions of applications that are submitted through the big banks for account confirmation, loan and many other requests. In short, so much data needs to be harnessed in an efficient and yet secure manner to ensure that data explosion does not become an excuse for these perpetrators.

Distributed computing to rescue

As banks produce trillions of transactions, Hadoop allows them to store as historical data so that they can be banked upon in future for decisions regarding loans or account openings. Bankers, before Hadoop happened, used to rely on third parties to grant eligibility status of loan requests and account opening demands. However, this is where frauds would enter and open high-risk accounts that can put the bank in a dicey situation.

However, Hadoop ensures that everything happens inside it so that it can analyze multiple streams of data coming in with the previous records to tell a bank manager whether the new request is risky or not. In fact, banks can update their loan policies when Hadoop provides new dimensions of decision making and points out where bankers could go wrong.

Complete protection from the unseen dangers

Because of its fantastic ability to work with huge chunks of data, slowly banks can see unforeseen traits of data which may help to detect behaviours that otherwise go unnoticed. High intelligence frauds like terrorism often operate very subtly which may elude the banker’s eyes but will not escape the watchful analytics of Hadoop.

In fact, even while taking decisions, Hadoop can update the risk factors of the decision and the future impacts of such risk so that a better decision can be taken the next time. Often, malicious actors hide behind an array of fake accounts and make them difficult to track down. However, with Hadoop, all transaction logs are now archived and can be accessed for further tracking of such accounts which will surely churn out a link to detect such cunning launderers.