In 2009 BBVA launched a project to analyze brand reputation with the help of Big Data analytics. This resulted in an increase of 1% in the positive feedback and a decrease of 1.5% in the negative feedback of the company.
Everyday petabytes of data are created by ATMs, RTGS, credit cards, debit cards, online trading, etc. Banks need data centers and architectural facilities to manage this data and thereafter analyze it effectively and efficiently. Risk-informed decisions are a necessity for survival but for progress banks need to make customer-centric decisions. These can be achieved by leveraging data analytics, by monitoring customer’s web activity and personalizing their experience by recommending offers and schemes most relevant to the customers’ needs.
Why Implement Big Data?
There was a neatly structured system with paper bonds, promissory notes, bills of exchange, etc. Trading took place on the floor and the entire room emulated a fish market. Then a wise man decided to go digital and we haven’t stopped ever since. The economy went digital creating a complex mesh out of the traditionally structured data. And that’s where the Big Data analytics comes in; to make sense out of that plethora of information.
Banks can now actively monitor their clients and know the entire history of their transactions from the day they are born to up until they are in their hearse. Big Data analytics enriches the concept of KYC and curbs the possibility of fraud by detecting conspicuous customer behavior. It shortens the investigation cycle providing higher and faster filing of SARs.
With Big Data analytics banks can score their clients more precisely on their credit worthiness, based on their previous transactions that are not necessarily with the banks. Also targeted marketing of new products, predicting market trends and wealth management of clients becomes easier when banks have appropriate information regarding the economic activities of their clients. The four steps of detecting, investigating, comparing and identifying are automated through Big Data analytics, which decreases the decision making time substantially.
Applying differential piece wage system in banking might seem imbecile to some ears but it is completely possible. Thanks to Big Data analytics, employers can now monitor the work history of their employees and provide for the good performance and target the employees/segments that are not performing well. A variable based pay program will lead to better performance and employee satisfaction.
A combination of the buying proclivities of the customer and schemes provided by merchants will lead to better location based services. Eg: credit card offers at theatres, airplanes, restaurants, etc.
By inculcating the social media data into bank’s data analytics, banks can provide for resources that the customers are most likely to search for. When a student posts a life event like ‘graduated’ or ‘first job’; banks can seize the opportunity to provide them with funds and other wealth management services that they may require.
The constant battle to juggle the survival and progress quotient in the banking industry has been reduced to one single step of statistics based analysis. Big Data analytics techniques provide optimum risk management, wealth maximization, security, and an enhanced banking experience. Not only does Big Data analytics provide to banks but also to customers and the entire economy as a whole by reducing financial crimes, facilitating speedier regulatory functions and forming a healthy brand that can be trusted.