How Big Data & Analytics is solving all problems in the Banking and Finance Industry

Modern companies want to know everything about their customers. They want to know where they are going, who they are meeting, what they are eating and of course what they are buying. They are one step ahead of the potential customer, knowing what she’ll do before she does herself. This, of course, only a distant dream just a few years back, is a reality today. The reason behind this is Big Data & Analytics which is changing the way businesses function. 

In the Banking and Finance Industry, its applications go much further than customer data, and the potential for its uses are immense to the say the least.

Here are a few significant areas where firms can (and are) benefitting from Big Data Analytics.

Customer Segmentation and Personalized Marketing

Although we might consider ourselves to be unique, statistically speaking we can always be clubbed under a fixed set of personality profiles or customer segments. These profiles can, almost with precision, predict most of our online activity patterns and purchase behavior. This particular application of Big Data is one which spans sectors. In banking, this helps the banks deliver the right offer or product to the right customer segment.

Taking customer segmentation a step further, personalized marketing is what garners the most hits. Depending on whether the consumer is looking to buy a house or some clothes, he is offered a house loan or a credit card. Customized offerings are made to individual customers after analyzing their recent purchase activity and online behavior.

Cross-selling, or the act of selling an existing customer an additional product, is another practice that has become easier to convert into a sale due to customization using personalized data.

Risk Management and Fraud

By nature, the banking industry is a sector where the firm not only wants to but should know about its clients. User card limits, loan sanctions, and credit scores are all metrics requiring a deep insight into the behavior of the consumer. And Big Data has been a panacea to the hurdles in these activities. High-risk customers come with the likelihood of default but are also the drivers of revenue. With analytical modeling, a risk assessment predictor forecasts the default risk probability which helps the banks take a suitable decision. The risk-reward tradeoff is optimized which in turn maximizes profitability.

These models are also used by banks to frame their credit policies and strategies. Credit exposure is managed right from its origination to fulfillment. Default percentages are now internalized in the policies, and the banks have effective provisions in place for them.

Fraud analytics is perhaps the most sought-after predictive analytics related information in the banking sector. As per a 2017 release by PTI, banks in India lost an estimated ?17,000 to frauds in that year. Through profiling and historical statistics, fraud can not only be detected very early, it can also be prevented altogether.

Operations Optimization Strategies 

Numerous process that banks undertake can easily be automated owing to Big Data, which would save many man hours and otherwise spent resources. Some examples are loan sanctions, queue optimizations, and process efficiency strategies. An example will highlight one of the various operations optimizations that are possible through Big Data. Suppose a bank incentivizes the data updating process of customers that is carried out by employees in terms of the number of such cases an employee can update in a day. After a while, the employees might start taking up the easier or the less time-consuming cases so as to maximize the number of updation cases they can take up. This would mean a few cases might not get taken up at all. This would be an undesirable situation. With big data analytics, it would be possible to analyze and assign weights to the cases that get left out to modify the incentivization process. This rather simplistic example is representative of the numerous ways processes in a firm can be optimized.

Regulation

It is a well-known fact that banks and financial institutions have to meet a lot of mandatory regulatory requirements. Analytics models are used to predict and hence internalize a lot of requirements that the banks may have to comply with beforehand. This ranges from a lot of things such as prediction of stress days (i.e. days on which higher than usual withdrawals happen) to meet the minimum liquidity requirements, to macro level predictions to make provisions for taxations or exposure.

Since the 2008 financial crisis, the number of such compliance norms has grown even further and a lot of them have been enforced very recently. This means the banks have yet to make much headway on a standardized efficient way of tackling them. Big Data & Analytics holds the key to successful compliance in this regard.

Feedback

Customer relations can be considered as the foremost driver of business growth in a lot of aspects. Based on the customer segmentation strategies and the feedback received from the individuals, big data can help predict the best possible tactic for grievance redressal which in turn can maximize customer satisfaction.

Organizational Development and Employee Relations

Employee engagement is an area that arguably has the maximum scope for Big Data applications. It can be used in salary and incentive optimization to maximize performance, identifying employees at attrition risk (both current and at entry level) to reduce turnover and to incorporate their feedback to increase the satisfaction and hence productivity.

Thus in a data-powered world, enhancing capabilities in Big Data & Analytics can drive sustainable growth, reduce risk and maximize performance. Though its implementation is yet to be materialized on a large scale, judging by its potential for value addition, it would not be long before it becomes commonplace. Big Data & Analytics is the driver of a revolution and there’s no turning back.