<div>Sustainable growth assumes paramount importance in a booming economy like India. To continually achieve this, our banking industry plays an active role through a slew of actions which bears an impact on the economy. However, the banking system has its own set of problems which needs attention. These mainly include managing risk effectively, fraud detection and prevention, maintaining customer relationships etc. All these burgeoning issues can be handled in a streamlined and process-oriented way through the use of Data Analytics.<br /><br />Data Analytics (DA) enables organisations with large customer bases to manage and automate large volumes of day-to-day decisions. DA plays a very important role in ensuring that organisations achieve their growth objectives while better managing the risk and reward of their commercial decisions. According to a NASSCOM report, the Indian data analytics market is expected to grow at a CAGR of 25 per cent to become a $1 billion industry by 2020. <br /><br />Though data is available in abundance, the Indian BFSI sector is yet to utilise this data effectively to make significant inferences and draw valuable conclusions that will increase the efficacy of operations. To bridge this gap between information and the effective use of it, modern DA solutions bring with them the knack to determine what would be the decision to make for both banks and customers. Thus, the use of DA is increasingly becoming a necessity as it effectively filters through huge databases. It provides software and analytical tools that help organisations detect and prevent fraud, and help authenticate new customer services. To put it simply, it helps banks and financial institutions take better, more profitable decisions about their most important asset (customers) throughout their credit lifecycle. <br /><br />Enumerated herewith is a brief on the stages of the customer lifecycle and the usage of data analytics in each of them. <br /><strong><br />Customer Targeting</strong>: DA can be used to understand consumer behavior, for segmentation and to develop the right offer for the right customer. It also helps in determining the right channel to reach out to the customer.<br /><br /><strong>Customer Acquisition:</strong> Within this, there are multiple opportunities for banks to benefit from data and analytics in order to acquire potentially profitable customers at lower cost and risk. For e.g. a bank can conduct a fraud check by using data from internal sources; from other banks; from credit bureaus and from third party data sources to ascertain the identity of the applicant. Once the applicant's identity is ascertained, banks can do a credit check with a credit bureau; undertake analytics and develop an application score to predict the payment behavior of the applicant. This can also be used to offer differential pricing for different sets of customers. In addition to understanding the propensity to pay, data and analytics can also be used for understanding the affordability status of the applicant in terms of ability to pay.<br /><br /><strong>Customer Management:</strong> The golden rule for most banks is to nurture customer relationships. This can only be done through tracking the customer's requirements, propensity to pay and ability to pay. During this phase, the banks also get an opportunity to maximise customer value through cross selling and up selling products. Banks can also use data and analytics to ensure responsible lending. While credit bureau data can play a crucial loan in customer management practices, DA help banks and NBFCs undertake effective risk management measures and provide better customer service.<br /><br /><strong>Collections:</strong> Banks can use data and analytics to reduce delinquency, manage cost of collections, manage attrition and reduce wasted time. It is all about knowing the right value of the customers to understand which customers are worth retaining for the future. Credit bureau data can be effectively used for better delinquency management.<br /> <br />All said and done, it is understood that information too has a time-value. The earlier a piece of information reaches a decision-maker; the higher relevance it holds. DA solutions drastically help in cutting down the time it takes for actionable information to reach decision-makers, thereby improving the value of information and providing a decisive edge over competition. It enables continuous monitoring of the health of the business. Especially in banking, this capability is critical, since there is a need to continuously monitor critical measures such as asset quality and risk exposure of banks.<br /><em><br />The author is Head - Decision Analytics at Experian Services India</em></div>
