Top ways how Predictive Analytics helps the Banking sector

Predictive analytics was initially designed to predict the failure of machine parts based on their performance data. The art of scientific prediction using data has come a long way since then. Apart from predicting machine failure, predictive analytics are extensively used in fraud detection, customer behavior prediction, risk management, identifying drug success rate and many other applications in almost every industry. The banking sector is not behind and is using this technique to not only improve its margins but also reduce its risk and losses. Banking industry applies predictive analytics for cross-selling, retention, fraud detection, liquidity planning and many other retail and banking applications.

1. Acquisition. The customer interaction and other data of the potential leads can be used to identify the most valuable potential customers and is the best way to acquire them. Analytics are used to determine the right customers, gauge their likelihood to join (based on various acquisition strategies), the right time to target those potential customers and the most appropriate channel to approach them. Screening of applications which is an essential part of the acquisition process is made more effective by processing large volume of applications without excluding critical variables and without delays or errors with use of analytics. The results obtained with use of predictive analytics are more accurate and authentic.

1. Customer retention. Far from the boring traditional model of the banking business, modern banking thrives on competition, value-added services and on identifying most valuable customers to retain them, all for a minimal cost. With so many banking options available in the market, the customer loyalty is getting fickle. A bank today needs to focus not only on retaining customers, but those customers have to be of the right type, the ones that fit into bank’s target strategic demographic (Ones that are most likely to invest in bank’s target products). Predictive analytics helps in identifying such customers and focus on the retention activity for them. Not only that, it is now possible to quickly identify which retention strategy is likely to work best for a specific customer from a plethora of options. It is possible only by using analytics.

1. Customer Segmentation. Traditionally, the customers are segmented based on past static data. Real-time analytics can be used to segment the customers based on their potential business value which in turn is based on both – recent and historical transactions as opposed to just historical transactions. These insights into customer’s lifetime value are used for making improved recommendations to retain the customer.

1. Campaign Optimization. Traditionally, the campaigns were based on static customer profiling. Predictive modeling, combined with business rules, can make most profitable campaign selection decisions for each customer. Each customer can be profiled against multiple campaigns to identify the best one for that customer. This can improve the conversion results by as much as 20% for each campaign. For example, one bank in USA achieved 600% (Yes, that is six hundred) ROI with better campaign efficiency using Predictive analytics

1. Collection. Banks always have some customers who do not pay on time. Some of these payment defaults are incidental, one time while others are habitual. Nonetheless, delayed collections cost bank in terms of cost of collection and cost of turnover of money to earn interest (or fees). Predictive analytics can help in identifying the potential customers who are more likely to default, well before they default. Portfolio risks can be evaluated with better accuracy and collection effort can be made more productive based on these risks.

1. Liquidity planning. This is one of the biggest challenges for any bank today. With so much of commerce getting digital and yet so much of cash moving via ATMs and cash counters, liquidity planning is anything but easy. However, analyzing the customer behavior along with social insights can help in predicting the cash demand (e.g. A customer who just swiped his card in a different city, on his salary day is not likely to withdraw cash from his home branch. Neither is he likely to deposit any more cash.) Such insights at an individual level can help maintain daily coordination between inflow and outflow of cash and maintain the liquid assets only the extent necessary, generating maximum returns for the bank.

1. Fraud detection. The banking sector is no stranger to fraud. With increased digitization, the number and value of frauds in banking have gone up exponentially. Cybercriminals commit frauds which are not easily detectable. Predictive analytics with machine learning, big data, etc. can help detect these frauds by analyzing real-time stream of data and even catch a fraud in progress. Data mining, big data analytics help in uncovering fraud that may have slipped or escaped the real-time fraud detection, before it’s too late in the day to catch the culprit.

1. Experimentation/Closed loop analysis. One of the most significant advantages of predictive tools is experimentation with the vast amount of data that has been collected by the bank. It is now possible to discover how the customers will behave with slight tweaks to product or services. Monte Carlo simulations can produce results of such experimentation without any impact on real business and produce most realistic results. The products or services can then be tweaked to improve the outcomes before they are released in the market. Such optimization of services, before they are released is only possible with the use of predictive technologies.

The on-demand prediction analytics provides real-time results based on most up to date data on the customer, rather than old, static data. This not only improves the top line, by bringing more and better customers, it also helps in reducing costs in terms of time and effort required to position the most appropriate product to the customer, rather than peddling everything in the basket of the bank. Predictive Analytics when applied rightly, can improve almost every process used in banking.