In the history of banking, it has never been as competitive as it is today. With the emergence of new kinds of digital service platforms, the competition between the banks and other financial institutions (e.g. Insurance) is only increasing. Financial services are becoming more and more like commodity business with little (apart from the security of NOT going bust) that differentiates amongst them for an average customer. Most insurance providers seem similar, and so do many digital wallet providers. Hence they too need to play the game accordingly. They need to focus on retaining profitable customers, reduce costs and continuously innovate. Analytics help them to identify patterns in customer churn, validate new products before launch and lift pressure off the shoulders of the employees helping them focus on the strategic activities. Financial Service providers need to focus on their key business drivers, and predictive analytics assists in accelerating those drivers.
- Accelerating Growth. In-depth profiles of the existing customers, along with an analysis of their transactions and trades with financial Institutions can provide insights into customer behaviour, their preferences and pain points. The likelihood of their defection to the competition can be easily deduced. The information can also be used to identify the products that are more likely to appeal to specific customers thereby improving the conversion of cross-sell and up-sell activities. On the other side, analysis of the merchant transactions can help in enhancing client-merchant transaction thus increasing the commission for the bank, in addition to the revenue for the merchants.
- Enhancing Productivity. Most financial processes are repetitive and mundane (application, validation, underwriting, etc) but need a very high degree of accuracy. Every application needs to be screened with precision to process it for the maximum benefit of the service provider. Processing thousands and millions of such applications is slow, tedious and an error-prone process. Predictive analytics can take over this job and process millions of applications with very high accuracy. The use of predictive analytics for decision support system and in turn to decide the amount of cash that should be maintained in the ATMs across the locations has saved good money for a well-known bank in the USA. The employee cost and time saved are a bonus for adopting this technology.
- Improving Risk Control. Risk management has always been a bane of the banking industry. Almost all the risk algorithms are backward looking, tested on old static data which do not account for real-time parameters. The use of predictive analytics for real-time credit assessment, early warning systems (for collection defaults) and stress testing can produce more accurate results than those done by traditional models. The predictive analytics can be effectively used to detect and curtail frauds. On the regulatory side, compliance reports can be efficiently created using analytics.
- Customer Behaviour and Experience. Digital technology provides an opportunity to addresses each customer individually. Big data has been used to understand the past behaviour and successively approach customers with that insight. However, the past behaviour is seldom a reflection of future actions. Predictive analytics can be used to gauge what exactly may be customer’s next action, in real time and approach him with a suitable response to the most likely action that he is going to take. Similarly, predictive analytics can be used to provide improved one on one digital experience for every individual increasing the customer satisfaction and retention. (for example, with vanilla analytics a customer who has been exploring deposit rates can be shown investment product on his next visit. But predictive analytics can be used to show him the product based on his long-term balance and duration of investment predicted, thus increasing the probability of the sale.)
- Innovation. It is often difficult for organizations to predict which products will work and which will fail. For banking industry,which operates on very thin margins and fees, it is essential to come out with right products that will resonate with the customers and do well. Predictive analytics run closed loop feedback using previous customer interaction data to understand how the customer will react to the product before it is launched. Thus, the product can be tweaked to make them best for uptake by maximum customers. Not only the product, but every banking process can be tweaked and simulated with analytics to understand the outcome of the change. With modern computing, multiple models can be generated with minute variations to create most optimal values for the process variables.
Banks, whether corporate or retail, always need to evolve to stay relevant to their clients. Predictive analytics can help them optimize their resources and identify most profitable products and customers, making the best use of their limited resources.