Ten Ways Predictive Analytics Can Improve Banking Sector

Banking CIO Outlook | Friday, November 30, 2018

In this era of technological developments, ever-growing customer base and satisfying their needs has become a challenging task. Catering to individual needs and not as general is what customers demand. Banks are trying to implement various tools to get over multiple challenges. Now the role of Predictive Analytics comes into the picture. It helps the financial institutions to gather the relevant data of its clients, identify all the wrong-doings, helps in application screening and also to predict future outcomes.

Below mentioned are ten ways in which predictive analytics helps the financial institutions.

1.  Fraudulent Actions: With the development in any sector the chances of wrong-doings also increases. But banking and financial firms are more prone to such threats and can cost a lot to them if they are trapped in it. We cannot deny that digitization has paved the way for cybercriminals to commit more frauds, therefore, banks have to be smart enough to deal with such issues. Big Data, Machine Learning, Predictive Analytics, Stream Computing, and Data Mining are a few examples of tools that help us in identifying such frauds. Analytics can be used to recognize, and predictive analytics can be implemented to analyze them further.

Check out: Top Security Analytics Companies

2.  Application Screening: Predictive analysis in banking can help in processing the vast bundles of applications, without excluding essential variables, without any delay or error, without growing tired. The results gained after this is much accurate and authentic to be used. Hence we cannot compare the traditional or manual form of screening and analytics as compared to the current technology.

3.  Customer Acquisitions and Retention: Predictive Analytics help in the process for optimized targeting, making it less demanding for banks to instantly recognize the high-esteem client fragments most likely to react. The client base can additionally extend by obtaining the correct sort of client. According to the report, it was seen the banks that adopted predictive analytics had an expansion of around 10% in new client opportunities for over a year.

Customer Retention is another area where financial institutions have to emphasize to reduce the number of customer defections. Loyal customers must be rewarded, and customer attrition must be reduced. Since an organization has a large customer base, sometimes it is too late to retain a customer, and they lose their track. It's easy to find a new customer, but managing relations with the older ones is equally important. Predictive analytics helps in identifying individuals who wish to switch the bank and the reason behind it.

4.  Buying Behavior of Customers: Tracking the right product and customer usage has become a challenge for the banking industry. With the usage of predictive analytics, banks can now segregate the customers and replace it with individualized messages tailored to reach customer's profile.

5.  Cross-selling: The best way of cross-selling can be done by analyzing the behavior of the customer when he is offered multiple products. The aim of successful cross-sellers to examine as to which specific products are to be sold to whom and thereby predicting the outcome. This process helps in increasing the profit and strengthens the customer relations.

6.  Collections: Banks have all sorts of customers, some who pay on time and rest who lag, keeping a record of each is difficult. Banks can achieve a better comprehension of their portfolio risk and along these lines enhance the productiveness of the collection process. In particular, analytics distinguishes the clients who might be in danger in the future and what moves banks should make to accomplish positive outcomes.

7.  Cash Planning: Predictive analytics enable banks to track the past usage patterns and daily coordination between the in-and out-payments at their branches and ATM's, thus foreseeing the future needs of their potential clients.

8.  Marketing optimization: Predictive analytics help marketers to plan marketing campaigns and programs and then monitor the result carefully. By providing all the details about the customers' behavior and attitude, analytics helps in delivering the right message at the right time to the right individual.

9.  Customer Lifetime value: Customer's lifetime value is to what extent the organizations can hold on to their clients. Distinguishing who the best clients are, improving them in various ways, and once you prevail upon them, securing their loyalty, are a couple of territories that banks are focussing.

10. Feedback: Feedbacks are essential for an organization's growth. Predictive analytics enables banks and financial firms to keep up their association with the clients by giving them the correct services and products for their need and coordinating individual preferences in the most arranged manner.

Check Out: CIOReview | Medium

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