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The Various Ways Banks Can Benefit From Data Analytics


It is being speculated that by 2020, the total assets become $900 trillion, which is roughly 10 times the size of the underlying global economy. This will result in an age of lower interest rates and pressures on margin. Changing demographics also mean that the banks will have to relocate and open branches in other areas. Add on top of that the disruption caused by fintech companies. Another important aspect that banks are becoming gradually mindful of is the fact that the states are increasing their role in the wake of the various financial crimes that are happening. The increase in public debt, as well as the blurring of boundary lines as far as the financial transactions go, are forcing law enforcement agencies to put more and more regulations and compliances on the banks.

Given the state of things, it is clear that the banks are working under tremendous pressure. Competition is becoming more and more cutthroat in this pressure cooker scenario. Customer experience is what will drive customer acquisition and retention. Digitization is what is on top of every banking CEO agenda right now with the focus areas being investment, Products, customer experience, compliance and the various risks associated with those. It is a well-accepted fact that data is the underlying principle for all digitization endeavors. Major use cases which are trending with the banks right now are revolving around customer retention & experience, risk management, Product creation, faster time to market, and compliances.

Banks are using data available to them internally in tandem with data available externally to enhance their decision-making capabilities. Data is also being used to predict credit score to decide upon the optimal price. Similarly, it is also allowing banks to predict the response to a certain product or offer and decide upon the marketing mix. From the external data sources, banks are studying the spread of the population and based on that deciding where to expand their operations. Similarly, there are several banks which use sensorial data to schedule maintenance of their ATMs.

Right from opportunities of cross-selling or up-selling to where and what to divest and invest is being driven by customer and market data.  Not to forget that ML is being leveraged to identify anomalies in transactions to zero down on fraudulent transactions. Retail banks are also implementing ML algorithms to cross-sell and do profitability or channel analytics. On similar lines, corporate banks are using it in loan risks, cross-selling and to identify their most profitable customers. Investments banks are more interested in new leads, tracking profitability, and coming up with newer enticing products.


Let’s deep dive into a few use cases where we foresee data analytics being widely leveraged –

Customer Segmentation: Banks are looking for ways to segregate their customers based on the economic value. Financial marketers need to combine the demographic data along with the data from their own CRM and core platforms to do segmentation clustering. Online, device usage, social data, core platform data is being analyzed for clustering and identification. With such clear and detailed information about their customers, banks are able to better target them and take initiatives to enhance the customer experience.

Reduce Customer Churn: Banking CXOs are looking at data analytics to prevent customer churn. Most of the banks are crunching humongous amounts of data to create a customer retention model. Data from all the sources mentioned above is analyzed to identify the early signs of churn. Models are developed and nurtured based on all the lost opportunity. Through data analytics, banks are trying to understand the drivers, symptoms and the factors that influence the customer to jump ship. By using decision trees and Logistic Regression, banks are able to determine the path which a customer is likely to take.

Increase Customer Loyalty: The first and foremost step in increasing the customer’s loyalty to the bank is to really understand the customer. By creating a holistic 360-degree view of the customer, banks can come up with bespoke products and services. By understanding the next stop of the customer, banks can ensure that they enhance the customer experience and increase the chances of customer conversion and retention.

Fraud Detection: No discussion regarding banks can end without discussing the role of Machine learning in money laundering and monitoring other fraudulent practices. By collating the data from disparate sources, banks can create a single view of the customer. Subsequently by drawing correlations from the various transaction anomalies pointing to fraudulent intentions can be captured and proper actions can be taken.

Compliance: Banks have to navigate very complex regulatory frameworks. For that, they need robust systems for regular and continuous monitoring and reporting. Using data analytics, banks are able to create transparent workflows and audit trails across geographies which help them in the identification of abnormal trading patterns and maintain compliance.

In the highly demanding and volatile banking industry, data analytics is a powerful tool that is transforming the way the banks function. Using data analytics, banks are able to positively transform their operations and create a stellar experience for their customers: improved customer segmentation, 360-degree view of customer, deeper analysis of customer behavior, effective compliance, risk management, and better marketing and sales campaign – the opportunities are endless.





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