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Reimagine Banking with Analytics: Part 2

3AI November 5, 2020

In the current macroeconomic environment post economic downturn and with rise in number of regulatory norms, growth in the Banking, Financial Services and Insurance Industry is hard to come by. Add to that the growing demand of customers for better services as a direct relation to them also being better informed, and they will not think twice to switch to a competitor offering if they are not happy with the services. Such a situation calls for a complete revamp in the working methodology of BFSI Industry. Find out how adoption of Analytics can foster this. 

The rise of digital has witnessed the increasing number of different types of customer touch-points for financial transactions, and also the inclusion of new ways of creating value for customers in the financial services domain, like spending analysis and insights, wealth-management capabilities and advisory services in savings, investment and legal. This Omni-channel, always connected model has not only led to creating value for customers in avenues of saving their money, time & effort, but also led to generation of huge amounts of customer data. Leveraging this potential gold-mine of customer insights using Advanced Analytics would make sure that financial firms can go further ahead to provide highly personalized customer experience, leading to customer acquisition and retention. From the company’s standpoint, Advanced Analytics can benefit them in three major aspects – driving revenue, mitigating risks, and cutting down on costs.

Driving Revenue

Acquiring Targeted Customers

Implementing Advanced Analytics for performing customer segmentation by assessing the customer lifetime value, profitability and customer brand loyalty, and targeting them with customized and personalized marketing activities using Campaign analytics, prospect segmentation etc. via multiple channels which are already setup as part of digital business model.

Growing Business from Customers

Due to the prevalence of Omni-channel of customer engagement for financial solution, teamed with rise in value-added services quickly becoming mainstream services offered due to rise in customer expectations, it presents excellent opportunities for cross-selling and up-selling  various services across various channels. As customers get more engaged and become more brand loyal, getting more business from them becomes easier which is further accelerated by implementing Advanced Analytics measure like Channel-mix modeling, profitability models etc. for cross-selling and up-selling. This has the potential to greatly increase revenue.

Customer Retention and Inhibiting Churn

Analytics processes like Proactive value based churn, Silent churn, etc.  Can be leveraged around the customer data pertaining to usage trends, frequency of branch visits, number and kind of complaints lodged etc. which could be an indicator of potentially unhappy customers. This can be then used to identify the pressure points of profitable customers and provide them with recuperative services and benefits in the form of Personalized Offers, trigger-based cross sell campaigns, Bundled Pricing, and Next-Best Offer to decrease customer churn.

Evolution of Financial Services with Changing Customer Behavior

Financial Firms need to keep a sharp eye on consumer behavior and constantly adapt to their changing demands. Social Media listening and measurement can give the Financial Firms ample amount of customer behavior data, which when leveraged properly using              techniques like sentiment analysis, keyword-trend search, graph search etc. will provide critical insight on how their preferences are evolving.



Apart from driving revenue, Advanced Analytics avenues like Predictive Analytics provide various ways in which a financial firm can bring down its operating costs. Some of the primary ways are:

Fraud Detection

Fraudulent Activities like credit card purchases, tax returns, fraudulent insurance claims, fraudulent bank transactions, invalid online ad-clicks, etc.  can be a major add-up to the operating costs of a financial firm. This is further complicated by transactions become increasingly automated and large in number. A complete digital setup will also mean fraudulent activities can be executed remotely, while being hidden, anonymous and inconspicuous. Fraud detection by Prediction (Predictive Analytics) and by Behavior Analysis (Behavioral Analytics) will make sure that Financial firms will not have to invest a lot of capital to manually check every transaction, every account. This will also mean they find more frauds per day and take rectification measures to save on losses due to fraud.

Response Modelling & Uplift Modelling for Marketing

Leveraging Advanced Analytics to assess prior marketing campaigns tracking the response of customers will provide critical info on the probability of them responding in the next marketing campaign. This can help create marketing campaigns highly targeted to those who have a high probability to respond (Response Modeling) which can significantly bring down marketing costs.

Also, tracking customers who would have made the purchase anyway would give one an estimate on the extra marketing capital spent without purpose. Predictive Analytics are the primary techniques implemented in such modeling.

Risk Mitigation

Discriminatory power of models

One of the most significant applications of Advanced Analytics is in the venue of mitigating risks, primarily surrounding credit-risks. Banks and other Financial Lending Firms are constantly trying to decrease the number of loan defaults. This requires knowing in prior before handing out the loan, the probability of the customer to default on their loan repayments.

Models are created which has the capacity to discriminate between good and bad risks,  which is called the Gini coefficient. The Gini Coefficient can be  increased by creating a 360 degree view of customers around aspects pertaining to financial health, relationship with the financial firm, spending and investing patterns, etc. The better the Gini Coefficient, the better the discriminatory power of the models. Additionally they can rope in 3rd party channels of information surrounding the customers to improve upon the discriminatory power of the model

Shadowing other payment trends

In developing countries, it is more challenging to find all-round data of customers pertaining to their financial health, spending and investing patterns, and repayment affinity. In such cases financial firms are tracking the payment behavior of customers in some other industry, like telecom for example. The paying behavior for the telcos is actually a great predictive indicator for the credit behavior with the bank. Thus financial firms can improve upon their underwriting by appending telcos payment data to their bank data and creating predictive models around it.

Using Text Analytics

Banks can increase their approach to qualitative assessment and improve their credit-risk assignment by leveraging textual information surrounding the customers. This information includes professional content such as Research Reports, Business Publications, Journals, stock indices as well as informal sources like blogs social media data. Such data is greatly more prevalent than financial information available about customers, corporate or small and midsize enterprises (SMEs), and provides a wealth of information around the latest developments of companies, companies’ strategies, competitive positioning, and outlook; All such data can be leveraged using Text Analytics and appended with the 360 degree view of customer’s financial records to create accurate depiction of the financial health and standing and probability of defaulting by the customers.

The Adoption Trend

The trend of adoption of Advanced Analytics by Financial Firms is going to be similar to the trend of adoption of ATMs in the early days, or that of internet transactions, which created competitive advantage for the companies for a few years, after which slowly the practice became mainstream and adopted by most of the firms. Hence what is to be seen is who will use this window to adopt Advanced Analytics and create competitive advantage to drive higher profits for a stretch of time, till the practice itself goes mainstream.


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