Analytics & Business Intelligence

Factors such as globalization, deregulation, mergers and acquisitions, international competition, and technological innovations, have forced businesses and institutions to re-think and gain deeper insight from the past and present data. Transforming data into intelligence is the key accurate data can help us in applying analytics and Business Intelligence and thus making informed decisions.


Analytics is the science & art of generating actionable insights from data with or without the use of statistical tools & techniques. It is known by various names viz. data mining, statistical modeling, business intelligence, decision sciences, operations research, etc. Essentially, it refers to the process of converting data into information and information into knowledge that is useful for business.

Today we can see that Businesses all over the world are depending more on computer systems to automate their processes. The data collected over the years is now proving to be a valuable resource. Decision making has become all the more challenging and institutions are realizing the importance of making informed decisions by using analytical tools and techniques.


Benefits From Analytics

Published on January 18, 2011

Benefits From Analytics: Some Case Studies

Here are few concrete examples of how banks have leveraged analytics to improve revenue & profitability. Concurrently, they have been able solve business problems & become more customer centric.

Retail Banking

A large MNC bank based out of SE Asia, leverage analytics to improve customer insight & thereby enable more targeted marketing campaigns with faster break-even points. Results are an exponential increase in take-up rates. Analytics has provided trusted insights for areas that include risk, scorecards, performance management and sales-force incentives

A mid sized European bank was focused on improving its product offerings for individual clients. To do this, it has embarked on a project to segment its client base with greater sophistication, using data on the ownership and usage of the bank’s products and services, in addition to demographic data. By exploiting customer data, the bank was able to analyze purchasing patterns within each of the segments, enabling the marketers to predict who will buy which products, and to focus marketing campaigns accordingly. Predictive modeling based on segmentation has given the bank a key advantage to effectively target their market.

A large French bank has been able to realize significant ROI by leveraging analytical CRM. The bank has created indicators for forecasting or analyzing customer behavior, which are then automatically communicated to individual branches. Each branch or call center consultant can then view a customer's file on a personalized card containing all of these indicators. Each customer's net banking income can be compared to the average revenue that customer generates and to the best customer in the segment. Customers are segmented on the basis of the financial indicators, and then an affinity matrix identifies the three products with the highest probability of appealing to those customers – invaluable information that helps sales consultants provide better service and increase sales. The bank developed two interlinked models that would predict attrition several months prior to a customer's departure, automatically identifying within a given customer account if, for example, automatic deductions began to fall off or assets began to decrease. The ability to detect the first signs of departure enables the company to intervene before it's too late and take steps to extend the client's "lifespan."

Investment Banking

A mid sized Asian bank (also offering a complete range of Islamic Banking products and services encompassing the areas of enterprise and consumer banking) was able to offer better risk management practice via better knowledge of their customers' portfolio (by undertaking risk analytics). The bank was able to offer risk-adjusted pricing to their customers.  With a comprehensive risk analytics framework, the bank addressed issues relating to data integration, metadata management, user authority management and credit/operational risk reporting capabilities that are fused within a single platform. This also led to a holistic risk-related reporting capabilities to meet both internal management and Basel-related reporting, as well as solid justifications to set the bank’s strategic and operational directions,”

A large Italian bank sought to deploy risk analytics to calculate risk of managed investment funds. In order to achieve the goal, the bank needed a multi-dimensional view of risk against benchmarks and assessment of investment processes. The bank has put in place a model based on the historical simulation of the returns and on the full valuation of the derivative products.  This multi-dimensional model provides a “view of the risk” in hundreds or thousands of possible ways, strictly in line with the investment process in place. Finally it is a model that tries to represent a fairly wide spectrum of portfolio risks (price, rate, exchange), including the non-linearity risk of derivative products, the credit risk of corporate issues and the country risk of bond issues from developing countries. This has enabled the bank to focus on bringing new strategies alongside those developed by the existing internal performance attribution system. To achieve this result, a good nine hierarchical, drill-down variables have been adopted (asset type, industry sector, industry group, industry subgroup, geographic area, country, currency, rating class and duration band). Besides, the analytical framework provides the basis for the development and introduction of a monitoring system capable of offering management more powerful instruments for the governance and assessment of the investment processes.

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