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.


Applying Analytics To Banking

Published on January 19, 2011

Few areas where analytics can be successfully applied in the Banking sector relates to Consumers, Marketing & Risk. Analytics begins & ends with data – (internal transactional data – for instance customer account data & externally available data – prime example being credit bureau data) and how it is interpreted.

Initially Banks get visibility by generating reports from the data available – standard reports which provide visibility into your daily operations, and summarized reports which provide you visibility into current & past performance. On the other hand, advanced analytics enables you to predict future behavior based on the trends / patterns of your past. This will enable you to (for example) conduct highly effective targeted campaigns.

Customer analytics pertains to building predictive model for acquisition & retention (cross / up sell). Another illustration relates to customer segmentation & profiling to better target marketing efforts. Fraud mitigation is a prominent example of how analytics can save millions of dollars & ensure customer satisfaction & enhanced experience. Various kinds of fraud can be pre-empted before the event actually takes place by predictive modeling.

Marketing analytics spans issues like figuring out ROI, advertising spend & channel effectiveness. It also relates to campaign specific matters like predicting response & conversion rates to measure its effectiveness.

From an investment banking perspective, one can analyze & model financial risk via risk analysis, asset allocation & portfolio optimization. Besides, financial & regulatory compliance can be achieved too.

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