Big Data can help Financial Services providers understand the customer

By January 29, 2014
Big Data

Taking the financial services sector as an example, business consultancy firm Capgemini offers a guide to non-technology companies on how they ought to be exploiting the potential of Big Data analytics in their strategic decision-making.

Management in the financial sector frequently dismiss Big Data as a mere IT tool, an attitude which leads them to take a misguided approach and consequently miss golden opportunities available to those who make good use of the data they collect. Now a study from Capgemini reveals the value of data analytics in the financial sector – inter alia creating a holistic view of the customer landscape. Using Big Data analytics could help banks, insurance companies and other financial sector providers to obtain a deeper understanding of the factors influencing the behaviour of the target audience and enable them to take a more personalised approach to the client and improve their communication strategies. To illustrate what is at stake, the Capgemini researchers give two examples of success stories.

Sentiment Analysis

One of the examples cited by the Capgemini authors focuses on the operational approach to Big Data taken by an Indian bank, ICICI. In an attempt to limit and prevent defaults on private customer loans, the bank set up a model that captures the details of each ‘delinquent’ case, factoring in a wide range of parameters, including risk behaviour and various sociological factors, so as to identify the best method of debt collection. Using this procedure, ICICI managed to raise the rate of successful debt collections by 50%, and in some regions the bank was even able to reduce manpower requirements by 80%. The Capgemini team identifies a set of tools which financial services need to deploy in order to exploit Big Data meaningfully. One of these is Sentiment Analysis based on emails, documents, social network exchanges and conversations between employees and clients, which provides a measure of the level of confidence or mistrust towards the institution. This can also help financial institutions both to estimate the risk of fraud and assess the level of operational risks being taken by their employees.

Building up experience

What the research team calls the ‘diamonds’ that can be mined from Big Data are real ‘actionable insights’ that are “likely to drive specific, positive, business outcomes,” says the report. Although some people might fantasise that Big Data might provide a way to forecast interest rates and market movements, it is primarily at the level of business operations that data analytics can deliver most value. Less ambitious – and also less expensive – analytics applied to the operational level can enables a company to substantially improve its business processes, allowing the IT department to garner useful learning experiences on lower-impact targets as a first step before moving forward to apply Big Data insights on a larger scale.

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