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Big Data Boost: Set to revolutionise critical banking functions

May 06, 2015

India’s banking and financial sector is expanding rapidly and is poised to become the fifth largest banking industry in the world by 2020. In this endeavour to increase the bottom line and manage risks better, banking and financial sector companies are exploring various business models and looking to increase their use of technology to support key operations.

For the banking sector, one of the most promising technologies is big data. Increasingly, players in the banking, financial services and insurance (BFSI) industry are realising the benefits of leveraging big data to collect and extrapolate valuable consumer information so as to offer innovative and cost-effective products and services to customers. According to industry sources, the big data technology and services market in India is expected to grow from $40.7 million in 2012 to over $130 billion by 2015. A large part of the growth in big data and its adoption is expected to come from the BFSI and IT sectors. The BFSI industry intends to use big data as an effective tool to enhance the customer experience and build sustainable businesses. Further, the technology can be leveraged for enhancing various functions such as credit risk, marketing analytics, upsell-ing, cross-selling and down-selling, and customer acquisition, segmentation and churn prediction.

Companies in the banking sector have to deal with a huge volume of data and it is critical for them to analyse this data correctly in order to derive key insights into customer behaviour for better decision-making. Big data analytics can facilitate data mining and tracking of transactions and fraud on a real-time basis. Industry stakeholders can use this technology to reduce transaction time, save costs and improve efficiency. In addition, big data-led solutions can be used to predict customer behaviour and outcomes and assess the business environment in an industry where decision-making cycles are becoming shorter. The analytical and transactional system built using information and insights from big data enables banks to achieve better sales by offering tailor-made products and services to customers. This can be achieved through a better pricing structure by using big data to collect valuable information regarding existing and new customer segments through available sources such as savings accounts, deposit accounts, credit card customer bases and various social platforms. Customer analytics, therefore, can help banks increase their wallet share by focused and targeted marketing. In fact, at present, a large number of public and private sector banks are using big data to enhance their customer onboarding process by reaching out to the right customer with an appropriate financial plan through a suitable distribution channel.

Customer intelligence and risk management are other areas where big data analytics holds huge potential. Several banks have started providing big data analytics skills to their sales force. For example, with the help of big data, sales personnel can accurately assess the risk profile of a borrower by conducting a thorough background check and reviewing the past credit history of the loan seeker. Access to such information allows sales personnel to make quick decisions regarding the allocation of products to particular customers. Many banks are using SAS Analytics Dashboard to provide customer intelligence across the organisation. Such platforms give early warning signals to employees as well as the management to avoid risky investments, thereby improving risk management within the organisation. Axis Bank and ING Vysya Bank along with other leading banks have made significant investments in big data to improve risk management.

Another critical function in which performance can be enhanced manyfold using big data is collection. With greater adoption of technology, banks are able to use multiple channels of debt collection. These include non-intrusive channels such as SMS, email, interactive voice response, dunning letters, and reminder calls through call centres. Banks and financial institutions are using big data to have a consistent and uniform framework for debt collection. Organisations also use big data analytics to evaluate performance and prioritise or deprioritise collections across branches.

BFSI players are also relying on big data techniques to improve money laundering monitoring. Data analytics helps organisations identify and detect suspicious transactions, thereby bringing down the incidence of fraud in the banking system. Also, the use of big data can help the industry achieve more transparent and integrated client servicing.

Today, the BFSI industry is looking at new ways to leverage technology in order to achieve higher efficiencies in dynamic market conditions. Among various technology trends, big data and analytics is undoubtedly emerging as the topmost priority of chief information officers at leading financial institutions as it allows the management to make timely decisions and reach out to potential clients through the right channel, with the right product. Going forward, big data will play a crucial role in revolutionising critical banking functions such as market segmentation, customer acquisition, marketing, cross-selling, monitoring, predictive analysis, and forecasting and alert generation.


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