Why Big Data Makes Sense for the Banking Industry - eKutumb Why Big Data Makes Sense for the Banking Industry - eKutumb

Why Big Data Makes Sense for the Banking Industry

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Why Big Data Makes Sense for the Banking Industry

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Today, businesses across industries realise the importance of customer-centricity forbuilding better customer relationships and generating more revenue. Banks and financial institutions and are not an exception. Research by Capgeminishowsa big perception gap—70 percent of executives from the banking industry believe that customer-centricity is important to them but only 37 percent of customersfeel their banks adequately meet their needs and preferences. This is because many banksare not fully utilising data to generate customer insights that can enhance customer experiences, according to the study. In fact, Capgemini’s researches show that banks using customer data analytics edge out banks that do not.

However, considering that banks already generateand often aggregate a huge volume of data on daily basis, big data’s benefits for the banking industry should not be limited to just enhancing customer experience. Banks, in our view, should have a wider approach to adoptingbig data analytics in their business processes. Incidentally, because of considerable regulatory led programs like Basel 2/3, Credit, Market and Operational Risk investments, many banks now possess considerable maturity in using analytical approaches for decision making. For many banks these are fairly advanced and there are a lot of learning from these implementations. We believe that banks will gain considerably if these experiences are carried into developing Centres of Excellence for big data analytics and solutions.

Here are some ways banks could leverage big data analytics in combination with the right framework and approaches for implementation and execution.

  • 1. Drive Better Customer Acquisition, Experience, and LoyaltyToday’s digital-savvy customers expect banks to deliver a better all-around experience. From swift on-boarding, to predicting their needs and serving them smartly on time, they have high expectations from their banks. Big data based analytical solutions can help banks deliver such experiences and create significant value for themselves and their customers.Big data analytics encompassing a wider variety of data such as form social information, coupled with real-time execution capabilities, can help marketing managers not onlyunderstand customers better as groups, sub-segments and as individuals but also predict and take real-time actions on such insights. This often results in improved customer engagement, loyalty and experience. Additionally, learning algorithms deployed in intelligently designed engagement loops can supercharge such customer experience paradigms.

    According to Capgemini’s observations on a leading European bank, simply shifting to advanced, predictive analytics models (that uses both internal and external customer data) from one that solely relied on internal data helped the bank to serve their customers better. Likewise, a mid-sized bank in Europe used diverse data sets of their customers to analyze probability of churn for each customer and thus saved millions of dollars from going out to other institutions.

  • 2. Deliver New Types of ProductsIn today’s banking landscape, data is king. Each day we hear of new players developing data-led disruption models to challenge existing players. Banks will be well-advised to take a deeper look at their existing data assets and work with product teams to create more trust based data services platforms for enriching their products and customer experiences. New sources of data from external providers can enrich existing products and create new product opportunities for banks.According to the Accenture’s North America Consumer Banking Survey, 79% of consumers surveyed defined their banking relationship as transactional and this trend was growing. This is not good news for banks and a growing trend in such perception will make it hard for banks to improve their profitability and shield them from industry disruptions. However, the study also points out that consumers are open to receiving more value-added services from their banks, including discounts for purchases, proactive bill services, and proactive product recommendations. Banks have an opportunity to look at their existing products and processes and re-imagine all the value creation possibilities along the entire customer interaction journey of which they are presently only point player (such as a payment transaction). For such new capabilities, banks will need to look at entire transaction value chains that their customers are participating in, create capabilities to understand its digital footprints and devise mechanism of capturing, analysing and creating trusted data led products and solutions across the network.
  • 3. Better Risk Management and Fraud PreventionBig data and new analytical approaches and methods can make it easier and faster for financial institutions to identify and manage the complex risks associated with the development, deployment, and maintenance of intricate models used for risk management, valuation, and financial/regulatory reporting purposes.In recent times there is much discussion about use of machine learning techniques coupled with diversified information sources to enrich and improve on the current generation predictive models that are used by banks for decisions on consumer credit risks. One such paper from MIT demonstrated significant improvements in the classification rates of credit-card-holder delinquencies and defaults over traditional models. Similarly, internal data sources combined with external information and new machine learning techniques are being applied to create vastly improved capabilities in anti-money laundering and fraud detection.

    Big data’s implications are far-reaching. Until now, banks have only scratched the surface in terms of how they use the large volumes of data residing in their systems – from transactional, account-related, customer or employee data, behavioural,financial, market-related, etc. Integrating and utilising these data points in new ways to improve their profitability, performance, and business operations is where lies the future of big data in the banking sector.

Author: Kishore Kapoor

Kishore Kapoor an industry veteran of 31 years in global banking technology. A Founder & CEO of eKutumb.com – World’s first marketplace for enterprise software delivery and consulting business by creating value for all of industry stakeholders involved (customers, partners, individuals and investors) through a disruptive and trans-formative approach of doing business.

Kishore Kapoor Founder & CEO Of eKutumb

Kishore Kapoor an industry veteran of 31 years in global banking technology. A Founder & CEO of eKutumb.com - World’s first marketplace for enterprise software delivery and consulting business by creating value for all of industry stakeholders involved (customers, partners, individuals and investors) through a disruptive and trans-formative approach of doing business. You can connect with Kishore Kapoor on Linkedin and Google Plus