Big Data to the Rescue of the Banking and Financial Sector

Digital transformation, though an enabler of increasing productivity, efficiency, and managing services, has challenges galore mostly in terms of a growing curve of cybercrime and the need to adhere to regulations. The banking and financial sector has been tasked with accessing, analyzing, and managing vast data volumes while it goes about improving efficiency and performance. Also, banks are increasingly focusing on revenue generation, risk management, and enhancing the customer experience, both in retail and business banking. The sector aims at increasing revenue – based on interests and fees. In recent times, the areas of operations for banks have expanded phenomenally – from the traditional retail banking to the higher portfolio of wealth management offering differentiated services. Managing internet based online banking services encompassing social media, mobility, ATMs, and digital wallets has necessitated the use of analytics and information management.

With the banking and financial sector embracing digitization in a big way, the amount of data swirling around has grown exponentially. In fact, apart from the quantum of data and the methodology to collect the same, its type has become even more complex. The data can emanate from sundry sources as mentioned below.

• Customer touchpoints such as ATMs, mobile banking, branches, call centres, credit and debit cards, loans etc.
• For financial data, the sources can be the stock markets, news, regulatory agencies, analytics reports, industry, trade, and social media.

As the rate of data generation grows, business analysts have their tasks cut out. They want the growing volumes of data to be analyzed quickly and stored for a longer period. This is where big data solutions can come to the rescue of the banking and financial sector by offering a next generation data management architecture that is dynamic, swift, secure, and all encompassing.

Big data applications to the rescue of the BFS sector

Infusing agility: As the level of competition increases with the entry of new players and the existing ones undergoing digitization, banks aim at enhancing the delivery of customer services. With customer experience becoming the differentiator as well as enabler of revenue generation, deploying big data management systems in managing data warehouses using Hadoop and/or NoSQL databases can garner better insights into data and drive better decision making. To ensure the seamless functioning of big data management system, emphasis should be accorded to big data testing.

Risk management: Traditional banking architectures have helped the sector to mitigate operational risks, manage credit, capital, and market liquidity, and meet the Basel norms quite effectively so far. However, as the sector goes into an overdrive to dispense credit, predicting the creditworthiness of individuals/businesses by analysing the loan application data has become critical. Moreover, with a growing number of NPAs turning the balance sheets of individual banks red, the focus is on the lack or near absence of due diligence exercised by banks and financial institutions. To gather a better insight into the creditworthiness of individuals/enterprises, big data solutions can leverage P2P payment data from mobile devices, mobile services data purchase, payment for utility services etc.

Also, banks can simulate various risk factors to derive better outcomes using big data technologies at low costs. Big data applications, on their part, can carry out predictive analysis to identify regions notorious for mortgage frauds. The heat maps so generated can help banks and financial institutions to zero in, both at the zip code and individual level, on habitual defaulters. Thus, new loan applications can be properly analyzed backed by correct property evaluation and occupation status. The analysis can help banks get a better insight into the customer’s ability to pay back the loan amount besides identifying opportunities for up-selling and cross-selling of banking products. The efficacy of big data solutions can only be ensured through big data and analytics testing.

Improving customer experience: The customer of today is likely to have multiple relationships with a number of banks. For example, they may have an account with a bank offering no fees followed by a bank with the highest interest on savings, or availing loan from a bank with the least EMI rate. Thus, successful banking products are replicated across banks with customers availing them based on a slew of factors such as the felicity of customer experience, transparency, cost of product etc. Given the competition, banks must ensure customers to stay with them for long. To enable this, banks must anticipate customer needs and preferences and design a product portfolio customized to their needs. No point in guessing that big data solutions can execute the steps anticipating customer needs. This calls for adopting a rigorous big data application testing to ensure the system delivers a seamless customer experience across multiple channels.

Conclusion

The growing footprint of data in the banking and financial sector needs the adoption of big data solutions to infer meaningful decisions. Since big data has the potential to enhance customer experiences while protecting the industry from frauds, big data testing should be made a part of the SDLC.

Diya works for Cigniti Technologies, Global Leaders in Independent Software Testing Services Company to be appraised at Cmmi-Svc v1.3, Maturity Level 5, and is also Iso 9001:2015 & Iso 27001:2013 certified.