Bank IT Asia: Cognizant’s Vice President and Head of ASEAN and Greater China Says Data Science can Help Banks Scale Newer Heights

“Amidst the ever-present big data buzz, some global banks have mastered the art of using data science and are already reaping benefits. Riding on big data, they have managed to improve customer engagement, revamp products and optimize marketing outreach, risk management, pricing and ongoing cost reductions,” writes Jayajyoti Sengupta. Excerpts:

“At some point, banks of all sizes, shapes and forms need to incorporate data science into their operating models. The future of banking will be determined by how well banks use technology to maximize their accumulated wealth of transactional and interactional data to better understand hidden patterns of customer behavior and make necessary service improvements by customizing existing offerings to properly align the right products with the right customers. To successfully implement data science, banks need to have a strategic and structured approach.

Companies such as Google, Pandora, Netflix, Amazon—and many others—are winning decisively in their markets because of their refined ability to mine insight from the wealth of digital information surrounding people, organizations and devices, or what we call a Code Halo. When properly harnessed, code halos contain a treasure trove of business value. By understanding these valuable digital records and applying insights gleaned from data from customers, partners and employees, banks can more effectively compete on code halos and gain incredible edge over competitors.

To accurately ascertain how customers prefer to be served, banks can apply such data science techniques as hypothesis testing, crowdsourcing, data fusion and integration, machine learning, natural language processing, signal processing, simulation, time series analysis and visualization. Data science can help banks recognize behavior patterns, providing a complete view of individual customers and segments. Analytics techniques can also play a significant role in the early warning, detection and monitoring of fraud.

Advanced data science techniques could enable institutions to improve underwriting decisions and increase revenues while reducing risk costs. These techniques can be fruitful across all asset classes, all types of credit risk models and the entire credit life cycle, including profit maximization and portfolio management.

For debt collections and recoveries, analytics is a critical part of the process, as it can enable organizations to create an accurate picture of the customers' propensity and ability to pay and, therefore, the amount likely to be recovered. This behavioral scoring is used to segment customers and priorities' collections activities to maximize recoveries and reduce collections costs.

A savvy, experienced team of data science consultants can help create a roadmap that results in a meaningful, business-aligned approach to data science. The best approach is to start small rather than setting off a big bang. Data science offers endless growth opportunities to financial services and banks need to scrutinize their data for invaluable insights before it is too late.”

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