To stay on top of business amidst the hyper competition and market volatility, every bank has to be highly driven by both technology and data. The key is to squeeze the most out of every opportunity it has on hand.
By Srikant Sastri
Luckily, banks have a huge advantage — data. If pulled and analysed, every transaction data can reveal tons of unmatched insights into customers’ needs and deeds. Data represents a great competitive advantage and it can make banks smarter and more efficient.
Today’s tech savvy financial businesses and banks understand the value their data represents. They understand that when their structured operational data is integrated with the unstructured data from web, social streams can provide them with valuable insights on transaction patterns to predict customers’ behaviour in the future and to customize their business models to include ultra-personalized solutionssuitable to their each and every customer. To do this, banks require support from high-tech big data firms that can help them combine their data with external data, cleanse and mine for valuable insights.
The algorithms and technology required for doing this are highly sophisticated. Hence,it requires adifferent talent pool to work on, that very few banks can afford. There are many businesses who can caterto these specific needs of banks with their own proprietary platforms. A classic example is the big data driven B2B offering like Merchant Optimizer that helps banks not only to tap into a broader merchant network, but help them optimize their active network for profitability and effectiveness. Another example is Personal Choice app that helps to deliver ultra-personalized rewards & choices to the banking customer, based on their taste, influence, behaviour and context.
Even though customer analytics is the primary focus of banks, there are also vast options available to improve their analytics in areas like fraud detection, risk identification, mapping credit worthiness, regulations and compliance by using big data analytics
The banking sector is rife with change and unpredictability. Owing to its complex environment, it’s difficult to predict the future of this sector with any degree of confidence how changes in banking laws and regulations will affect profitability, what should be the stress scenarios, what is needed to correctly measure each business line’s different risk characteristics and where can we more effectively apply better customer models to reduce risk factors and financial losses…
To find out answers to these puzzling questions and ambiguities, banks can use big data analytics to answer these and identify additional questions to effectively manage risk and drive risk-adjusted performance.
Thus, leveraging big data effectively by integrating both internal and external data can turn routine transactional data intoa solid competitive advantage.
The author is co-founder of big data start-up Crayon Data