The BFSI sector is steadily beginning to rely on analytics in multifarious ways. There is a
compelling case for the argument that without the presence of analytics, the entire operational sphere turns into – you guessed it right, an assumption. This is further proved by the widespread adoption of Big data in the banking space, along with AI (Artificial Intelligence) across the operational spectrum.
How does it help? By enabling invaluable access to analytics in real-time for organisations, authorising swifter action on derived-insights, and higher business procurement via intelligence, thereby staying future-proof and one-step ahead of the competition.
In fact, experts feel that analytics may not only become mainstream, but actually dominate the BFSI market in the near future.
Key drivers of change in the industry
These are some of the major change-drivers that could revolutionise the space in the near future. Here’s taking a closer look at some of them.
• Data management- Analytics and related solutions automatically enable superior data management, helping firms structure, streamline, and leverage data for gaining insights.
• Predictive analytics- This can help immensely in terms of forecasting risks, enabling segregation of riskier customers and better strategising to avoid these risks.
• Fraud detection– With predictive analytics and other intelligence, banks can zero in on fraudulent activity faster, while forecasting and eliminating risks of the same in the future.
• Cloud banking- This helps institutions manage and operate core banking applications and platforms within the cloud, with better service delivery online.
• Data fabric- Banks and institutions gain better coverage for their data points and sources, enabling easy data discovery, better digital access, helping the development of data catalogs and automated data pipelines likewise.
• AI Governance and engineering- This ensures the creation of standardised frameworks for governing and managing usage of AI with suitable engineering, tools, and processes.
Understanding the customer- how analytics makes it possible
Customer understanding is an assumption until analytics comes into the picture.
Advanced data analytics may help in leveraging consumer data to enable better understanding at multiple levels. Sentiment analysis, for instance, can help in assessing consumer feelings/perceptions about the organisation, while other analytics methodologies can help understand what customers want.
Also Read | A changing threat landscape: Need for optimised resilience and security for reinforced business continuity
Banks and financial institutions can mark their customer types, and tailor offerings accordingly. Sales and marketing can be automatically refined as a result.
Analytics also helps generate better customer service and experiences, along with enhancing scope of personalisation and sales figures.
If your aim is to build stronger relationships in the market, while lowering customer attrition levels, explore one of our many analytical and technological solutions that are enabling top BFSI players to stay ahead of the curve, call 08047092630 and book a 30-minute demo today.
Views expressed by Souvik Chaki, Business Head, Indus Net Technologies (INT.)
