Retail banks generate value through a mix of businesses like financing, investing and by doing financial transactions for their customers. Banks have been offering basic banking services for free and charging an attractive margin in other areas like credit card fees and foreign exchange transactions. On an average 59% of the bank’s earnings come from fee-based products and services like advice on payment, origination, sales, etc. which forms an important part of the banks P&L statement.
There is a growing trend of using data and analytics in the financial services industry. Banks are using Artificial Intelligence and Machine Learning to improve their operational capabilities. These technology initiatives are empowering banks to discover new ways of revenue generation. Banks can acquire and retain a large customer base while being profitable by following a data-driven approach. They have realized the value hidden in the unused data and analytics capabilities, making it the global trend for the retail banking industry in 2019.
(Source: The Financial Brand)
The Motivation Behind The Adoption of Data Analytics
The convergence of several technology trends is accelerating the progress of advanced data analytics. The volume of data is doubling every two years as information pours in from multiple channels. Data storage capacity has increased while its cost has gone down. We also have the computing power to crunch all this data generated at high velocity. In India, the BFSI sector has been at the forefront in the adoption of analytics which has captured around 36% of the market size.
(Source: IVY Professional School)
Banks who have started with the digitization are generating terabytes of data which is helping them identify new business opportunities and improve operational efficiencies.
They have started offering services which have taken the industry by surprise, few of the examples are listed below
YONO is a digital banking platform of SBI which was launched in 2017. The platform provides regular banking services and lifestyle-related services like cab booking and online shopping. In the month March SBI launched a cardless cash withdrawal facility on this platform for users.
HDFC has launched an AI-powered chatbot EVA. Customers can get information about various products and services without needing to search or make a phone call. HDFC also anticipates that soon enough EVA will be able to handle real banking transactions as well.
ICICI bank has been the front runner in deploying robotics software in its 200 business processes spanning across various functions. The software deployed is said to be following human actions to automate and perform repetitive, high-volume tasks. That leads to reduced response time to customers and increased productivity as well as efficiency.
These examples from leading Indian banks successfully showcase how data analytics and customer service can help retail banking functions. Further analytics can enhance banking capabilities, providing better customer insights and automation for back-end processes.
Advanced analytics has opened new doors for banks, also forcing them to investigate into new business avenue which is different from their existing business models. But even with the attractive outputs through advanced technology undertakings, banks have been unable to scale their efforts. These undertakings are not being scaled up and are still at a nascent stage, but it also implies there is significant untapped value creation potential.
If banks are willing to put considerable strategic and organizational efforts into analytics, it will become a true business discipline. Example, today banks cannot imagine their business without marketing and sales, gone are the days when marketing and sales were not actively pursued by the banks. They have become core business functions for banking as well.
Few Other Areas Where Banks Can Use Advanced Analytics
Advanced analytics can accelerate growth. Detailed customer profile combined with transactional data can improve customer acquisition, retention and cross-selling, most common example are banks using credit card transaction data to develop offers that encourage the customer to make more purchases from a specific merchant. It boosts banks commission, as well as providing value to the customer.
Advanced analytics can help banks in providing faster and more accurate responses to regulatory requests. This leads to enhanced decision support and better service to the customers.
Risk control techniques and prediction are one of the most important functionalities provided by advanced analytics. Analytics enables digital credit assessment, advanced early warning systems and credit collection analytics. With further advancements banks can reduce losses they incur through various financial frauds.
Analytics can greatly improve the digital banking experience for its customers. It enables customers to interact with banking activities through a wide variety of communication channels. By gathering real-time customer data analytics banks can understand customers better and maintain consistency in service delivery.
Challenges Faced in the Adoption of Advanced Analytics
The major challenge faced by the banks includes multiple competing priorities, functional silos, talent crunch, and inadequate data and system infrastructure. Many of them have experimented with advanced analytic techniques but have failed to attain desirable results.
Factors holding back from realizing the full potential of analytics
Most banks have legacy IT systems, which leads to fragmented data being collected from multiple sources and various business processes. These systems have a huge amount of data but extracting and processing this data proves to be a real deal-breaker.
Over the years traditional banks have gone through various mergers or acquisitions. This has created a Silo’ed business module, creating many hidden barriers to data extraction.
Data security breach is one of the major concerns for banks, which stops them from adopting new technologies easily.
Making it happen
To develop analytics competencies, it requires a strategic approach from leaders in the organization. They should identify clear goals for implementation of analytics. In future analytics will drive sustainable competitive advantage for banks. In an era with eroding product differentiation, diminishing customer loyalty and exploding volume, velocity, and variety of data, the extent of growth the banks can achieve will depend upon the how well banks beat these challenges and use the opportunities to their advantage.
For adoption of new technologies, the biggest hurdle is cultural change and banks which can manage this will be able to deploy analytics solutions much faster and reap handsome dividends.
This article was first published here