The global pandemic has brought unprecedented challenges to the BFSI industry, with the closure of branches, reduced face-to-face interactions and a shift towards remote working. As a result, digital transformation has become a key priority for organizations to stay competitive and maintain customer engagement.
Digital transformation in the BFSI industry has been accelerated by the pandemic. Many banks and financial institutions have increased their investment in digital technologies such as online banking, mobile banking, and chatbots to provide customers with convenient and safe access to financial services. This has not only improved customer satisfaction but also reduced operational costs for banks.
In addition, the use of data analytics has become more important than ever. Financial institutions are using advanced analytics to better understand their customers and to identify potential fraud or risk. This allows banks to make informed decisions and offer personalized services to their customers.
Alex George, Country Manager, India & South Asia, Riverbed Technology, says, “At various points in time, different solutions have been acquired to address different challenges, and it’s difficult to find an enterprise today that doesn’t have at least five to six, or more, tools or visibility solutions examining various network and application ecosystems. This is particularly relevant in the insurance industry, as these solutions have a significant impact on business operations.”
He further stated, “In each of the domains or verticals, there exist distinct challenges that are unique to that particular industry, with some overlapping with others. For example, in the banking industry, an organization like the Access Group, which includes Access Securities and mutual funds, may face challenges related to cross-functional customer services.”
“While there may not necessarily be infrastructure or regulatory requirements dictating that these entities remain separate, customers who use both banking and securities services may experience performance issues when attempting to execute transactions across both platforms. As such, at some point, these ecosystems will merge, requiring a comprehensive approach to ensure the smooth functioning of the end-to-end user experience,” he concluded
Prashant Thakkar, Chief of Operations & Technology Officer, LIC MF, says “During the transformation journey, one faces varied challenges. The biggest challenge arises when you are in the process of transforming from a traditional system to a new one, including setting up new infrastructure and applications. The issue is how to ensure unified visibility despite the existence of silos created by different solutions and time periods. The challenge lies in bringing these silos together and making them function as a single system.”
He stated, “Our challenge lies in the fact that we have a vast amount of data scattered across various sources and purposes. The task at hand is to gather this data and make sense of it to overcome the challenges we face. Improving visibility could help alleviate these challenges as the current main challenge is reducing the downtime window. In this scenario, data can play a crucial role by providing the necessary visibility.”
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“We have lots of data, but the problem is it is lying in different pockets, used for different purposes. So, it’s all about bringing that data back. So, bringing everything together and making sense out it to move away from the challenge, moste of the time goes in pinpointing,” he concluded.
Deepa Vegesina, Chief Digital Officer, Revfin, says “To effectively analyze data and identify valuable data points, one should not limit themselves to a specific use case. By expanding your data collection beyond your current use case, you can gather more data points to incorporate into your modeling and reduce visibility blind spots.”
He stated, “When you focus only on data specifically related to your current use case, you restrict your potential and may miss out on valuable data that could be used. This could happen if you don’t consider data from sources like video or online gaming that may seem irrelevant at present but could be useful in the future.”
“It is important for everyone involved in data analysis to consider larger data sets and not just the data sets they are working with. This includes analyzing data from any related sources. If you want to look ahead and look at data, the data point, you should not restrict yourself from the data point from your use case. The minute you go outside of that use-case, that is when you can actually get more data points that you can build into your modeling and then reduce the visibility blind-spots. The more you concentrate on getting data exactly pof the usage you are doing, you gets restricted. And you might find it that you are going to hit that data set 5 years from now, but actually there is enough data around that you could already use, because you did not think that the data coming from video, online gaming etc is going to be useful to you,” he stated.
He concluded, “AI has been existing for 15 years now, and whenever you switch websites the cross selling happens, and Amazon has been cross selling you from 3 years now. The whole point of Chat GPT is that suddenly the visibility we are talking about has come to the common man. AI has been around for 15 years, and during that time, cross-selling has become a common practice on websites. For example, Amazon has been using cross-selling for the past 3 years. With the advent of ChatGPT, the visibility of AI has become more accessible to the general public. This means that the benefits and capabilities of AI are now more widely understood and available to a wider audience.”
