In the last few years, India has witnessed rapid growth big Data Revolution with the integration of AI, ML, iot in the financial services industry. The global fintech market is growing rapidly and is projected to reach around $309.98 billion by the end of 2022 with a CAGR of around 24.8 percent. According to a report by Boston Consulting Group, Indian fintech companies will be worth $150-160 billion by 2025. Many Indian companies have adopted a product-oriented approach, providing services based on AI that will help the financial sector generate new revenue potential, improve process automation, risk management, fraud prevention, resource utilization, customer retention and acquisitions. direction to use it.
hyper-personalized financial services
In a survey conducted by NTT Data of 5000 customers and 500 financial institutions in 8 countries, proactive personal and personal financial services were found to be on-demand. In revisiting and re-examining the interpersonal relationship between banks and their clients, it is hypothesized that modern client financial organizations may be able to expand themselves beyond their traditional roles of those enabling the more active role of a financial advisor. say for. To the same end, a multitude of customers personally confirm the sharing of their data (their social media activity, geographic location, financial history, goals and aspirations) in exchange for the Bank’s informed intervention in their financial purchases, discounts, savings guidance. Is. recommendations. However, it is important to note that the customer still has the power to override the financial institution.
“With assimilating AI” banking services, we will be able to bring mass-market human-based personalized services; This will help the customers to meet their financial goals as well as their social goals.”
–Michael Goodman, Vice President, Business Insights, NTT Data Services
Furthermore, when it comes to customer acquisition and retention, a significant portion of customers are willing to switch banks as technology develops, and as their needs grow. Shifting financial services to digital mode has also accelerated the process due to the COVID-19 pandemic. Increased mobility and agility are notable across all demographics. It is also important on the part of banks to establish market partnerships with non-financial services outside the organization. However, only about 14-18 percent of the institutions involved in the survey are actively participating in it.
“In today’s era of hyper-personalization and the concurrent rise of technological advancement, data analytics and artificial intelligence are enormously effective in attracting and retaining customers.”
–Neeraj Singhal, Senior Vice President, Head of International Consulting, NTT Data Services
parallel to technology
“It is not just technology, but a trio of regulatory developments, digital data infrastructure development and evolving consumer behaviour, all of which are coming together – along with technology to drive financial access, servicing and customer retention in the way of financial services. To position very strongly to change. Deliver and receive in the market”.
—Vijay Chandok, Managing Director and Chief Executive Officer, ICICI Securities
While technology has a massive impact on the financial sector, the regulatory landscape is also evolving from traditional methods to digital modes of customer acquisition. Moreover, there is an urgent need to furnish this sector with data infrastructure support to the same end. Both of these help in changing customer profiles as well as in technological advancements to revolutionize the industry. Although technological intervention and its impact are greater in the financial world, the nature of regulation needs to adapt to it. With the diversified functions involved by fintechs, there is a need to broaden the regulatory perimeter. With regard to infrastructural support, RBI has already introduced revolutionary products like IDRBT and NPCI. Efforts are also being made to increase the participation and interoperability of non-banking institutions. Customer participation is being encouraged by mitigating risk and offering low-cost and customer-friendly transaction models such as 2FA or AFA. RBI’s regulatory sandbox encourages systematic innovation while promoting efficiency. The Reserve Bank Innovation Hub is also working on creating an enabling ecosystem for academicians, technologists, finance experts as well as regulators.
Real estate, retail and corporate landscape
In examining the extent of automation adoption in the real estate sector, we can see a relative under-digitization. Even though cloud computing and basic AI-based models have been employed in India, and though we have already moved beyond Excel sheets, asset management is yet to be automated. Countries like the US are already deploying BIg data-backed AI to provide actionable inputs to buyers. In real estate, sales data is now increasingly being accessed by being something beyond just Excel sheets and PowerPoint. We just have to get that data. However, hopefully with the advent of 5G and the adoption of software-driven approaches, we will be able to enhance the customer experience and support. While we have access to technological innovation, there is still a need for improvement in the area of data analysis.
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It is imperative that there is greater adoption of technology and AI in the built world. While technology will further impact everyone in the built world, AI and Big Data will greatly improve project connectivity and help developers and other service providers target the right customer set.
–Vipul Rungta, Managing Director and CEO, HDFC Capital Advisors Ltd.
On the other hand, the implementation of data analysis is widespread in the retail sector. This field exemplifies cumulative innovation in the areas of customer segmentation, customer interaction, intelligent product recommendation, pricing, forecasting, inventory management, store labor optimization and even logistics. The introduction of simulation technology is another pioneering development. We can attribute this rapid progress to the standardization of processes in the retail sector and AI is well suited to handle any minor changes effectively. In contrast, the presence of a diversity of exclusivity and diversity is greater in the corporate sector, for which AI is less suited.
major development
With the amalgamation of AI in the insurance business, lending business, real estate and even equity businesses, the possibilities of AI are quite clear. It is now also possible to capture images of infrastructure through drones and access them in real time in the form of digital buildings in e-format. The introduction of payment systems like UPI, IMPS, eKYC, P2P lending and crowdfunding platforms is a step in the right direction. New concepts of customer self-service through smart apps have also led to advisory intervention. According to insights from Deolite, nearly 70 percent of all financial services firms deploy ML to manage cash flow, credit scores, data processing and contactless banking.
“When it comes to high-value transactions, customers want utmost honesty and even a broad time frame, while low-impact products are expected to be automated and instantaneous. ”
–Manish Madan, SVP/Head Digital Customer Service, Claims, Aegon Life
With the spread of digital, the cases of digital fraud have also increased significantly. In 2020, public sector banks saw a 234 percent increase in fraud cases, while private banks saw a 5000 percent increase. AI and AML are extremely beneficial in fraud management, especially in lending institutions.
road ahead mapping
Although already making progress, the Indian fintech industry is only at the starting point of AI adoption. Experts predict further breakthroughs in business analytics. Additionally, the prevalence of blockchain is also predicted to potentially reduce operating costs and reduce traditional bureaucracy. However, with the digital boom, issues of cyber security and data privacy need to be addressed and reconsidered creatively. Countries must address their legislative and regulatory deficits equally. The importance of data and data accuracy as well as efficiency is yet to be fully realized.