Artificial Intelligence (AI), as one of the leading technological trends, continues to grow in popularity among marketers and sales professionals, and has evolved into an essential tool for brands seeking to provide a hyper-personalized, exceptional customer experience. AI-enhanced customer relationship management (CRM) and customer data platform (CDP) software is now available, bringing AI to the enterprise without the high costs previously associated with the technology. On the basis of exclusive interactions with leaders in the BFSI sector, Nidhi Shail Kujur of Elets News Network (ENN) explores how with constantly evolving technologies, the banking and financial services industry promises to exceed customer expectations.
Artificial Intelligence Aids in Customer Understanding
The banking industry is undergoing significant change, particularly with the spread of customer-centricity. We live in a world where the majority of people have access to the internet. The scope of digital transactions has expanded through various mediums.Banks have already begun to implement Artificial Intelligence (AI)-based solutions to improve their processes and provide a better customer experience. AI-powered processes more accurately assess customer credit histories in order to reduce future payment default. Using AI in these processes enables banks to make faster and more precise predictions about the risks associated with loan issuance, insolvency, and the threat of fraud. Banks and credit unions around the world are also incorporating software robotics into their business processes across a variety of functions. Unlike traditional data analytics tools, AI is able to predict client behaviour by continuously learning and improving from the data it analyses. This enables businesses to deliver highly relevant content, increase sales prospects, and improve the customer experience. Brands may acquire a much more precise picture of their customers by using AI and machine learning for acquiring and analysing social, historical, and behavioural data. Karthikeyan K, Co-founder& CTO Kreditbee, says, “I think there are three parts to that, there is personalization, interaction and context. Some of the things which we have already done are like, let’s say we cater to a large segment of Indian customers and they come from various diverse backgrounds. So personalization is definitely a must and here what we do is, we have different models which keep running in the background and decide what are the segments of customers, what are the products that we need to make available to the customers etc. So personalization is definitely important in the sense that we decide what the best fit of products is for which type of customer and definitely there’s a learning process as the models keep getting improved”.
On this Nikhil Bandi, Chief Technology, Digital & Operation, APAC Financial services Pvt ltd adds, “APAC finance which is a purely MSMe focused lending institution where we focus the areas like tier two, tier three, tier four cities which is a trillion dollar market in terms of financial need of finances. The customer expectations also are changing along with our dire need of changing the customer experience based on multiple technologies. When we talk about AI it has multiple used cases related to conversational AI. I think it’s very important when a conversation is hit with a potential customer or a prospect, a conversation is hit with an existing customer of yours, or a conversation which is hit with a customer who thinks that x amount of your services were not great. I think amalgamation with a new generation coming in an amalgamation of a formal and informal conversation. This is an expectation which is growing and coming in India”.
Financial firms may use artificial intelligence to analyze and manage data from multiple sources in order to provide valuable insights. These innovative results assist banks in overcoming the challenges they face on a daily basis while providing services such as loan management and payment processing. On this Vasudev Sharma, VP- Platform engineering Upstocks comments, “When we started experimenting with AI around two years ago, there were two areas where we thought that AI could have more value. One was operations where the customer on boarding happens, there’s a lot of documentation and a lot of verification that needs to happen. The second aspect is the customer’s journey or the customer experience itself when they are getting on boarded. In terms of operations, straight numbers Upstocks saved roughly 60%. Our efficiency improved a lot by just implementing the right level of the tech that we are talking about is the NLP. Second aspect that really helped Upstocks is with the customer support. Our customer support right now is heavily dependent on NLPs especially the chatbots that’s where we heavily use AI. I think there will be a point where 80-90% of your queries could be easily handled by chatbots and very rarely you would probably get down to an agent level thereby ensuring that the customer data is completely secure, increasing the overall efficiency and reducing the overall overhead into a system”.
Leveraging Data at a Massive Scale with AI in Banking
The banking industry has always been built on data. What has changed in recent years is the amount of data available, the speed with which it is processed, and the need to respond quickly to market changes. To thrive in today’s ecosystem of fintech and other players, banks must constantly innovate by transforming their data from a cost center into an asset. AI Chatbots (also known as bots) are modernizing customer banking services. AI bots can assist customers with day-to-day queries and provide 24*7*365 service. AI-powered chatbots can provide users with a more personalized experience, as well as a more accurate and faster response rate. According to a Juniper Research survey, banking-related chatbot interactions will increase by 3,150 percent (from 2019 to 2023), and banks will save approximately 826 million hours through chatbot interactions by 2023. AI chatbots for banking and finance operations are already positively influencing customer retention and optimizing service quality. Rishabh Garg, CTO, U GRO Capital says, “In today’s world if a BFSI organization has to compete then it cannot run like a 9 to 5 pm shop. Customer centric is the key for success and with bots; organizations can keep their brand available 24×7. Also, Customers can ping their brand anytime, so bots have now evolved a lot in the last few years. Conversational base bars reduce the gap between a human touch point and an automated rule based dummy bot, so customers can now chat in their natural language and using natural language processing tools bots can translate customer queries into actionable requests. We are now working on building solutions for multiple use cases of bots across the entire life lending life cycle”. One of the most significant barriers to Enterprise AI in banking is not the implementation of machine learning models or even the creation of the models themselves. Rather, it is data management, which (while appearing simple) is critical to enabling the organization to leverage data from the ground up, democratizing data use across teams and roles. Banks do not have to start from scratch in order to embark on the journey to Enterprise AI. Many of the pieces are already in place, including staff across roles and business lines that are already using data to make day-to-day decisions.
Remote working and its challenges to the BFSI sector
Banks needed to upgrade their existing infrastructure or build new ones to meet the needs of their customers and employees in the ‘new normal.’ They needed to ensure that they could handle the increased volume of digital transactions without compromising security. They also needed to make it possible for their remote employees to work efficiently and without interruption. To put it another way, they needed to go through a new wave of digital transformation in order to survive in the new environment. Ravi Sundarajan, COO, Gupshupmentions, “Talking about payments, we worked very closely. Whatsapp as you know allows for P2P payments but not A2P payments. So we worked with a few payment providers and vendors and created A2P payments and we also kind of won the NPCI grand challenge for enabling feature phone payments. We’re working with RBI to try it and a few other things, kind of enable it on feature phones itself. We did i think 100 billion dollars’ worth of transactions in december which was i think about 5 billion UPI transactions and it’s growing a lot every month”.
Banks are now digitizing all of their data, processes, and remote servers in order to meet market demands. Digital technologies such as Machine Learning (ML) and Artificial Intelligence (AI), which assist banks in accessing this data and processing and analyzing it in real-time, are becoming more prevalent. However, increased digitisation brings with it more cybersecurity challenges, which are becoming increasingly amplified over time. Balaji TK, CIO,Orange retail finance states, “Everything is an online journey for me with the customers right now. This entire journey has helped me to reduce the timeline of the onboarding activity. Since the platform is building the complexities of the data, that keeps increasing so making it much faster. The AI comes into play where you can capitalize the multiple things.The business objective is one set of things .The security that i can see whether there are any gaps in my entire loan onboarding process, whether there are any anomalies agencies in the huge set of data because once they transfer money that’s it, end of the day”.
The number of digital transactions has increased dramatically as a result of the pandemic, and banks must ensure that these are secure so that customers continue to place their trust in the system. However, when BFSIs adopt a hybrid work model, they must also prioritize connectivity and security at the employee end to ensure uninterrupted work. As the number of UPI transactions grows, the infrastructure must be upgraded, scaled, and made more secure.
Artificial Intelligence’s Role in BFSI: Smartly Improving Customer Experience
The sheer power of artificial intelligence has enabled banking and financial services organizations to provide more seamless, innovative, and delightful customer experiences. AI-powered technologies such as Optical Character Recognition enable faster customer onboarding by auto-filling forms by extracting user details from uploaded identity documents. AI enables accurate and comprehensive credit risk profiling by combining multiple data points from various sources, such as income statements, employment history, credit score, and bank statements.
Speed, agility, and flexibility
AI provides unparalleled speed, agility, and flexibility. Unlike the traditional banking system, which required a lot of paperwork, the use of AI has resulted in a contactless, paperless system that can draw up any information and serve the customer in seconds. All activities that previously required a trip to the bank can now be completed from the comfort of the customer’s own home. Customers, for example, have found it very convenient to digitise the KYC process by using AI-based OCR to verify uploaded documents and NLP to process forms using this information. Fintech lenders are also using this method to process loan applications. Dominic Vijay Kumar, VP& CTO , Art housing finance India ltd says, “We work mostly in tier 3 and tier 4 cities where customer interaction is very important. When you look at scalability by using AI, it has really helped me in my complete digital journey from the loan origination up to the disbursement journey. Especially when you look at the scalability, the credit part is the place where the maximum amount of time is taken to sanction any loan, especially in the secured loan segment because the kind of customers we have, the kind of the financial statement, the kind of credit scores and analyzing everything and getting a very fast sanction clarity becomes a challenge for us. Where we use a lot of AI tools like, we have tied up with some good technology partners like Experian, griff also because it gives a very good data which is related to more of my business”.
Artificial intelligence enables a system to understand a customer’s needs based on past activity and data and convert it into insights. This will assist the system in better addressing the customer’s issue. In fact, artificial intelligence in conjunction with edge computing processes data in real-time and provides actionable information.
AI has made breakthroughs in across industries, from everyday transactions to customer service. Natural Language Processing (NLP) and Predictive Modelling are two technologies used in conversational AI that have an impact on customer experience. Bank loans are approved in just two days (with AI) instead of the usual 10-15 days, significantly improving customer satisfaction. Understanding the needs of the customer is a prerequisite for providing better service. Today’s fintech platforms provide their customers with a plethora of options in the form of tailored offerings that cater to the customer’s specific needs. The customer has a variety of options, whether it is the range of financial products available or the methods of repayment. Some payment companies are increasingly utilizing AI to provide their customers with a more personalized payment experience. At the time of checkout, AI is used to analyze previous payment patterns and prompt the preferred payment option that is the best fit for the expense. If a customer consistently prefers the EMI option for large-ticket purchases, the system will automatically make the best EMI option available to them.