AI revolution in fintech: How India is leading the way

India has emerged as a global leader in Artificial Intelligence (AI) adoption and innovation in fintech. According to a report by India.AI, India has seen a significant rise in fintech investment, with about $35 billion invested across segments thus far, more than doubling India's share of global fintech funding since 2016. The success of our vibrant ecosystem of startups is significantly attributed to corporates, academia, government and regulators collaborating to foster AI development and deployment in fintech.

AI can be leveraged to democratize access to financial services for millions of Indians currently underserved or unserved by traditional institutions. Fintechs in India are using AI to create innovative products and services that cater to digital-savvy customers' evolving needs and expectations, thus enhancing competitiveness and resilience in the dynamic and disruptive global market. To understand why India is in the lead for using AI in fintech, let's break down some of its most significant applications, such as data sourcing, data structuring, scoring models, customer service, fraud detection and more.

AI for data sourcing 

From digitally lending money to large-scale data analyses, the use of AI and Machine Learning (ML) has brought fintech—an industry that covers the integration of finance and technology, to new heights, especially with the ever-enlarging spools of data that need to be processed. 

According to a report by Bain & Company, credit penetration in most living expenditure categories is less than 5% for households and more than 60% for MSMEs. This prompts both customer bases to rely on costly informal sources of credit. With AI, this gap can be bridged by enabling fintechs to source data from alternative sources such as social media, e-commerce platforms, utility bills, mobile phone usage, etc. These data points serve as insights into the creditworthiness, behavior and preferences of potential customers who may not have a formal credit history or a bank account. 

Previously, sourcing was driven by spreadsheets and manual calculations that were recurrent and tactical – extremely time-consuming tasks for sourcing professionals. With AI-embedded technologies, talent can focus more on strategy and less on menial operations. 

AI for data structuring 

Categorization or structuring of data using AI techniques like natural language processing (NLP), computer vision and ML is used widely to create comprehensive profiles and segments of customers. What's automated is data capturing and streamlining while supporting regulatory and compliance requirements. 

AI-powered lenders use advanced data analytics to assess the creditworthiness of borrowers, which helps in quicker approval of loans and offering better rates. These models examine a wide range of financial and non-financial data that are significant determinants in making loan decisions. Building on this form of structuring, fintechs and digital lending platforms can serve a more extensive customer base, especially those underserved by traditional financial institutions. 

AI for building scoring models

Based on data categorization, AI is aiding fintechs in building scoring models to assess the risk and return of lending to different customers. These models can leverage advanced algorithms and analytics to generate credit scores, interest rates, loan amounts and repayment terms tailored to each customer's profile and needs. It also supports monitoring the performance of their loans and portfolios, and identifying early signs of delinquency or default.

There is a heavy dependency on AI for developing scoring models as it serves as one of the main factors determining the customer's creditworthiness.  

AI for enhancing customer experience 

Artificial intelligence is being used extensively to enhance customer experience and satisfaction by providing personalized and convenient services. For instance, fintechs offer chatbots and voice assistants that interact with customers through natural language processors and deliver instant answers, guidance and support. Through robo-advisors, fintechs provide automated and customized financial advice and recommendations based on the customer's goals, preferences and risk appetite.

More importantly, AI is transforming traditional work processes by improving operational efficiency and security, automating various functions and tasks that are repetitive, time-consuming or prone to human error, such as document verification, KYC compliance, fraud detection, anti-money laundering checks, etc. On a compliance level, artificial intelligence supports fintechs to detect and prevent cyberattacks and data breaches using techniques such as anomaly detection, encryption and biometric authentication.

With more emphasis on fintech startups globally, India continues to lead the AI revolution in this space, with significant investments and collaborations driving its success. UPI's potential and success metrics have put India on the map for digital transformation, prompting other countries to partner and create similar instant payment models. And while AI stays at the heart of this success, let's continue to use it ethically and in moderation. 

The article has been written by Nageen Kommu, Founder & CEO, Digitap