By Aalesh Avlani
With the Reserve Bank of India (RBI) releasing the National Strategy for Financial Inclusion at the start of 2020, it set the vision and objectives of financial inclusion policies that can ensure easy financial access and timely credit for low-income and vulnerable groups within the Indian population. This national financial inclusion strategy will complement the RBI’s move to issue differentiated banking licenses, all in an effort to bridge existing gaps in ensuring universal access to financial services. That said, the use of innovation and technology continues to be an important pillar in this journey and artificial intelligence (AI) technology has emerged as the leading facilitator of financial inclusion. Let us look at how AI is transforming the financial technology (FinTech) space and enabling financial service providers to leverage data in order to drive faster decision-making.
Driving automation and increasing efficiency of banking services
In a bid to provide banking access to citizens living in remote Indian villages, the RBI has stressed upon strengthening digital financial services and has been promoting the use of advanced technologies to improve the quality of services being provided. In that sense, the role of AI in revolutionizing how financial institutions like non-banking financial companies (NBFCs) are offering banking-like services is proving to be extremely important. Not only is AI helping new-age financial institutions in automating manual processes such as data entry and customer request management, but it is also providing unparalleled levels of customer support through the deployment of AI-powered chatbots that can simulate human conversation.
Moreover, by employing machine learning (ML) algorithms that are essentially a subset of AI technology, financial institutions are being able to streamline operations and automate complex tasks that involve problem-solving and decision-making. This is helping them to eliminate human errors and reduce the time taken to perform repetitive functions, resulting in significant efficiency gains that is lowering the cost of banking operations. As a result, financial institutions can tap into markets that were previously considered unviable, all the while delivering faster and more accurate services.
Advanced data analytics for accurate credit decisions and personalized experiences
With the potential to handle even higher transactional volumes than traditional banking models, AI-powered algorithms are helping financial institutions extract valuable insights from customer data, that too in real-time. This ability is helping them to augment their underwriting processes, in turn enabling them to leverage improved risk assessment strategies and provide credit to those who were previously ineligible. From a consumer perspective too, AI is enabling banks and financial institutions to offer personalized financial services by analyzing past customer behaviour and understanding their changing preferences. The increasing use of AI-powered virtual assistants in answering customer queries 24X7 while also providing personalized recommendations is just one example of this trend. As financial institutions integrate AI solutions across their suite of products and services, their ability to cater to diverse customer groups with differentiated offerings will help achieve financial inclusion goals at a faster rate.
Proactive fraud detection and prevention for better risk management
In situations where the level of credit risk is overwhelmingly high, AI-powered fraud management systems are helping financial institutions identify different types of fraud such as phishing attacks, identity thefts and even payment-related trickery. This has been made possible by the use of AI algorithms that can detect anomalies in financial transactions, thereby supporting them in sustaining business operations even in the face of increasing credit or cyber risk.
Moreover, unlike traditional credit scoring models that assign static weights to different criteria, AI-driven alternative credit scoring models factor in non-traditional data such as rent payments, e-commerce activity and even online behavioural patterns to accurately assess the creditworthiness of new customers. This in turn is improving credit scoring accuracy levels and empowering financial institutions to provide India’s underserved population with tailored credit products, encouraging them to participate wholeheartedly in the country’s burgeoning economy.
The author is co-founder, Credit Wise Capital
