Artificial Intelligence (AI) is not a science fiction notion. In fact, in a PwC study1 conducted in India, 44 percent of participants in the banking, financial services and insurance (BFSI) industry observe that that machine learning (ML), robotics and data analytics greatly impacts their business. If Banks and Non-Banking Financial Companies (NBFCs) wish to remain relevant, they need to embrace AI for financial inclusion and to become part of the disruption.
AI, with the use of ML algorithms, refers to a computer’s ability to learn human behavioural patterns and detect potential risks in order to predict the most likely behaviour while mitigating associated risks. BFSI industry especially, has benefitted greatly from implementing AI. Here’s how:
Financial institutions are constantly seeking to improve fraud detection measures. According to McAfee’s February 2018 report2, the cost of cybercrimes is roughly US$600 million (0.8 percent) of the world’s GDP. One of the most common type of fraud is loan stacking – that is customers taking multiple loans from different lenders. Alternative data based credit scoring companies like CredoLab today use AI to help identify these trends by simply flagging customers who seem to have apps from multiple loan providers on their phone.
In addition to this, by using trends from the industry and combining it with the trends of each organization, AI can also predict delinquent customers much earlier thereby reducing the cost of risk, band rates, and improve the overall business.
Customer Experience and KYC
AI and data analytics can be used by financial institutions to understand clients better. With human customer service agents, customers sometimes have lengthy wait times until an agent is available to take their call. Companies that use chatbots for resolving simple customer issues are able to satisfy multiple customers at once.
Also, pulling on individualized data through social media and smartphone metadata, financial institutions are able to analyse consumer behaviour patterns so that banks and NBFCs can tailor products and services to their specific needs. They can also determine the ideal product for each customer while focusing on risk-based pricing. This will increase profitability and customer loyalty.
Traditional credit scoring methods depend on historical credit information so persons being assessed would already need to have bank accounts and past credit. This method excludes the large number of persons with thin or no credit files. Also, this information does not guarantee that a person who is current in loan payments will repay a new credit facility. In India, many banks and NBFCs depend on credit scores from companies such as TransUnion Cibil, to assist with their decision making. However, non-payments and defaults still occur.
Utilizing alternative credit scoring methods, such as smartphone metadata with the use of AI and ML, will better predict consumer behaviour and spending habits which can help determine persons’ willingness to repay debt. This will lessen the number of bad debts, decrease turnaround for processing loans and increase overall profitability.
AI is the way of the future and has been adopted in fintech. Based on research from Mint3, the implementation of AI may potentially increase India’s economy by $1 trillion in 2035. However, AI adoption is still in its early stages in India. According to Accenture’s Banking Technology Vision 2018 report4, 79 percent of Indian bankers believe that within the next two years, humans and AI will work alongside each other in a more integrated way. In the interim, partnerships with fintech companies can fill this gap. AI and ML are growing globally and fintech companies are also on the rise. Banks and Non-Banking Financial Companies (NBFCs) in India need to embrace AI and ML for financial inclusion and to become part of the disruption if they wish to stay relevant in an ever-changing financial environment.
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