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AI can do a lot for financial services penetration in the hinterlands

The success of mobile or agent banking to financial inclusion depends on the level of financial literacy

AI can do a lot for financial services penetration in the hinterlands

By KUSHANKUR DEY

Branchless banking, either through agent or mobile route, is claimed to be instrumental in driving financial inclusion. Safaricom’s M-PESA (a subsidiary of Vodafone) in Kenya unleashed the utility of agent or mobile banking in 2007–08. A similar sort of business model has been adopted by many developing and least-developed countries. India is no exception.

As per Micro Save 2019, mobile banking penetration—recorded in terms of the number of transactions—was 513 million followed by agent banking, and bank ATMs (80 million) and bank branches (28 million) between 2005–18.

The departure from a ‘brick-and-mortar’ structure to branchless banking (agent & mobile) can be attributed to ICT adoption by users. This has led to a structural and technical reform in the financial services landscape in the last decade—by partnering the mobile network operators with financial services providers—which has allowed adoption of low-cost solutions to deliver a slew of financial services to the unbanked.

It is well-known that e-commerce has impacted the quality of financial services and expanded the scope for scalability and increased operating efficiency. Nonetheless, inclusiveness in institutional finance access remains an area of concern as about 54% of population is yet be into the fold of formal financial institutions (NABARD, 2018). RBI (2013) reported that 90% of small businesses have no links with formal financial institutions and 60% of the rural and urban population do not even have a functional bank account.

While (bank) account opening by a percentage of poor is a a yardstick to measure the degree of financial inclusion, access to financial institutions and appropriate use of bank accounts for accessing various financial products or services, namely saving, credit, investment, insurance, etc. ascertains the extent of financial inclusion in true sense. Nachiket Mor Committee (2010) appointed by the Reserve Bank of India suggested a differentiated banking structure (vertical and horizontal) to meet up the demand for financial services.

A financial inclusion measuring scale known as ‘global findex’ developed by the World Bank in 2010 and revised in 2015 has already been replicated by many scholars in India. Their findings suggest that the determinants of borrowing like formal education, gender, age, demography can affect the extent of financial inclusion and can be strengthened through branch-less banking. In such scenario, bank could appoint individual agents or organisations (for example, FINO fintech foundation) as business correspondents (BC). To prop up the extent of financial inclusion, the penetration and success of agent banking is counted on trust fostering and customer relationship, quality of service delivery, customer protection (data security and localisation), and cash availability.

Two mobile or digital banking models have evolved to drive financial inclusion.

Model I: Financial service providers, say micro-finance-NBFCs are tying with mobile network operator for rendering mobile financial services; for example, Musoni is the first completely cashless microfinance institution in the world wherein customers receive and repay their loans via Safaricom’s M-PESA system.

Model II: Financial service providers are partnering with payment banks to leverage branch or access points. Aadhaar-enabled payment system (AePS) supported by the unified payment interface plays a pivotal role.

The success of mobile or agent banking to financial inclusion depends on the level of financial literacy. This can improve risk-sharing among rural communities and increase their demand for financial services, reduce economic volatility, improve intermediation, and fructify overall financial development.

Digital payment and credit system will drive financial inclusion and make new business model scalable, robust, and secure. Machine learning and artificial intelligence can do a lot for financial services penetration in the hinterlands and extend the drive of banking the unbanked.

The author is Assistant Professor, IIM Lucknow. Views are personal

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