In this digital age, Smartphone usage is about as common as breathing air. Most persons have access to a mobile device which they use for daily tasks and transactions. According to a study by the Federal Deposit Insurance Corporation (FDIC), easily accessible variables from the digital footprint equal or exceed the information content of credit bureau scores. Digital footprints thus have the potential to boost access to credit to parts of the currently two billion working-age adults worldwide that lack access to services in the formal financial sector, thereby fostering financial inclusion and lowering inequality.

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With the increasing concerns around data privacy and the evolving data protection laws being implemented by regulators, banks and Non-Banking Financial Companies (NBFCs) can safely tap into the superior metadata. Metadata refers to the non-personal, binary (1s & 0s) data of the device. For example, instead of giving details on who the calls were made to, metadata only suggests how many calls were made at what time of the day and so on. Smartphone metadata is therefore the most important pool of customer intelligence available today that doesn’t compromise with the users’ privacy.

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Accuracy of Smartphone Metadata

According to DataReportal, in January 2019  87 % of India’s population  had mobile subscriptions and 38% of the population used internet on their phones. The report also states some mind boggling figures on the way users use their smartphone which goes to prove that there is a lot of data that can be collected from smartphones which can give a truer picture of consumption. In other words, mobile data captures behavioural data which assists in predicting not just a user’s ability but also the user’s willingness to repay debt.  

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Benefits of Using Smartphone Metadata with the Help of AI

Alternative sources of data have been used for a while in the industry for mapping the creditworthiness of customers. However, the benefits to utilizing smartphone data with the help of AI are much more far reaching than sources like social media or psychometric data that are intrusive and highly inaccurate (to say the least). For India especially, smartphone metadata is the best way ahead to achieve financial inclusion and overall profitability. Here’s how:

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Scoring the Unbanked: In India, not everyone has a bank account and all transactions are done mostly via cash by them. According to the Global Findex report by World Bank, in 2017 one third of the adult population remained unbanked and based on the Pradhan Mantri Jan Dhan Yojana (PMJDY) progress report in March 2019, only 35.65 crore bank accounts have been opened under this initiative, implying that there are still a large number of unbanked persons. These persons would not have any credit score with traditional bureaus. The only way for them to have access to credit, is to be scored using alternative credit scoring methods. Given the high mobile usage, smartphone metadata becomes perhaps the only reliable substitute.

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Increase Profitability: Lenders gain access to a new market or segment of unbanked persons. In India, this is an additional one-third of the total adult population. This may lead to an increase in profitability in the long-run, since these new customers will be able to benefit from borrowing funds that they would otherwise not be able to access

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Increase Efficiency: Advanced AI allows metadata to be processed within minutes, which significantly reduces the wait time for risk assessment. This increases the lender’s efficiency and reduces the cost of risk. In a report by financial research firm Autonomous, it is projected that financial institutions will be able to reduce costs by 22 percent across front office, middle office and back office functions by implementing AI driven technology to smarten business processes.  

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Increase Predictability: The biggest power of using Ai based algorithms in scoring is that the more data you feed into it, the smarter it gets. Smartphone metadata analysis using AI will not only product highly accurate scores in real-time but predict future occurrences like flagging customers with high probability of fraud and so on.

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Improve Customer Experience: Based on data from Accenture, 63 percent of customers want tailored products. The real-time scoring of customer will greatly reduce time-to-yes thus improving the customer journey for credit applications. In addition to this, the AI algorithm also indicates which are the other possible products that fit the customer’s profiles, thus improving upselling opportunities by giving the customers exactly what they need.

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Conclusion

In India, NBFCs and fintech companies are focused on the under-served customers, those viewed as riskier by traditional banks. Nowadays, these non-banks are leveraging AI, ML and data analytics to predict consumer behaviour by using smartphone metadata.  Smartphone metadata coupled with AI allows both the lender and consumer to mutually benefit from its usage by increasing the lender’s target market and profitability and also the consumer’s ability to receive credit and other financial product offerings easily and at fair terms.

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