Traditionally, credit is extended to customers based on a credit score. Lenders such as banks, credit card companies and other financial institutions assess creditworthiness using information from credit bureaus and their own databases. The traditional credit-scoring process usually verifies customers’ identity and assesses their ability and willingness to pay. The ability to pay is based on current income and outstanding debt, and willingness to pay is based on past credit performance.
In emerging economies such as the Philippines, the traditional credit-scoring process can be a barrier to accessing credit, especially for the lower-income segment. Their ability to pay is a challenge to establish because many of them do not have regular fixed wages. Instead, they are often self-employed or engaged in different income-earning activities that do not have consistent cash inflows. They are usually paid in cash, with little to no formal savings accounts or registered assets that can be used as collateral. Similarly, their willingness to pay is also difficult to assess because they do not have records of past credit performance and borrowing behavior. Their segment is the so-called unbanked — people who are not served by a bank or a similar financial institution.
The traditional credit-scoring process creates a cycle that can be limiting to the lower-income segment. They lack financial records to establish creditworthiness caused by little to no opportunity to secure credit or access to other financial tools necessary to secure a loan or save money. Ironically, for them, access to credit is especially critical. It can provide them with the instrumental opportunity to get an education, start a livelihood, or purchase a house. Without loan grants, they end up relying on informal and costlier alternatives. According to the Bangko Sentral ng Pilipinas third quarter 2018 Financial Inclusion Survey, only 3% of adults with outstanding loans actually borrowed from a bank, while 39% borrowed from informal sources.
From the lenders’ point of view, expanding the coverage of possible borrowers can be a growth area. To reach the unbanked and underbanked, financial technology (Fintech) companies are now developing approaches to credit-scoring by looking beyond the traditional credit bureau databases and using other available sources of information.
One of the more promising data sources is mobile phones. Mobile phones are available, accessible and replete with valuable information that can be used for credit-scoring. This is particularly true in the Philippines as in 2017, the International Telecommunications Union identified the country as having 110.4 mobile-cellular telephone subscriptions for every 100 people. In fact, the National Telecommunications Commission (NTC) identified a total of 120 million users and 96% of them are prepaid subscribers.
Mobile phone usage is presumed to be a good indicator of the user’s lifestyle and economic activity. Simple inputs such as the way users organize their contacts (e.g., first name and last name) and structure their text messages (i.e., grammar and punctuation) can be used as data points in the credit-scoring model. More complex data points include analyses of location movements and call detail records, among others.
Mobile phone data also shows location movements, which can be used to infer the users’ frequent locations, such as their home and their workplace.
Location movements also provide insight on employment, modes of transportation and frequency of travel. Call detail records connecting individuals who contact each other result in social networks that can be indicative of age, gender, economic status and geography. These records can additionally be used to infer a user’s socioeconomic class due to homophily, or the individual’s strong tendency to associate with others whom they perceive as like themselves in some way. This is actually the same concept that one of Facebook’s patents anchors upon. The United States Patent and Trademark Office granted Facebook a patent on technology that determines users’ creditworthiness based on their social network connections — where after taking the average credit rating of the user’s social network into consideration, a lender can either proceed with or reject a loan application.
While the long-term predictive power of using mobile phone data for credit-scoring remains to be seen, it promises to be an alternative or complement to an existing process that is worth exploring. A person’s digital footprint is difficult to manipulate, although not impossible, and provides a more holistic view of the customer’s socioeconomic activity compared to traditional credit reports. It makes the credit-scoring and risk-profiling processes simpler and faster through the input of the customer’s mobile phone number, where results can be generated in a matter of seconds.
For lenders, embracing credit-scoring alternatives can boost profits. Reaching more customers can increase revenue and new technologies can reduce costs in the long run. Valuable insights from these alternative data sources can also enable cross and up-selling of products.
However, crucial to alternative credit-scoring are data privacy and banking regulations which influence how Fintech companies and creditors obtain, analyze and use information. Prior to implementation, the process must be configured to applicable laws and regulations to ensure compliance. Part of the considerations would be ensuring that customers provide consent and authorization to creditors and Fintech companies in obtaining data on mobile usage from telecommunications companies.
Mobile phones are both convenient and accessible. Customers may not have credit history, but they have mobile phone records. Converting data from digital footprints to financial track records and creating meaningful credit insights out of them can be a powerful tool. It provides opportunities to lenders to offer the right products according to the customer’s needs, enable them to make good financial decisions, provide access to credit to a larger segment of the population, and move toward an inclusive financial system that meets the needs of all income levels.
This article is for general information only and is not a substitute for professional advice where the facts and circumstances warrant. The views and opinion expressed above are those of the authors and do not necessarily represent the views of SGV & Co.
Leslie Anne G. Huang is a Senior Manager from the Financial Services Organization of SGV & Co.
