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millions of unbanked individuals into financial systems .
Such forms of lending unlock a wide range of data on consumers demonstrating their eligibility for formal loans . For example , Creditinfo data shows that new banking customers are twice less likely to default when they have good history of mobile loans . This alternative data can then be used by financial institutions to ‘ graduate ’ underserved communities to formal credit , narrowing the financial gap .
Mobile phone transactions can also be an important indicator in estimating credit risk . As mobile wallets or banking apps allow millions to access banking services without having to visit a traditional bank branch , these services are facilitating the
Dmitry Borodin , Head of Decision Analytics at Creditinfo financial inclusion . Data from mobile and online transactions can inform banks and lenders on a customer ’ s income and cash flow , acting as a risk indicator for formal loan applications .
With a wealth of data available to FinTechs and financial institutions , it is now down to them to design solutions to process and analyse data which can inform loan eligibility . AI and Machine Learning models are proving crucial for this process with the ability to extract actionable insights from a wide range of unstructured data points drawn from these new data sources .
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