Pngme Insight 4: Savings

Published by Pngme on

Pngme provides actionable insights through its mobile SDK and unified API. Such insights are driving formidable growth and powering robust credit capabilities with the financial institutions in SSA. This week, we’re taking a look at Insight 4: Savings to give an idea of how our Insights are built and how they can work for you. Check out the below snapshot of Insight 4: Savings and learn about more Pngme insights through our handbook.

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Consistent savings is paramount to financial health. Underwriters want to see that a borrower has a steady flow of income, rare changes in expenses, enough cash saved to reliably make payments, and sufficient reserves when a borrower endures a financial shock, such as a dramatic reduction in income or an unexpected expense.

To normalize for cyclical variation observed in account balances (where local maxima occur from a pay-check credit and local minima come after substantial debits to pay bills and other debt obligations), we compute average_daily_ balance_90, the average daily balance over 90 days to approximate the most expected balance in savings over a 3-month time period.

Savings

Insight: Savings

Pngme’s API identifies that almost half of users have an average_daily_balance_90 of less than 5000 NGN. These users pose significantly greater credit risk as 75.7% and 65.7% of individuals in the <2K NGN and 2K-5K NGN buckets, respectively, are considered to be risky, unlikely to have sufficient funds to meet debt obligations and vulnerable to overdrafting on their accounts.

Overall Takeaways

Assessing a user’s savings is a great way to differentiate risk.

No Savings:

Users with a <5K NGN average daily balance (ADB)90, exhibit a pattern of using their bank accounts more like wallets.

Larger Savings:

Users with larger savings may be demonstrating different behavior from those with a low average daily balance and may be actively saving money.

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