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Credit risk management models for the new normal

June 29, 2020 in Data Analyics



Credit risk management models for the new normal

In our last article, we highlighted the economic effects of the ongoing COVID-19 pandemic, the oil price shock and how both factors have heavily contributed to Nigeria’s looming recession. The virus’s global impact is in turn directly impacting Nigeria’s macroeconomic stability.

Chinese imports resold by small businesses (most of whom are sole proprietorships) dominate Nigeria’s informal markets. Such businesses are a source of livelihood for over 90% of productive Nigerians. Supply chains have been interrupted for months because most of these Chinese factories shut down production. Other than the reduced availability of Chinese products for resale by Nigerian SMEs, the other impact is coming from the lock-down in business activities like meetings, weddings, religious gatherings, which have severely impacted on the gig economy. 

Businesses have also been forced to close, resulting in cost-cutting measures like layoffs and reduced wages for workers. These actions have repercussions in all sectors of the economy. This inevitably translates into both supply-side (output reduction) and demand-side (reduced purchasing power) challenges. 

Individuals and businesses who ordinarily would not have had cash-flow problems, now face the stark reality of missing their loan repayments, seeking payment holidays and searching for additional credit options to offset debts. This has prompted the Central Bank of Nigeria to encourage financial institutions to take extensive measures aimed at supporting Nigerians through this period of uncertainty. 

Data Analytics Will Be Essential

In the face of massive unemployment and reduced purchasing power, lending opportunities still exist. The potential spike in credit delinquencies does not preclude value within existing and new customer segments. Technology and alternative data can help lenders underwrite people whose credit histories have been ‘dented’ by repayment delinquencies as a result of the pandemic. Specifically, alternative data points like income and transaction data can be leveraged to help lenders identify customers who have experienced income shocks as a direct result of the pandemic. This is the time for alternative data to provide an alternative picture to support the work traditional credit registries have been doing to support credit risk management. 

Alternative data sources like customer transaction data, social media data, utility payments history, etc. can help lenders gain powerful insights on borrower profiles. This will give lenders a clearer picture of consumer creditworthiness, spending capacity and financial behaviours.

Conclusion

The current crisis highlights the fact that lenders need to determine the best options for their immediate and long term business continuity needs. Those who leverage data analytics, and scale intelligently will emerge with strengthened customer relationships and their reputations intact. Those who do not may find themselves exposed unnecessarily to the economic impact of the pandemic and the new normal it is creating…

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