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Adapting a Lending Business for the New Normal

June 24, 2020 in Data Analyics



Adapting a Lending Business for the New Normal

The COVID-19 pandemic brought with it, a global oil price shock and depressed consumer and business sentiments which have led to severe economic dislocations. For Nigeria, a second recession in four years is on the horizon. By the International Monetary Fund (IMF)’s estimation, Nigeria’s economy is expected to shrink by 3.4 percent this year and Africa’s most populous nation could face a recession lasting until 2021.

The impact of COVID-19 on the Nigerian economy has been mainly two-fold. There has been a slowdown in trade, investment and project finance, considering that Nigeria’s largest and most important trading partners are China and India – who are on lockdown. Also, the plunge in oil prices has had a devastating impact on Nigeria’s macroeconomic stability. 

Data from the National Bureau of Statistics (NBS) reveal that Nigeria’s inflation rate increased by 12.34% (year-on-year) in April 2020. This is 0.08% higher than the rate of 12.26% recorded in March 2020 and the highest rise in 24 months. The health, social and economic implications of the pandemic-induced lockdown have hit hard, creating negative consumer, business, and investor sentiment.

A combination of all these will see lower government revenue and likely lower spending. This will translate into many states having difficulty meeting salary and pension obligations. Capital projects will be suspended and contractors owed, negatively affecting aggregate demand. 

Realigning lending business model for the new normal

Lenders will need to protect their customers and themselves in these uncertain times. Given the social and economic realities of the new normal, lenders will be required to constantly take decisive, timely and data-driven decisions to manage their credit exposures through the crisis. Precisely, lenders will need to take a more granular and contextual approach to credit risk assessment and management.

Firstly, with the onset of COVID-19, social distancing measures pose a big challenge to the traditional ways of lending i.e in-person customer visits, assessment by loan officers/agents and the filling of lengthy forms. Lenders can react to this proactively by leveraging digital technologies to leapfrog these constraints and service customers directly. This approach will perfectly balance the innovation needed to keep a business running in a crucial time like this and the efficiency required for a scalable lending business model.

The risk management systems of lenders require a re-think. This means each organisation’s underlying processes and procedures will need to embrace the realities of a pandemic of this scale and adjust accordingly. The methods used to verify income for certain customer segments may also no longer be relevant and need to be revisited.

Lenders can use the customer data available to them to offer a model of lending that is faster, more cost-effective and transparent. By collaborating with advanced data analytics platforms to assess transactional and alternative data, lenders can gain a much deeper understanding of individuals and businesses. This, they can put towards automatically assessing creditworthiness, evaluating risk easily and issuing out quick loans to qualified leads on-boarded seamlessly from both mobile and web. This can be done with zero customer touchpoints and paperwork.

On the way back to recovery, many businesses and individuals will need fresh liquidity in order to grow. A large chunk of these businesses and individuals are new-to-credit. This segment does not have a past credit history so lenders have traditionally been cautious with lending to them. However, in this new normal, this segment may be too big in size for any lending institution to ignore. Thus, lenders will need to find alternate ways to evaluate creditworthiness of new-to-credit businesses and people. Lenders will also need to understand what data is leveraged to make credit decisions. 

With advanced data analytics that can categorize customer financial transactions, lenders can extend credit beyond their traditional markets, as they are able to deploy risk models to assess the credit worthiness of a wider market. Consumers also get the option to maximize the credit opportunities offered by lenders. From the customer’s point of view, the credit approval process becomes more seamless, with reduced turnaround time, which results in superior customer experience.

Conclusion

Lenders that respond to immediate priorities of the social and economic challenges of the pandemic in an efficient and effective way can actually build significant enterprise value. Now, more than ever, it is vital that credit markets coverage is expanded to cover especially SMEs and individuals who require credit market support at this time. Lenders will need to adjust their risk models accordingly to cater to the specific risk profiles of these market segments.

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