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Alternative Data can scale Solar Energy Pay-go Systems

June 19, 2020 in Data Analyics



Alternative Data can scale Solar Energy Pay-go Systems

In a previous article, we established the socio-economic impact of the energy access problem in Nigeria, identifying that this presents an opportunity for the country to diversify its energy mix through solar home systems and solar energy systems. We also identified that the high price point for these solutions might pose a steep challenge, given the economic profile of those who currently don’t have access to energy. We noted that the Pay-as-you-go (PayG) system offers an opportunity to scale this price barrier and highlighted that credit bureau data might be insufficient for the needs and realities of a renewable energy credit business model. The reason for this is that traditional credit registries largely do not cover the market segment without access to electricity. We then made a case for using alternative data to develop risk management frameworks for a renewable energy PayG model.

This article places a focus on alternative data as one way to go about the risk assessment of the segment of the Nigerian population not currently covered by the credit registries. The World Bank places Nigeria’s private credit bureau coverage (% of adults)at 13.9%. However, this does not seem like a true reflection of credit activity in Nigeria. A substantial amount of credit activity is channelled through the informal sector i.e loans outside regulated financial institutions. In fact, according to the IMF, Nigeria’s informal sector accounted for 65% of the nation’s GDP in 2017. Mobile penetration rates also tell a familiar story. Research shows that as of 2019, Nigeria had 172 million mobile subscriptions, and at a mobile phone penetration rate of 87%. What this infers is that potentially, a higher number of Nigerians are involved in financial, and consequently credit activity, than what is currently captured by existing credit bureau data. 

Creditworthiness assessment is all about data

Innovations in big data and analytics now make it possible to underwrite successfully as mobile phone data can predict a person’s capacity and willingness to meet their liability commitments. Customer transaction data can also be effective in fraud and risk management. This can be an effective means to identify and assess creditworthiness for the millions of Nigerians who do not currently have access to electricity.

Some of the useful data points include telecom/recharge history, bank data,  utility payments/consumption data, scheduled payments, social media activity, location-based data, phone calls, texting connections and more. How a user interacts with the input fields on a credit application process can also be quite predictive in determining user profiles. Access to these kinds of alternative data in real-time can be leveraged to give customers unbiased credit scores and personalised interest rates.

Conclusion

Risk assessment is made difficult for renewable energy providers due to the credit coverage challenges identified above. Data analytics makes it possible for providers to meet the need and demand for electricity, irrespective of economic class or availability or not of credit bureau coverage. This will ultimately give more Nigerians access to renewable energy solutions while empowering providers to deploy an efficient and automated risk management framework.
KliQr has built big data computing capabilities that can process alternative data to build customer profiles that are critical for credit decision making. You can speak to us for more information on how we can support your growth ambitions.

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