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Managing customer payments is becoming increasingly complex in the rapidly evolving Energy and Utility (E&U) sector. Economic uncertainties, coupled with higher unemployment rates, have resulted in escalating customer defaults. According to the National Energy Assistance Directors Association (NEADA), one in six US households is behind on their energy bills. By December 2023, the national arrears reached a record USD 20.3 Billion, up from USD 17.7 Billion earlier in the year. In the UK, regulatory bodies are taking proactive steps, including raising the energy price cap to safeguard suppliers from financial collapse.
E&U providers are additionally challenged by stringent consumer protection regulations designed to minimize environmental impact, protect vulnerable customers and ensure safety compliance. These challenges are compounded by certain customer segments exploiting loopholes in E&U processes, leading to increased defaults and revenue losses.
In this constrained environment, how can E&U companies mitigate bad debt write-offs? This paper outlines a three-pillared framework leverages predictive analytics, Generative AI (Gen AI), speech analytics and income-qualified customer strategies to enhance collections, minimize bad debts and foster customer loyalty.
