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Intelligent Utility
Kateryna Nechet
 · Posted in Intelligent Utility

Can machine learning predictive models become a breakthrough for utilities in debt?

Imagine your utility company monitors thousands of contracts and spots a red flag with Contract No. 32786, which, despite being active for over three years, now shows a concerning 90% chance of debt. This account's recent billing spike, coupled with its cash payment method, prompts a strategic shift. 

Utilizing a machine-learning system, your company introduces a fixed payment plan for this specific contract. This not only aims to boost customer loyalty but also drastically lowers the debt risk to 1%. This approach demonstrates how targeted, data-driven strategies can turn a potential financial risk into a predictable and manageable revenue source, benefiting both the company and the client.

This was one of the situations where the contract's ML-powered debt risk predictive model could save the day for the company. If distilling the debt situation across Europe, for example, only in the UK, according to Ofgem, the amount of debt and arrears among energy customers has surged by over 107% in the past five years. The sector's total estimated debt ranges from ÂŁ3.5 billion to ÂŁ3.6 billion.

So, what do ML predictive models actually do?

At MaxBill, Machine learning (ML) models enhance the capabilities of utility companies by:

  1. Precisely identifying contracts with a high propensity for future payment defaults.
  2. Investigating the principal elements that lead to the accumulation of customer debt.
  3. Employing predictive scenarios through 'What-If' analysis to guide strategic planning and decision-making processes.

Through the application of machine learning, companies can derive actionable insights into the most effective strategies for debt collection from consumers.The models give predictions on the chances of different interventions working. This lets many different strategies be simulated to find the best order of actions for getting the most money back from debts and clearing up arrears faster. 

The model works in the following steps:

  1. Problem Identification and Business Understanding
  2. Data Collection
  3. Data Processing
  4. Data Analysis
  5. Data Modelling
  6. Model Deployment

Each company has unique needs and problems, which means the model will operate based on different parameters. Just to name a few. These might be service duration, consumption patterns, payment methods, and regional data. 

The scope of the model’s predictive analysis encompasses residential customers, and industrial and commercial clients, demonstrating broad applicability across different customer segments.

“What-if simulation” for proactive and reactive strategies

The 'what-if' simulation, powered by machine learning, optimizes debt recovery by exploring effective action sequences for swift resolution. This proactive approach shifts debt management towards predictive strategies, improving financial outcomes.

Advanced analytics enable personalised interventions, tailoring solutions to individual customer profiles for both efficacy and empathy. Integrating data, from usage trends to economic factors, supports the creation of targeted plans and proactive strategies. The aim is to balance revenue preservation with customer support, fostering trust and understanding.

What do utilities get at the end of the day?

First and foremost, it’s revenue protection. The model is crucial in identifying accounts likely to ‘fall in debt’, significantly reducing potential bad debt and safeguarding the utility's revenue. This leads to a more stable cash flow. Enhanced management of consumer debt ensures steadier revenue streams, contributing to the financial robustness of the company.

Moreover, it supports regulatory compliance and boosts customer satisfaction. Leveraging predictive analytics for proactive interactions with customers meets regulatory expectations for focusing on consumer well-being and minimizes financial vulnerabilities without depending on heavy deposit demands.

Final thoughts

Machine learning models vary widely to boost the operational efficiency of utilities. They encompass models for predicting customer turnover and potential revenue leakage, forecasting demand response trends, identifying anomalies or fraudulent activities, and facilitating predictive maintenance. 

These models are cultivated within a model training camp and a model farm, environments tailored for the development and deployment of numerous models in real-world settings. This setup enables the customization of models to meet the unique business requirements and objectives of each utility organization.

 

 

 

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