Utilities in consumer and B2B markets are trudging through a grim winter as they begin to grapple with the arrival of the debt crisis which has been dolefully expected since the start of the first national lockdown in March last year.
Extensions to government support schemes for companies and individuals impacted by the pandemic masked the full extent of this challenge for utilities throughout 2020, and to some extent, they continue to do so into 2021. Inevitably though, the effects of long term income loss or reduction are beginning to feed through and swelling ranks of customers are falling behind on or failing to meet payments for their energy and water supply.
By autumn 2020, figures from Citizens Advice suggest that around 6 million UK adults were behind on at least one household bill during the pandemic, including 3 million on their water bills and 2.8 million on energy bills. A fresh period of national lockdown, despite the continuation of furlough, will see this problem deepen.
The complex and unfolding impacts on finances has forced many utilities to question their existing debt management strategies. Many are exploring emerging artificial intelligence (AI) and machine learning technologies as a means to get ahead of the debt environment and help support customers in maintaining payments for life’s essentials.
The ability to crunch through large and varied datasets can create a clearer picture of the trajectory of debt in the short and longer term, it can more effectively identify behavioural triggers or other signs that individuals are experiencing financial hardship.
AI has the potential to optimise the debt resolution workflow by identifying actions most likely to have the biggest impact when collecting debt, also reducing the number of steps involved. This can streamline engagement with customers, ease their long-term accumulation of debt, and improve company profitability.
In a new report created by Utility Week in association with AI specialist Inawisdom, the experiences of utilities beginning to access these benefits and more are explored. The report summarises the developing picture for consumer debt and bill arrears in the UK and provides practical insights into the ways in which AI and machine learning can help utilities overcome pressing challenges in creating debt prevention, provision and collections strategies at a time when pre-existing approaches to modelling debt propensity in customer bases have been heavily undermined. Go to utilityweek.co.uk and download the report for free to learn more.
Q&A
Mark Wilkinson, head of income at Northumbrian Water Group
Included in Utility Week’s latest report is an interview with Mark Wilkinson, head of income at Northumbrian Water Group, in which he explains how use of advanced AI and machine learning gave it a head start on debt management in these uncertain times. Here’s a sneak preview:
When did you start to look at using AI for debt management?
We did some work at a high level a few years ago, trying to use machine learning to predict our debt paths and make the system more reactive to what was actually happening “on the ground”.
When you build a tool for debt management, it is valid at the time you build it but not necessarily six months down the line when there’s a new customer segment or a segment that behaves slightly differently, etc.
We wanted to get to a point where our system could learn as the circumstances change – a particular concern given all the current uncertainties around Covid19 – however, at the time the AI was new technology and quite hard to adapt.
Fast forward to 2019 and we were introduced to the AI/ML specialist Inawisdom, whose advanced cloud-based system promised to give us much greater insights into our collections data, improve our engagement with customers and optimise the debt recovery process.
What’s the quickest win you’ve achieved in terms of better debt management?
Crunching the data provided insights into aspects we hadn’t even considered.
Previously we sent customers text messages when a payment was due, but Inawisdom’s analysis of historic data on our customer behaviour and the timing of messages revealed that if we sent SMS’s to a specific customer group on a specific day of the week, for example on a Friday instead of a Wednesday, we would be more likely to receive payments. It’s a different perspective, we had never really gone into the timing of things.
Were you already using automated processes?
We use an automated rules-based system that focuses on debt paths for specific customer groups, but the prototype developed and implemented by Inawisdom allowed us to refine the way those groups are segmented and change the order of the debt path for each customer group to achieve the best outcomes.
We found that we could reduce the number of steps involved in collecting a customer’s debt, cutting out steps identified as unnecessary (some can’t be missed due to regulatory requirements), in the process reducing our costs and improving the overall customer experience. We’ve also been able to fast track certain stages, and reduce the time between triggering debt recovery and getting the money back.
What customer data is fed into the tool?
A plethora of data goes in if it’s relevant, including customer payment behaviour, previous debt recovery activity, complaints data, demographic data, socio-economic data, credit scores, data from the deprivation index, a model we previously developed to predict water poverty was another source.
Obviously there is a data protection angle to all of this, particularly since the introduction of the General Data Protection Regulation. We anonymise any customer data before it’s uploaded to the cloud so Inawisdom can’t connect it to individual customers, then when we get the results back we reconnect it to their accounts.
Did you have to overhaul your IT infrastructure?
No, our statistical software links into the Amazon Web Services (AWS) cloud platform where Inawisdom runs its Rapid Analytics and Machine learning Platform (RAMP). Inawisdom is a full stack services provider on AWS and using a cloud-based system has benefits in terms of data storage and security.
Did you use the AI to estimate longer term debt provision?
We carried out some modelling around the longer term impacts of the pandemic and what that might mean for provisioning and levels of bad debt risk, pulling together models for furlough, other financial support, unemployment changes and the impact on particular sectors.
Provisioning models normally calculate bad debt risk based on previous bills, but this was more forward looking and predicted the increase in provision needed in coming years through the lens of Covid.
Unemployment seemed to be the main driver for bad debt, the extension of furlough and initiatives like Eat Out to Help Out weren’t in play when we did that modelling, but the model is adaptable so we have the option to update parameters and create new forecasts.
The tool could come in useful – our five-year business plan means we have fixed income over that period, so any shocks need to be planned out in advance.
What are the limitations of AI?
The biggest limitation is our systems and ensuring we can make the most of the learning from Inawisdom to implement effective changes to our debt collection strategies. They built a collections model that essentially looks to optimise every element of the output, but the constraints we currently have to work to limit its effectiveness.