Fraudsters never stand still – so why do you in your attempts to beat them? The threat of lost revenue poses an opportunity to adopt a holistic and data-driven approach to non-technical losses … and indeed all losses within the utility sector.
Globally, non-technical losses (AKA fraud) cost utilities $96 billion annually according to a study issued by Northeast Group in May 2017. An internet search will bring to your attention other international and regional studies, all of them quoting hefty figures. Regardless of which studies you feel are accurate, the problem – or, as I prefer to think of it, the opportunity – here for utilities to address is huge.
Currently, utilities are tackling fraud in a silo-fashioned way – running specific teams that focus on specific processes, backed up with one-off projects to boost revenues short term. This typically focuses on areas such as billing and revenue assurance, identification of unbilled revenues by comparing metered consumption versus energy billed out, and where possible comparison of power, gas or water metered values throughout the network versus the sum of metered consumption downstream at the customer level.
It works but with limitations!
This silo-type solution approach, whilst delivering some benefits, can only do so much. It is massively hamstrung from a number of angles, including:
- Lack of any significant ability to understand, classify and importantly prove non-technical losses in a way that follow up actions can be justified or indeed show any given case is valid.
- Financial benefits of acting on certain categories of fraud or individual cases are often not well understood.
- Non-technical losses are managed in isolation from technical losses. As a simple example: if a customer’s premises include complex metering arrangements of which a portion are missing from a utilities’ system and/ or just not billed accurately, the customer could well understand that they are being under-billed and have just chosen not to confess. Are those losses technical or non-technical? Utilities need to think about losses as holistically as possible – this is all revenue!
- The silo approach is tactical in nature, but focused on short-term fixes for short-term gains, instead of longer term solutions focused on permanent prevention.
These limitations are driven by a lack of focus on how to use data – both from the perspective of what can be done with it, and how to integrate it at scale from multiple sources in a way that everyone can tackle losses holistically, and more effectively.
Water utility hits the mark
On a positive note, there are proven international examples of utilities that have overcome these barriers, and taken advantage of the benefits on offer. As far back as 2013, during a conference, a Brazilian water utility revealed its integrated approach to losses that considered nontechnical losses alongside other factors, including water pressure and leakage, to understand the gap between total losses it was incurring across the board, and what it termed “unavoidable losses”.
The gap was then fed into more targeted programmes looking at technical and non-technical losses, with the latter using various forms of detailed segmentation highlighting illegal connections and meter bypass, illegal use of hydrants, and tampering with meters themselves and/or the data readings taken. More strategically, this work also fed the company’s metering replacement programme, focusing the rollout of smarter meters and telemetry on network zones linked with higher losses to stamp out these issues more permanently.
All in, this delivered a 7% increase in revenue in the first year alone. They did not stop there: reusing the integrated data view in other innovative ways such as integrating masses of data from sources such as the CRM, billing and finance estate, GIS, metering systems and more – that could be used to underpin other initiatives at minimal cost, such as improving customer water efficiency to meet new regulatory goals. Other utilities have adopted a similar holistic approach to losses, and indeed data integration. For instance, a US-based utility focused on debt recovery, and what to prioritise. This included understanding whether a customer was committing fraud willingly or unwillingly based on their financial position. With this information the utility was able to potentially de-prioritise those cases given that cash collection could be costly, or there might be no cash available! This had the associated benefit of enabling the utility to better manage bad debt reserves, all based on the power of integrated data.
The power behind the data
Building on this concept of using data to fight fraud – there are multiple pre-built segmentation tools with baseline analytic capabilities that segment fraud cases, and importantly identify cases up front on the market. These tools can be very powerful; however, they are only as good as the data you integrate and feed them with. In the case of one major US utility, such a tool was enabled with an integrated data view similar to that quoted in the Brazilian case. This is used to produce live dashboards that can be updated in near real-time, if required, providing a focus and extra intelligence for fraud detection teams.
This end-to-end solution focuses on power theft in the field via meter tamper and bypass for example, not only identifying individual customers as being potential fraud cases but also showing the time period during which suspected fraud is taking place as extra evidence to pursue cases. The lesson here is that the integrated view of data was not just used for fraud detection. It was also used to underpin energy efficiency drives, demand response programmes and the creation of advanced time-of-use tariffs, and to underpin rollout and monetisation of smart metering, and more – all of which indirectly contribute to lower losses. A holistic approach to energy provision more widely leads to a more holistic approach to tackling losses.
Refocusing specifically on fraud, it is important that utilities look to other industries for new ideas on how to tackle this problem, and for ideas around using data. Modern technology and analytics make data much more accessible to people as information in ways that are both easily understood, and even quite visually appealing. One thing we can be sure about is that those committing fraud are continually changing their modus operandi! Artificial Intelligence has the potential to really step up here, giving ever more intelligent insight into what those fraudsters will look like, and indeed look to do in the future.
In summary, the best way to resolve non-technical losses is to take a strategic approach that also links to your work on technical losses, all of which needs to be underpinned by a solid data strategy in terms of those areas you want to focus on, and how to integrate your data. Integrated data can be used both directly by the utility to run its own analysis, and indirectly to feed third party tools – and as importantly, the same integrated data can be monetised in many ways in areas totally separate from losses. Although the industry is yet to achieve a zero non-technical loss rate, there are utilities world-wide who are stepping up the pace to keep fraud to a minimum. Looking ahead, the markets are exploring the power of integrated data for artificial intelligence as the next frontier in tackling fraudsters. ESI
About the author
Iain Stewart is an executive consultant with experience across many industries, currently focused on utilities and smart cities. Within Teradata, Iain coordinates the go-to-market for utilities across Europe, MEA and Asia and is part of a wider Industrial Internet of Things team focused on data integration and analytics.