Kaboodle Ltd, founded in 2005, announced that insolvency specialists BRI Business Recovery and Insolvency have been appointed to enable the company’s voluntary liquidation.

The kitchen brand offered home appliance supply and installation services for retailers and manufacturers across five depots.

Since opening more than two decades ago, Kaboodle opened facilities in Highbridge, Basingstoke, Stevenage, Telford and Leeds.

UK kitchen firm shuts down with immediate effect

Kaboodle announced its closure with a statement on its website, sharing: “We regret to inform you that Kaboodle Ltd has ceased trading with immediate effect.

“Insolvency Practitioners, BRI Business Recovery and Insolvency, have been instructed to assist in placing the Company into Creditors’ Voluntary Liquidation.

“You will be contacted from them directly in due course in relation to the liquidation process, including details on how to submit a claim for any monies owed, as applicable.”

Adding: “If you currently hold stock with us, please arrange for collection as a matter of urgency.”

Kaboodle then urged customers to collect all stock no later than May 8, sharing: “Please note that after this date, we cannot guarantee access to or availability of your stock, as staff will be leaving the business and control of the premises may pass to the appointed liquidator and/or the landlord.

“Thank you for your prompt attention to this matter.”

Commercial Director at Kaboodle, David Simpson, wrote on LinkedIn confirming the brand’s closure: “I’m sad to formally advise that Kaboodle is to be liquidated as a result of cash constraints.

“My thoughts and appreciation go to all those employees involved in the insolvency – it’s a distressing time for all.

“I’d also like to thank our manufacturing and retail customers for their loyalty.

“We were charged with restructuring the business to dedicate ourselves to the Home Installation market some time ago, meaning that we needed to exit our Housebuilder sector.”

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Former managing director Matthew Pitt at Kaboodle added: “Sad to hear the news about the closure of Kaboodle today.

“It’s very disappointing to see a business with strong foundations end up in this position.

“There are a lot of talented people affected, and I hope they’re supported through what will be a difficult period.”

Did you ever use Kaboodle? Let us know your thoughts in the comments.

SOFIAH NICHOLE SALIVIO

News Editor

FICO has urged UK lenders to use artificial intelligence in collections operations, following its analysis of UK credit card data for 2025.

The share of customers missing two or three card payments rose through much of the year, while balances on those accounts also increased from a year earlier. Accounts with two missed payments reached an average balance of £2,938 in November, up 4.9% year on year. Accounts with three missed payments climbed to £3,324 in December, up 4.1%.

The trend is adding pressure to collections teams already dealing with larger case volumes and more complex customer circumstances. Many lenders still rely on manual handling, with staff reviewing cases one by one, a model FICO described as expensive and difficult to scale.

It argued that this has left many operations dependent on older tools and processes that do not adapt well when payment problems rise. FICO highlighted scripted systems such as diallers and basic chatbots, saying they can improve productivity but are often limited to simple interactions.

Mike Trkay, Chief Information Officer at FICO, said lenders face a growing mismatch between demand and the way collections work is organised.

“Collections is one of the clearest examples of where traditional processes are no longer fit for purpose,” Trkay said.

He said lenders are trying to manage high volumes, limited resources and changing customer expectations at the same time. In FICO’s view, conversational AI can support more natural exchanges with borrowers and gather information as discussions develop.

Operational strain

One area of focus is how AI systems identify signs of customer stress and vulnerability. Those signs are not always obvious and may differ between people with a long history of paying on time and those who fall behind more regularly.

A borrower who misses a payment because of affordability pressure may need a different response from a customer with a repeated pattern of late payment. In that context, FICO said AI tools could help direct cases to more appropriate treatment and reduce unnecessary manual work.

At the same time, Trkay said conversational systems create risks of their own if lenders do not tightly define the rules they operate under. Because these systems can follow a customer conversation in many directions, they increase the chance of inconsistent outcomes.

“Basically, the AI needs to know when it should stop and make a hand-off to a live agent,” Trkay said.

That point is likely to resonate with lenders navigating consumer protection obligations and internal conduct rules. FICO said AI systems used in collections should be limited in the language and guidance they provide, and should recognise when an interaction is edging into advice or becoming too prescriptive.

Decision tools

Beyond conversation handling, FICO said the bigger opportunity lies in combining those systems with decision intelligence. In practice, that means using AI not only to understand what a customer is saying, but also to choose an action based on policy, affordability signals and expected outcomes.

That could support automated negotiation of payment plans, identify customers in financial hardship, guide borrowers through repayment options and keep interactions within regulatory and internal policy limits. AI-driven optimisation could also help lenders decide where to deploy limited staff and resources across large portfolios.

This would involve weighing the cost, effort and likely returns of different interventions, rather than applying the same collections treatment across broad groups of accounts. FICO said that could help lenders prioritise cases where engagement is more likely to lead to recovery or where a more tailored approach is needed.

Trkay said the aim is to move away from standard responses that fail to reflect differences between borrowers.

“AI enables organisations to move beyond static, one-size-fits-all approaches,” Trkay said. “It allows lenders to deliver more personalised, responsive and effective collections strategies, improving outcomes for both the business and the customer.”

The comments come as lenders continue to monitor the effect of household financial pressure on unsecured borrowing performance. Rising missed payments on credit cards can point to broader strain in consumer finances, while also increasing servicing costs for banks and card issuers if account management becomes more labour-intensive.

FICO warned that lenders that do not update their collections approach risk falling behind as volumes change and operating pressure grows. “Organisations that leverage AI effectively will not only improve efficiency but also build stronger, more resilient customer relationships,” Trkay said.

SOFIAH NICHOLE SALIVIO

News Editor

Keen AI and SP Energy Networks have launched a digital tool for new electricity grid connections, aimed at developers seeking access to the UK transmission network.

Called Intelligent Connections Explorer, or IConn, the system is designed to speed up the earliest stage of the connection process by giving users an initial view of where and how a project may be able to connect. It provides indicative costs, likely timelines and potential technical constraints, producing results in under five seconds instead of after weeks of manual assessment.

The launch comes as pressure grows on Britain’s electricity networks from a surge in applications linked to wind, solar and battery storage projects. Grid connection reform has become a central part of the government’s Clean Power 2030 agenda, with network operators and developers under pressure to cut delays that can hold back investment decisions and project delivery.

Demand for new transmission connections rose more than fivefold between November 2024 and June 2025, according to Keen AI and SP Energy Networks. The increase has intensified scrutiny of the pre-application stage, when developers often seek early guidance on whether a scheme is viable before committing more time and money to formal submissions.

IConn digitises transmission network data to create a single view of existing, contracted and planned capacity. It then uses locally hosted models to generate possible connection routes, estimate costs, simulate power flows and flag technical limitations that may affect a project.

Work that previously relied on manual engineering analysis could take hours or several weeks to assemble, depending on the information required and the resources available. By automating much of that initial review, customer-facing teams are expected to give developers more consistent answers earlier in the process.

Growing queue

Britain’s grid connection queue has become one of the energy sector’s most persistent bottlenecks as the country tries to expand low-carbon electricity supply and strengthen energy security. Developers have long argued that uncertainty over available capacity, cost and timing can slow project planning well before a formal connection application is submitted.

For transmission owners and network operators, the strain also falls on engineering teams, which must process rising volumes of requests while balancing technical, regulatory and operational demands. In that context, tools that standardise initial assessments may help free up specialist staff for later-stage analysis and formal design work.

SP Energy Networks said IConn has already shown measurable benefits in the preliminary phases of the connections process, including faster early insight, more consistent information for developers, more efficient use of engineering time and support for teams handling high request volumes.

The system also aims to capture knowledge that might otherwise remain with individual engineers or local teams. That matters in a process where early advice can vary depending on who handles an enquiry and what data is immediately available.

Sector focus

Keen AI is a UK company focused on artificial intelligence applications for critical infrastructure. It has worked with utilities including National Grid, Scottish Power and SSE on asset monitoring and network planning, and secured Ofgem funding in 2025 to develop what it described as Britain’s Foundation Source Model for electricity infrastructure.

SP Energy Networks operates electricity distribution networks across central and southern Scotland, Merseyside, Cheshire, North Wales and North Shropshire, as well as transmission networks in central and southern Scotland. The business serves more than 3.5 million customers across its network areas.

The collaboration reflects a broader move across the energy sector to use digital systems and automation in planning and operations as network owners face rising data volumes and tighter delivery targets. For connections, the commercial value lies in shortening the period before a developer can judge whether a project is worth pursuing.

Alana Cairns, Transmission Customer Liaison Manager at SP Energy Networks, said: “We’re really pleased to see IConn taking shape as a practical tool that reflects the depth of knowledge within our engineering teams. Working with Keen AI has allowed us to turn that expertise into something customers can access from day one, giving them a clearer understanding of their connection options and helping to set more informed expectations. It’s a great example of how innovation can directly enhance the customer experience.”

Amjad Karim, Chief Executive Officer and Founder of Keen AI, said: “We have taken a process that used to consume hours of engineering time and compressed it to seconds. That’s the kind of step change the UK needs to meet growing demand for clean energy. There’s a narrow window to get this right. The projects waiting in the queue today are the wind farms and battery storage we need online by 2030. Now it’s about scaling that solution across the industry.”