Starling Bank cuts 130 jobs in AI and restructuring push


TL;DR

Starling Bank is cutting around 130 jobs as it restructures operations and pushes AI deeper into its business. The neobank’s profits fell for a second consecutive year, but its technology licensing arm Engine grew revenue 25%.

Starling Bank is cutting around 130 jobs, roughly 3% of its 4,000-strong workforce, as the London-based neobank restructures its banking and technology operations. Staff were told this week that the changes were intended to simplify how the company operates, reduce duplication, and accelerate product delivery.

The cuts come as Starling pushes AI deeper into its operations. In March, it launched Starling Assistant, an agentic AI tool that can set up savings goals, organise bill payments, and quiz customers on their spending patterns using voice or text prompts.

Falling profits in a falling-rate world

The restructuring follows a second consecutive year of declining earnings. Pre-tax profit fell to £217 million in the year to March, down from £223 million a year earlier, while total revenue dropped from £940 million to £887 million.

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Starling attributed the decline to falling interest rates, which have squeezed margins across UK banking. The neobank remains profitable, having now posted five consecutive years in the black, but the direction of travel is clear.

Customer numbers continued to grow, with platform accounts reaching 6.2 million, up from 5.3 million the previous year. Deposits rose to £12.7 billion.

The AI arms race among neobanks

Starling’s AI push is part of a broader race among digital banks to automate customer-facing operations. Revolut launched its own AI assistant, AIR, to UK customers in April, offering similar capabilities around spending analysis and account management.

Starling’s scam detection tool, launched in October 2025, uses Google’s Gemini models to analyse marketplace listings and flag fraud in real time. The tool has since been expanded to detect more than ten types of scam, including romance fraud and deepfake phishing.

“A key factor in our competitive edge over legacy banks is our agility, our ability to test, launch, learn and reorganise at pace,” a Starling spokesperson said. The bank added that it is continuing to hire technology and AI engineers even as it cuts elsewhere.

Engine as the growth story

The brighter part of Starling’s business is Engine, the software-as-a-service arm that licenses the bank’s core technology stack to other financial institutions. Engine’s revenue grew 25% last year as its client base doubled on international demand.

Engine already powers banks in the UK, Romania, Australia, and New Zealand, and is now targeting the US market. The division has opened an office in New York with a reported $50 million investment and is in discussions with mid-tier American lenders.

A sector-wide shift

Morgan Stanley estimated in June that AI could eliminate as many as 400,000 European banking jobs by 2030, double its earlier forecast of 200,000. ABN Amro announced last year that it would cut roughly 20% of its workforce by 2028, primarily through automation.

Starling’s 130 cuts are modest by comparison, but they signal a shift within the neobank sector itself. The digital challengers that once defined themselves against the bloated workforces of high-street banks are now applying the same efficiency logic to their own operations.



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Meta stripped NameTag facial recognition code from its AI app one day after WIRED exposed it on 50 million phones. Meta says no decision has been made.

Meta removed nearly all traces of an unreleased facial recognition system from its smart glasses companion app on Friday, one day after WIRED reported that the software had been quietly embedded in an app installed on more than 50 million phones. The feature, which Meta internally called NameTag, was designed to convert faces captured by the company’s Ray-Ban smart glasses into unique biometric signatures and compare them against a database stored on the user’s device. WIRED also found that faces the system failed to recognise were cropped, indexed, and stored locally for future processing.

Andy Stone, Meta’s vice president of communications, told WIRED on Monday that the feature is “purely exploratory,” adding that no final decision has been made on what to do with it. That characterisation sits uneasily with the evidence WIRED documented. The version of Meta AI published the day of WIRED’s Thursday report contained several code libraries explicitly named for face recognition, a process for running the NameTag recognition pipeline, and a “Person recognised” alert the app would have shown if someone were identified.

Friday’s release stripped all of it out, along with a folder where the app would have stored the cropped images and biometric signatures of unrecognised faces. Meta did not answer WIRED’s questions about why the code was removed or whether the changes were planned before the story was published. A few fragments remain in the latest version, including an internal debug menu label and a dormant link meant to open a recognised person’s profile, pointing to parts of the system that are no longer there.

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The gap between Meta’s public statements and the code WIRED found is the central tension. Before the Thursday report, Stone dismissed the findings by writing that the company could not answer questions about how the system would work because “the feature does not exist.” Andrew Bosworth, Meta’s chief technology officer, called the reporting “incredibly misleading” and “absolutely dishonest.” Yet the code was functional enough to include three AI models, one to detect faces, another to crop them, and a third to encode them as biometric data, all embedded in the companion app for a product already at the centre of a mounting privacy crisis.

Meta declined to answer ten questions WIRED posed before publishing, including whether it had already created the database of face profiles NameTag uses, how long the app retains photographs and biometric data of unrecognised people, and whether that data would ever be sent back to Meta’s servers. The company also did not respond to questions about whether it was building NameTag for blind or low-vision users, or to criticism from privacy advocates who warned the system could let stalkers and abusers identify strangers in public.

NameTag first surfaced in February, when The New York Times, citing internal Meta documents, reported that the company was developing face recognition for its smart glasses and considering a launch as early as this year. One internal memo reportedly described releasing the feature during a “dynamic political environment” when privacy and civil liberties advocates would be distracted by other concerns. WIRED subsequently found that much of NameTag’s machinery had been built into the Meta AI app as early as January, months before any public acknowledgement, adding another layer to the company’s pattern of shipping first and disclosing later when it comes to its smart glasses.

Kade Crockford, director of the technology for liberty programme at the American Civil Liberties Union of Massachusetts, said the removal does not undo the original decision to ship the code and pointed to it as evidence that consumer privacy needs stronger legal protection than Congress has been willing to provide. The Massachusetts House of Representatives last week unanimously passed a consumer privacy bill that, if enacted as written, would impose strong enforcement provisions including a private right of action allowing aggrieved users to sue. “State lawmakers need to do their job and step up to protect consumer privacy,” Crockford said.

Meta’s sneaky tactics in slipping the face-recognition code into its smart glasses show exactly why data privacy bills need the teeth of strong enforcement,” Crockford added. “Companies like Meta prioritise their bottom line, so lawmakers need to speak in the only language its C-suite understands.” Whether a code removal prompted by investigative reporting constitutes a victory or merely a tactical retreat depends on what Meta does next, and on whether the regulatory pressure building on both sides of the Atlantic produces enforceable consequences before the feature quietly returns under a different name.



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