Resource: Blog

BritCard: Identity, Inclusion, and the Fine Line Between Safety and Surveillance

Written by Jason Abbott on October 8, 2025. Posted in Blog, Resource. No Comments on BritCard: Identity, Inclusion, and the Fine Line Between Safety and Surveillance

BritCard: Identity, Inclusion, and the Fine Line Between Safety and Surveillance

Let’s be honest. The first reaction to a new government-backed identity card like the proposed BritCard isn’t excitement — it’s suspicion.

Headlines and social media posts paint a picture of a tracking tool:

A way to log when you go abroad.
A database that can follow your every move.
Even fears that the government could dip directly into your bank account.

These stories get attention because they play to something real — our collective anxiety about privacy and control in the digital age.

The plan is to anchor BritCard within the existing Gov.UK One Login/Wallet infrastructure, enabling landlords, employers, banks, and public services to verify entitlements — such as right-to-work and right-to-rent — through a single secure verifier app.

This blog explores both sides of the BritCard conversation: the tangible benefits a universal digital ID could deliver and the concerns that need addressing if it’s to earn public trust. Whether you see it as a step toward inclusion or a step too far, the debate matters — because the way we design identity systems shapes how millions of people access services, prove who they are, and protect what’s theirs.

The Potential Benefits

Free ID for Everyone
Passports and driving licences cost money — often over £80 — and not everyone can afford them. That’s why, even today, estimates suggest between 2 and 3.5 million adults in the UK do not have any form of recognised photo ID. For those people, everyday tasks like proving their identity for a job, rental, or bank account become unnecessarily difficult.
A free, universal ID could change that by giving everyone the same basic proof of identity, regardless of income or background. Everyone should have the right to a free, recognised form of identification. For some, the BritCard could be their very first form of official ID — a tool that unlocks access, not just for the few, but for everyone.
“I Don’t Have My Document With Me — But I Have My Phone”
We’ve all had that frustrating moment: halfway through an application, asked for a passport or licence that’s sitting in a drawer at home. With a reusable digital ID, that roadblock disappears. You carry it with you, ready to use in seconds, whether you’re applying for a loan, signing a tenancy, or verifying your age.
Fighting Deepfakes, Fake IDs, and Synthetic Identities
Fraudsters thrive on weak ID checks. They exploit gaps by creating fake identities, using stolen details, or even building synthetic identities that blend real and fake information to appear legitimate. In 2024, UK victims reported over 100,000 cases of identity fraud, with losses running into the hundreds of millions.
Criminals are already a step ahead. They’re using deepfake technology to generate highly convincing images and videos of passports, driving licences, and even live “selfie” checks. These fakes are often detected — but when they slip through the net, the results can be very costly for businesses in terms of direct losses, compliance fines, and reputational damage.
Would the BritCard be a perfect, spoof-proof solution? Probably not. No system is. But by anchoring identity to a single, secure, government-issued credential, rather than fragmented checks across dozens of providers, it could raise the barrier significantly.
Inclusion for the “Thin File”
Not everyone has a long credit history. Young people, newcomers to the UK, and international students often struggle to prove not that they exist, but where they live.
Take Anna, a 19-year-old student from Spain arriving for university. She doesn’t have a UK credit record, isn’t on the electoral roll, and her rental agreement isn’t always accepted by banks. Today, opening a bank account might take weeks of back-and-forth. With a BritCard linked to her university enrolment and HMRC registration, her address could be confirmed instantly — letting her start life in the UK without delay.
This kind of real-time verification would mean:
- Faster access for genuine newcomers and young people.
- Less frustration in everyday applications.
- Stronger protection against fake documents, since address data would come only from verified sources.
One Solution Across Industries
Today, every organisation has its own way of verifying identity. Banks, lenders, telcos, landlords, and employers all use different systems, which means customers face repeated checks, duplicated requests, and sometimes inconsistent outcomes.
A universal digital ID like the BritCard could streamline this. Instead of juggling multiple verification systems, businesses could plug into a single, trusted credential.

Banks & lenders:
Since the Immigration Act requires them to verify that customers have the right to live and work in the UK, a universal digital ID could make compliance far easier — reducing manual processes and ensuring consistency.
Telcos & utilities:
Easier verification for new contracts, protecting against account fraud and “bust-out” scams.
Landlords & letting agents:
Reliable right-to-rent checks without chasing paper documents.
Employers:
Quicker right-to-work verification, reducing the cost and risk of manual checks.
E-commerce & digital services:
Stronger age and identity checks at checkout, with less friction for genuine buyers.
Healthcare and public services:
Faster onboarding with safeguards for sensitive data.

In short, the BritCard could become a common trust layer across industries, making life easier for genuine customers and raising the bar for criminals trying to exploit inconsistent processes.

What We Can Learn from Other Countries

The UK wouldn’t be the first to try a universal digital identity. Other countries have already rolled out similar schemes, with valuable lessons:

	Estonia has built one of the most advanced digital societies in the world on the back of its national ID. Citizens use it for healthcare, tax, banking, and even voting. A cryptographic flaw in 2017 forced an emergency response — a reminder that even strong systems must plan for cyber risks.
	Denmark’s MitID is used by almost all adults, proving that widespread adoption is possible. It has improved trust and convenience, though scams and social engineering remain ongoing challenges.
	Singapore’s Singpass shows how integration across public and private services can reduce friction for citizens, but also how critical it is to provide strong customer support against fraud attempts.
	India’s Aadhaar demonstrates scale and inclusion, giving hundreds of millions of people their first form of ID. But it has also highlighted the importance of legal guardrails and clear limits on how data can be used.

When designed well, digital ID systems can unlock access, improve security, and fight fraud. But every example also shows that inclusion, privacy, and resilience must be built in from day one.

The Concerns and Risks of BritCard

For the BritCard to work, public trust will be just as important as the technology itself. While the benefits are clear, there are also challenges that need to be addressed.

Inclusion and the Right to ID
Every adult should have the right to a recognised identity. For some, the BritCard could be their very first form of official ID. But to live up to that promise, it must be accessible to everyone — not just those with smartphones, stable internet, or digital confidence. Without inclusive design and offline options, the very people who stand to benefit most could still be left out.
Privacy and Data Use
People want to know how their data will be stored, who can access it, and for what purpose. Without clear guardrails, concerns about “too much information in one place” could undermine trust.
Cyber security
Any centralised identity system will be a target for hackers. Even the most secure designs need robust contingency plans, rapid patching, and transparent communication in the event of an incident.
Consistency of Experience

If the BritCard is adopted unevenly, with some industries using it fully and others sticking to older processes, users may end up facing the same frustrations as today. A smooth, consistent experience will be critical to delivering real value.

Walking the Fine Line

To some, BritCard feels like a step closer to monitoring; to others, it promises inclusion, protection, and simplicity. The truth is that it could be both — or neither — depending on how it is designed and delivered.

If the system is built with cyber security at its core, with ease of use for every citizen, and with a focus on adding real value for both consumers and businesses, then the BritCard could solve many of the frustrations we face today with passports, licences, and paper-based processes.

Get it wrong, and it risks being seen as another layer of control. Get it right, and it could be one of the most empowering tools of the digital age — tackling fraud, opening access, and proving that identity can be both secure and inclusive.

This isn’t about politics — it’s about tackling fraud, improving inclusion, and building a digital ID system that puts privacy and cyber security first.

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Navigating the Promise and Peril of Generative AI in Financial Services

Written by Mark Collingwood on October 7, 2025. Posted in Blog.

Navigating the Promise and Peril of Generative AI in Financial Services

Financial services leaders are being bombarded with AI pitches. Every vendor claims their solution will revolutionise decisioning, slash costs, and unlock untapped revenue. Meanwhile, your competitors are announcing AI initiatives, your board is asking questions, and your teams are already experimenting with ChatGPT and other tools—sometimes without your knowledge.

The pressure to “do something” with AI is intense. But the organisations that rush to deploy generative AI without understanding its limitations are setting themselves up for problems that may not become apparent until it’s too late.

At Provenir, we’ve built AI decisioning capabilities that process over 4 billion decisions annually for financial institutions in 60+ countries. We’ve seen what works, what doesn’t, and what keeps risk leaders up at night. More importantly, we’ve watched organisations make costly mistakes as they navigate AI adoption.

In this article you’ll find a practical assessment of where generative AI delivers real value in financial services, where it introduces unacceptable risk, and how to tell the difference.

Where AI Delivers Value

The efficiency benefits of AI in financial services are tangible and significant. Here’s where we’ve seen AI deliver measurable business impact:

Faster model development and market response:
What once took months in model evaluation and data assessment can now happen in weeks, enabling lenders to respond to market changes and test new data sources with unprecedented speed.
Transaction data transformed into intelligence:
Advanced machine learning processes enormous volumes of transaction data to generate personalised consumer insights and recommendations at scale—turning raw data into revenue opportunities.
Operational oversight streamlined:
Generative AI helps business leaders cut through the noise by querying and summarising vast amounts of real-time operational data. Instead of manually reviewing dashboards and reports, leaders can quickly identify where to focus their attention—surfacing which workflows need intervention, which segments are underperforming, and where action is most likely to drive business value.

These aren’t future possibilities. Financial institutions are achieving these outcomes today: 95% automation rates in application processing, 135% increases in fraud detection, 25% faster underwriting cycles. While GenAI-powered assistants accelerate model building and rapidly surface strategic insights from complex decision data.

The Risks Nobody Talks About

However, our work with financial institutions has also revealed emerging risks that deserve serious consideration:

When AI-Generated Code Contradicts Itself	Perhaps the most concerning trend we’re observing is the use of large language models to generate business-critical code in isolation. When teams prompt an LLM to build decisioning logic without full knowledge of the existing decision landscape, they risk creating contradictory rules that undermine established risk strategies. We’ve seen this play out: one business unit uses an LLM to create fraud rules that inadvertently conflict with credit policies developed by another team. The result? Approved customers getting blocked, or worse—high-risk applicants slipping through because competing logic created gaps in coverage. In regulated environments where consistency and auditability are paramount, this fragmentation poses significant operational and compliance risks.
When Confidence Masks Inaccuracy	LLMs are known to “hallucinate”—generating confident-sounding but factually incorrect responses. In financial services, where precision matters and mistakes can be costly, even occasional hallucinations represent an unacceptable risk. A single flawed credit decision or fraud rule based on hallucinated logic could cascade into significant losses. This problem intensifies when you consider data integrity and security concerns. LLMs trained on broad, uncontrolled datasets risk inheriting biases, errors, or even malicious code. In an era of sophisticated fraud and state-sponsored cyber threats, the attack surface expands dramatically when organisations feed sensitive data into third-party AI systems or deploy AI-generated code without rigorous validation.
The Expertise Erosion	A more insidious risk is the gradual erosion of technical expertise within organisations that become overly dependent on AI-generated solutions. When teams stop developing deep domain knowledge and critical thinking skills—assuming AI will always have the answer—organisations become vulnerable in ways that may only become apparent during crisis moments when human judgment is most needed. Combine this with LLMs that are only as good as the prompts they receive, and you have a compounding problem. When users lack deep understanding of what they’re truly asking—or worse, ask the wrong question entirely—even sophisticated AI will provide flawed guidance. This “garbage in, garbage out” problem is amplified when AI-generated recommendations inform high-stakes decisions around credit risk or fraud prevention.
Regulators Are Watching	The regulatory environment is evolving rapidly to address AI risks. The EU AI Act, upcoming guidance from financial regulators, and increasing scrutiny around algorithmic bias all point toward a future where AI deployment without proper governance carries substantial penalties. Beyond fines, reputational damage from AI-driven failures could be existential for financial institutions built on customer trust.

What Successful Institutions Are Doing Differently

Based on our work with financial institutions globally, the organisations getting AI right start with a fundamental recognition: AI is already being used across their organisation, whether they know it or not. Employees are experimenting with ChatGPT, using LLMs to generate code, and making AI-assisted decisions—often without formal approval or oversight. The successful institutions don’t pretend this isn’t happening. Instead, they establish clear AI governance frameworks, roll out comprehensive training programs, and implement mechanisms to monitor adherence. Without this governance layer, you’re operating blind to the AI risks already present in your organisation.

With governance established, these organisations focus on maintaining human oversight at critical decision points. AI augments rather than replaces human expertise. Business users configure decision strategies with intuitive tools, but data scientists maintain oversight of model development and deployment. This isn’t about slowing down innovation—it’s about ensuring AI recommendations get validated by people who understand the broader context.

Equally important, they refuse to accept black boxes. In regulated industries, explainability isn’t negotiable. Every decision needs to be traceable and understandable. This isn’t just about compliance—it’s about maintaining the ability to debug, optimize, and continuously improve decision strategies. When something goes wrong (and it will), you need to understand why.

Rather than accumulating point solutions, successful institutions build on unified architecture. They recognise that allowing fragmented, AI-generated code to proliferate creates more problems than it solves. Instead, they use platforms that provide consistent decision orchestration across the customer lifecycle. Whether handling onboarding, fraud detection, customer management, or collections, the architecture ensures that AI enhancements strengthen rather than undermine overall decision coherence.

These organisations also treat AI as a living system requiring continuous attention. AI models need ongoing observability and retraining. Continuous performance monitoring helps identify when models need refinement and surfaces optimisation opportunities before they impact business outcomes. The institutions that treat AI deployment as “set it and forget it” are the ones that end up with the costliest surprises.

Finally, they maintain control of their data. Rather than sending sensitive data to third-party LLMs, forward-thinking organisations deploy AI solutions within secure environments. This reduces both security risks and regulatory exposure while maintaining full control over proprietary information.

Why Inaction Isn’t an Option

The irony is that many leaders debating whether to “adopt AI” have already lost control of that decision. AI is already being used in their organisations—the only question is whether it’s governed or ungoverned, sanctioned or shadow IT.

Meanwhile, fintech disruptors are leveraging AI to deliver frictionless, personalised experiences that traditional institutions must match. The competitive gap isn’t just about technology—it’s about the ability to move quickly while maintaining control and compliance.

Organisations that succeed will be those that combine AI capabilities with strong governance frameworks, architectural discipline, and deep domain expertise. They’ll move beyond isolated experiments to implement AI in ways that deliver real business value while maintaining the trust and regulatory compliance that financial services demand.

The institutions making smart bets on AI aren’t the ones moving fastest—they’re the ones moving most thoughtfully, with equal attention to capability, transparency and governance.