BARNEY GOODMAN

Barney Goodman

AI-native Change Director. Strategy, product, operations and build. 20+ years in financial services. Analogue film photographer in my spare time.

Barney GoodmanBG

About

Change & Technology Director at Shermin Finance. I'm Product Owner of Stax, the UK's largest loan originations platform for consumer credit. Twenty years in financial services, digital transformation, product strategy, and operations.

One of the first UK Consumer Finance professionals to achieve 6x Claude Certifications, as verified by Anthropic.

I work with agentic engineering to architect solutions, design products, write code, and ship finished work myself end-to-end. Strategy, operations, and delivery all run through one person. The result:

Compressed development cycles. Products and MVPs that would normally take a team months, delivered in days. Full executive oversight with zero interpretation loss between strategy and execution.

I write about AI implementation, agentic engineering and fintech. I shoot analogue film and travel in my spare time. This site is where I share what I'm thinking about.

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·TLDR Tech

AI-First UX Will Break Our Loan Journeys

The framing around AI-first UX tends to focus on enterprise productivity tools and knowledge workers. That's the wrong place to look if you're building consumer credit products in the UK. The more interesting question is what happens to a regulated loan application journey when the interface stops being a form and starts being a conversation. Right now, our origination flows are essentially digitised paper. A sequence of fields, disclosures, affordability questions, consent checkboxes. Compliance teams have spent years getting comfortable with exactly what the customer sees and when they see it. Agentic UX breaks that contract. If an AI can carry context across a workflow, answer questions mid-journey, and adapt what it surfaces based on the conversation, then the "journey" as a fixed, auditable sequence starts to dissolve. That's not a UX problem. That's a Consumer Duty problem. The FCA's focus on good outcomes and fair treatment assumes you can point to the experience a customer had. You can screenshot a form. Auditing a conversational agent that behaved differently for different customers because it was personalising in real time is a genuinely harder compliance challenge. - The obligation to present information clearly doesn't disappear because the interface is conversational - Pre-contractual disclosure requirements don't care whether the customer is reading a screen or talking to an agent - Vulnerable customer identification becomes more complex when there's no standardised journey to assess against None of this means AI-first UX is the wrong direction for consumer credit. A well-designed conversational experience could do a much better job of explaining loan terms than a wall of text most customers scroll past. The potential for genuinely improved comprehension is real. But the teams building these journeys need compliance and technology working together from the start, not compliance reviewing a finished prototype. The question worth sitting with is whether your organisation is structured to do that, or whether you're still treating UX as something that gets signed off at the end.

AI-first UX will evolve from basic chatbots and text prompts to integrated experiences that blend conversational interfa
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·TLDR Tech

AI Agents Need Permissions Infrastructure, Not Just Policies

The EU AI Act deadline is concentrating minds on compliance documentation, but the harder problem is the one most teams haven't started: how do you actually enforce what an AI agent is allowed to do at runtime? A policy document saying your agent operates under least-privilege principles is not the same as technically enforcing it. In consumer credit, where an agent might be querying affordability data, triggering bureau calls, or updating application states, the gap between written policy and actual system behaviour is where your regulatory exposure lives. The pattern that matters here is treating AI agents like external service accounts, not internal trusted processes. That means: - Identity at the agent level, not just the user or session level - Scoped permissions that are checked on every call, not assumed at startup - An audit trail that captures what the agent was authorised to do, what it attempted, and what was denied UK firms often think the EU AI Act is someone else's problem. It is not. The FCA's own thinking on AI governance is moving in exactly the same direction, and the Consumer Duty obligation to demonstrate good outcomes requires you to explain what your automated systems actually did and why. You cannot do that without the infrastructure described above. The investment case for this work is also stronger than it looks. Building proper authorisation and audit patterns for AI agents is not compliance overhead. It is the foundation for safely expanding what those agents can do. Right now most teams are artificially constraining agent scope because they have no confidence in what the system will actually attempt. Fix the permissions model and you fix that constraint. The question worth sitting with is whether your current AI governance programme is producing artefacts that describe intended behaviour, or controls that enforce it.

Lays out identity, policy, and audit patterns teams need to externally enforce least-privilege agent calls under upcomin
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·TLDR Tech

Chinese AI Is Closing the Gap Faster Than You Think

Qwen3.7-Plus does something that matters more than the benchmark scores suggest. It unifies GUI and CLI interaction inside a single agent loop, which means one model can read a screen, decide what to click, write code to automate the next step, and keep going without being handed off between specialised components. That is the architecture that makes agentic automation practical rather than theoretical. Most UK fintech teams are still treating AI as a copilot that assists a human. That mental model is already out of date. What Alibaba has shipped here is a foundation for agents that operate across interfaces the way a capable junior analyst would, navigating systems that were never designed to be automated. Loan origination platforms, affordability tools, CRM systems built on decade-old assumptions about human operators: all of it becomes reachable. The geopolitical angle also deserves honest attention. There is a tendency in UK financial services to treat the AI conversation as a choice between OpenAI and a handful of European alternatives. Qwen3.7-Plus is available commercially via Alibaba Cloud right now, and it is competitive at the capability level that matters for production workloads. Procurement teams and risk committees need to engage with what that means for data residency, supply chain concentration, and the FCA's operational resilience expectations under PS21/3. Two things I would be thinking about if I were building or buying right now: - The scaffold-agnostic performance claim is significant. If the model genuinely works consistently across frameworks, the switching costs between agent platforms drop, which changes the vendor negotiation entirely. - Multimodal agents that can read GUIs will expose just how fragile some of our internal tooling is. That is not a reason to avoid the technology. It is a reason to get ahead of the audit trail questions before a model starts clicking through your origination workflow. The real question for UK technology leaders is not whether to engage with Chinese frontier models. It is whether your governance framework is mature enough to make that call deliberately rather than by accident.

Qwen3.7-Plus is a multimodal agent model that unifies vision and language into a single, versatile agent foundation. It
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