Reflections on the Hopcroft ruling, hidden commissions, and the future of AI-mediated consumer justice
In the wake of the Hopcroft decision, the UK Supreme Court has delivered one of the most revealing judgments of our time:
Credit brokers have no fiduciary duty. They may lawfully take bribes, conceal commissions, and place their own interest above the customer’s — as long as the contract permits it.
That single ruling exposes a truth many of us have known for years:
Structural untrustworthiness isn’t a malfunction in financial services — it is the operating system.
And that matters profoundly as the industry moves at speed toward agentic AI — autonomous, decision-making systems trained on historic practice, past data, and existing commercial priorities.
Because the question now is not whether AI will transform auto finance.
It is: What exactly will AI learn from an industry whose economic engine has long depended on information asymmetry, hidden incentives, and customer disadvantage?
1. Agentic AI learns from the past — even when the past is unethical
McKinsey paints a compelling picture of agentic AI: fleets of automated agents improving margins, streamlining operations, optimising pricing, upselling, managing remarketing cycles, detecting anomalies, orchestrating maintenance journeys, and negotiating with customers.
But AI is only as trustworthy as the systems it is trained on.
And in auto finance, those systems include:
- Hidden commissions
- Undisclosed dealer incentives
- Broker kickbacks
- Steering customers into costlier credit
- Baked-in conflicts of interest
- Data gaps, incomplete files, and inconsistent audit trails
- A culture that views customer value as “extractable margin”
If this is the substrate, then agentic AI will faithfully replicate the blueprint.
It will optimise the same extractive behaviours. Only faster.
Only at scale.
Only with plausible deniability.
2. Hopcroft confirms the industry’s true incentives
The Supreme Court did not merely decide a legal point; it exposed a mindset.
By confirming that brokers may profit secretly and owe no duty to act in the customer’s interest, the judgment crystallises what many consumers sensed intuitively:
The system is not designed to protect them.
So imagine training an AI agent to “maximise channel profitability” in remarketing, or “optimise commercial offers,” or “drive margin in dynamic pricing.”
Without moral boundaries baked into the architecture, that agent will do exactly what the current system rewards:
- Present favourable scenarios to customers while withholding better ones
- Direct inventory to channels that maximise yield rather than fairness
- Recommend products that benefit the lender more than the borrower
- Smooth over or restructure timelines to reduce liabilities
- Prioritise the lender’s risk exposure over the customer’s wellbeing
Not because the agent is malicious.
But because it learned from a malicious structure.
3. Structural untrustworthiness + agentic AI = amplified systemic risk
The danger isn’t rogue AI.
The danger is obedient AI.
Agentic systems excel at:
- Pattern recognition
- Process optimisation
- Strategic prediction
- Behaviour modelling
- Scalable execution
But if the patterns they learn are exploitative,
and the processes they optimise are biased,
and the predictions favour the lender,
and the behaviours prioritise margin over fairness…
…then the result is a system that makes *structural untrustworthiness look like operational efficiency.
This is the hidden risk no consultancy whitepaper will highlight:
Agentic AI may automate exploitation faster than regulators can detect it.
4. Independent AI changes the balance of power
The only counterweight is public-led, independent AI — the kind now powering the work of citizen investigators, victims’ advocates, and consumers who want to understand their evidence.
Independent AI can expose the very behaviours that agentic AI could entrench:
- Hidden commissions
- Timeline manipulation
- Fabricated notes
- Metadata inconsistencies
- Algorithmic steering
- Selective document disclosure
- Manipulated affordability calculations
We see this daily in Get SAFE: ordinary people uncovering what was previously invisible to them.
The public is finally gaining the tools to decode the system.
5. A new social contract for the AI era
The Hopcroft ruling creates urgency.
Agentic AI creates scale.
And consumers are caught in the crossfire.
If the industry does not build structural trustworthiness into AI from the outset — transparency, fiduciary alignment, customer primacy, traceable decisions — then AI will not simply reflect historic malpractice:
It will multiply it.
AI will not make the system fair.
Only ethical design, public pressure, and regulatory backbone will.
And in the absence of those?
The public will build their own tools.
Citizen investigators will rise.
And justice will become increasingly decentralised.
6. The path forward: Empowerment over extraction
The future of auto finance — and financial services more broadly — hinges on a single choice:
- Do we use AI to entrench the old model?
(hidden fees, conflicted incentives, opaque pricing)
or
- Do we use AI to build a new one?
(transparent, human-centred, structurally trustworthy)
Agentic AI has extraordinary potential.
But without a moral framework, it risks becoming the perfect servant of an imperfect system.
Hopcroft shows us what happens when the law tolerates conflict.
AI will show us what happens when conflict is automated.
The public must lead this next chapter — not the incumbents who benefited from the old one.
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steve.conley@aolp.co.uk | +44 (0)7850 102070
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