Technology has always pushed industries forward, but the rise of artificial intelligence (AI) appears to be innovating at breakneck speeds.
The financial services industry has been surprisingly fast to adopt AI, with mortgage brokers having to decide whether to invest in this new technology or fall behind. An estimated two-thirds of all UK banks are now using AI in the services they offer.
Looking ahead, it is likely that successful brokers will be the ones who do more than act as an intermediary for AI. Instead, brokers will become hybrid advisers, guardians of data and interpreters of algorithmic insights. In this article, we’ll take a closer look at what the data tells us about the next wave of brokers using AI in mortgages.
The data landscape: AI, mortgages and broker trends in the UK
The uptake of AI across the financial industry is happening at an astonishingly fast rate. A recent Bank of England survey showed that 75% of UK financial institutions are already using AI in some capacity, with a further 10% planning to adopt these tools within three years.
Within those already using AI, 17% already incorporate foundation models. These models are large-scale, general-purpose AI models that use vast data sets to power and adapt to various business needs. This suggests that we’re still early, but the direction of travel is clear.
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The broad uptake of AI across the industry means mortgage lenders are unlikely to be left behind. I’ve personally spoken with brokers who are looking to embed AI into areas like underwriting, decisioning, risk monitoring, and customer engagement platforms.
The mortgage broker market: Growth and structural trends
Understanding the current mortgage broker market helps frame how much room there is to reshape it. What many brokers forget is that the market is what ultimately governs change. So, let’s take a look at how things are currently going:
- The UK mortgage broker market was estimated at £2.2bn in 2024.
- Over the past decade, the industry has expanded by approximately 110%, with forecasts suggesting further growth of 30-32% by 2030.
- There are over 5,682 mortgage broker businesses in the UK as of 2024.
Running parallel to these insights, it’s important to see how this is affecting day-to-day operations for those in the industry. So, let’s take a look at what brokers on the ground are saying:
- In a recent HSBC Broker Barometer, 78% of brokers reported an increase in mortgage amounts agreed for clients.
- 91% of UK brokers expect business growth in the next 12 months.
- Many brokers are leaning heavily on specialist lenders; 61% of a surveyed group said they rely on specialist lenders to place more difficult cases.
These data points suggest that the broker channel is still vibrant and expanding, even before AI fully takes hold. However, that does not imply stability. Instead, it could point to the emergence of AI that will reshape roles, margins, and competitive positioning.
What the data suggests about the next wave of UK brokers
In my view, we can look at these trends to glean insights into what the future may hold for brokers. So, let’s look into what key features will likely define the next generation of brokers in the UK:
Hybrid advisory: Human and AI working together
The successful brokers will position themselves not as replacements for AI, but supervisors of it. They will orchestrate AI tools like MortgagX and move from doing to curating, validating, contextualising, and explaining. AI becomes a force multiplier, not a threat.
Data ownership and platform integration
Brokers who control rich datasets such as behavioural logs, application histories and granular borrower metrics will gain a competitive edge. This is especially true if they choose to invest in platforms that integrate data, APIs, and analytics. Some may evolve into fintech-broker hybrids rather than pure intermediaries.
Niche and complexity focus
As commoditised segments (including standard residential prime borrowers) become more automated or dominated by digital platforms, brokers will need to specialise in complexity. This might mean a switch to more niche offerings like buy to let (BTL), self-employed mortgages, bridging, green mortgages, conversions or mixed uses. These are areas where AI’s rigidity struggles, giving human judgment the upper hand by adding differential value.
Explainability and transparency as competitive differentiators
Brokers who can reliably explain AI decisions (to clients or regulators alike) will help remove potential trust concerns. That means embedding interpretability, audit trails, clear rationales, and human override logic into every workflow and process they are a part of. I’ve spoken with a broker who had to explain to clients how the AI tools were used to guide decision-making. Having that transparency is key to a solid relationship that utilises AI.
Ethical and compliance-first mindset
As AI becomes integral, regulation catches up. Brokers will likely need to work with strong governance here. This includes areas like bias monitoring, fairness audits, data privacy, and risk controls. Brokers whom I’ve spoken to about this believe that strong ethics will play a big role here, affecting client trust, brand reputation, and regulatory resilience.
Continuous learning and adaptation
Models adapt and change with the times, so the regulatory rules will also need to evolve, and markets must adapt. Brokers will have to build continual feedback loops to refine models, retrain, update strategies, monitor error rates, and adapt. Success will favour brokers who treat AI as a living ecosystem, rather than a static tool.
Key risks for brokers to watch with AI
No transformation occurs without friction, so there will be challenges that stem from the roll-out of AI. The data highlights some of the biggest challenges that brokers must address to survive and thrive. These include:
Bias, fairness, and data quality: The foundations of trust
AI models are only as good as their data. They can inherit historical biases or subtle proxies (postal code, property valuations, employment patterns). Without rigorous bias mitigation and fairness testing, models can produce unfair or discriminatory outcomes. This is, of course, a huge issue in the world of mortgages.
Client trust and scepticism
Survey evidence suggests significant consumer wariness. In one UK survey, 83% of respondents said they did not trust an ‘AI mortgage broker’ to assess their needs appropriately. In practice, many clients want the safety net of human oversight, especially in large or life-changing transactions.
Cost and talent barriers
Smaller broker firms may struggle with the capital, data science talent, or tech infrastructure needed to build suitable AI systems. Legacy systems, integration friction, and change resistance are real constraints that will need to be considered.
Accountability and liability
As AI-driven recommendations gain weight, misjudgments may expose brokers to reputational, legal, or regulatory risk. Human oversight may mitigate this, but brokers must codify responsibilities, audit trails, and control frameworks.
Reimagining brokerage for UK mortgages with AI
AI is no longer an optional layer, as it’s quickly becoming established infrastructure in the mortgage industry. For UK mortgage brokers, this is both a huge turning point and a unique opportunity.
The next wave of brokers will not compete with machines; they will work with them. Success will depend on blending human judgment, interpretability, domain insight, ethical rigour, and platform thinking. The future of mortgage brokerage in the UK won’t be about whether brokers survive, but about how they evolve.
