AI in fintech is moving rapidly from experimentation to standard operating practice.
A joint Bank of England and FCA survey found that 75% of responding financial services firms were already using AI, with a further 10% planning to adopt it within three years. Respondents expected their median number of AI use cases to more than double, from nine to 21.
That creates a significant opportunity for fintech businesses. It also creates a strategic problem.
When the same technology helps competitors generate code, automate operations, analyse markets and launch polished customer experiences, using AI no longer guarantees a fintech competitive advantage.
The important question is not simply:
Can we build something better?
It is:
What are we building that will become harder to copy as we grow?
Generative AI is improving the speed and economics of software development. McKinsey estimates that it could create between $200 billion and $340 billion in annual value for the global banking sector, largely through productivity improvements. Common applications include code generation, customer support, fraud prevention and the preparation of financial or regulatory documents.
These capabilities can strengthen margins and shorten development cycles. However, they are available to incumbents and new entrants as well as the original innovator.
This changes how fintech businesses should think about defensibility. A feature that once required a large engineering team may now be reproduced by a smaller competitor. A polished interface may attract customers without preventing them from switching. Faster execution may create a head start, but that head start can narrow quickly.
Chegg is not a fintech business, but its experience illustrates how quickly technology can reduce the value of an established proposition.
In May 2025, Reuters reported that Chegg’s quarterly subscriber base had fallen by 31% and revenue by 30% as students increasingly turned to AI-powered alternatives. The company announced plans to reduce its workforce by approximately 22%.
The lesson is not that every feature-led business will experience the same outcome. It is that apparent differentiation can lose value quickly when customers gain access to a cheaper, faster or more convenient substitute.
Fintech leadership teams should therefore distinguish between an advantage that helps win customers today and one that becomes stronger as the business grows.
Fintech’s first wave gained ground partly because traditional banks were slow to deliver modern digital experiences.
Faster onboarding, clearer account information and more intuitive dashboards created genuine customer preference. Those features remain important, but they are increasingly easier to reproduce.
A strong user interface becomes more defensible when it gives access to something deeper, such as proprietary data, trusted infrastructure or an operational workflow that customers cannot easily move elsewhere.
Without those underlying advantages, the interface may be a useful product feature rather than a durable moat.
Speed was another defining advantage for early fintech businesses.
A smaller technology company could launch new functionality while larger incumbents were still navigating governance, procurement and legacy-system constraints.
AI is now reducing development time across the market. The same tools that help one fintech launch faster can help competitors respond faster.
Being first still matters where it enables the company to secure exclusive distribution, gather valuable data or become embedded in customer operations. Speed becomes less defensible when it produces only a temporary feature advantage.
Watch the on-demand webinar
In Fintech Moats in an AI World: What AI Commoditises and What Remains Defensible, Martin Koderisch and Kayode Sulola explore how fintech advantages develop from temporary head starts into harder-to-replace business models.
AI can automate customer support, document processing, financial reporting and internal administration.
These improvements may reduce costs, strengthen margins and improve cash generation. For a business considering private credit, that stronger cash-generation profile may also support its debt-serviceability case.
However, operational efficiency and competitive defensibility are not the same thing.
Where several competitors automate an equivalent process using comparable third-party technology, each business may become more efficient without becoming more differentiated.
The cost base improves. The strategic moat may remain unchanged.
AI can help businesses research prospects, personalise outreach, produce marketing material and automate parts of the sales process.
That can lower customer-acquisition costs. It also lowers the same costs for competitors.
The more defensible distribution advantages are usually structural:
AI can accelerate distribution activity. It cannot automatically create trust, exclusivity or privileged customer access.
Using AI is becoming an expectation rather than a differentiator.
The relevant question is what the technology enables the business to do that competitors cannot easily reproduce.
For example:
Where the AI capability does not create one of these effects, it may be valuable without being defensible.
A practical way to assess a fintech moat is to place the business on a ladder.
|
Level |
Primary advantage |
How easy is it to copy? |
What makes it stronger? |
|
1. Interface |
Better user experience or individual feature |
Relatively easy |
Connecting the interface to unique data or infrastructure |
|
2. Execution |
Faster development and go-to-market activity |
Increasingly easy |
Turning speed into customers, partnerships and operating history |
|
3. Structural advantage |
Proprietary data, distribution or regulatory capability |
More difficult |
Combining several structural advantages |
|
4. Operational embeddedness |
Product sits inside critical customer workflows |
Difficult |
Integrations, dependencies, data history and switching costs |
|
5. Reinforcing infrastructure |
Network becomes more valuable as participation grows |
Very difficult |
Scale, trust and self-reinforcing network effects |
The lower levels can provide a valuable route into the market. The strategic task is to use that early advantage to move towards the levels that become stronger over time.
Cashflows, for example, operates across payments, onboarding, fraud controls and merchant infrastructure rather than competing through a single front-end feature. Its position illustrates why infrastructure and workflow integration can support a stronger commercial proposition.
Leadership teams should ask:
A business does not need to sit at the top of the moat ladder today. It does need a credible plan for moving upwards.
Our view is that AI investment should not be measured by the number of new features it produces.
The more important test is whether that investment strengthens retention, margins, distribution, customer embeddedness or the predictability of future revenue.
Those are the factors that can support a more durable business and a stronger capital-raising narrative. They also help funders understand why customer revenue should remain resilient during the life of a facility.
Businesses assessing how capital could support this development can explore Fuse Capital’s wider track record across growth and technology transactions.
Explore the full fintech moat framework
Watch Fintech Moats in an AI World for Martin Koderisch and Kayode Sulola’s discussion of the five-tier moat ladder, the difference between peripheral and core AI, and what fintech leaders should consider next.
Next in the series: Fintech Competitive Moats: 4 Advantages That Become Stronger as AI Spreads
A fintech moat is a competitive advantage that helps protect the business from replication, substitution or customer switching. Examples may include proprietary data, difficult-to-access distribution, regulatory infrastructure, workflow integration and network effects.
Not necessarily. AI can improve speed, customer experience and efficiency, but competitors may have access to similar models and tools. It becomes more defensible when combined with proprietary data, embedded workflows or a reinforcing distribution advantage.
Individual features, user interfaces, basic automation and broad claims about using AI are generally easier to reproduce than proprietary infrastructure, trusted distribution or deep customer integration.
A fintech can strengthen its moat by using early product traction to gather differentiated data, secure distribution, deepen integrations and become more important to customer operations.