In 2024, the UK’s financial sector reported a staggering 3.31 million fraud cases – a 12% jump from the previous year. Total losses hit £1.17 billion, making fraud the driver behind 41% of all reported crime in the country. Data from early 2025 shows this trend isn't slowing down, with identity fraud now accounting for nearly 60% of all filings and deepfake-related incidents surging as criminals find more sophisticated ways to mimic legitimate users.
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Having spent years in the fintech space, I’ve seen firsthand that the real challenge isn’t just stopping fraud – it’s doing it without getting in the way of growth. This is exactly where AI changes the game, allowing for invisible, real-time protection that actually speeds up the user journey.
Case Study: Scaling a UK Finance Leader with AI-Driven Growth
At intive, we’ve been AI-native from the start, putting machine learning to work for our clients long before it became a headline. While much of the world is only now realizing what’s possible, we’ve long known that AI is the key to turning security from a necessary cost of doing business into a powerful growth engine.
A good example of our work would be the cooperation with a leading UK finance and Prepaid Card Application company to streamline operations and enhance their main system. Their mission was ambitious: double their acquisition, revenue, and gross profit while simultaneously preventing any fraud action by new or existing customers.
By combining AI-driven analytics with a strong compliance culture, we protected their financial systems and helped rebuild global trust. Here is how our implementation of AI supported the business:
1. ML and AI to Prevent Fraud Transactions
Our algorithms are redefining how finance systems combat financial crime by forecasting fraud behavior.
- Pattern Recognition: We developed models that flag hidden links between entities using data collected from fraudsters delivered by several banks and financial institutions.
- Real-Time Monitoring: The system detects anomalies faster than humans, allowing for immediate intervention.
- Predictive Analysis: By "learning" from previous attacks, the AI anticipates potential laundering attempts and blocks new fraud users before they can act.
2. AI-Powered User Acquisition and KYC
KYC is the first step in establishing trust. We adapted the verification process to specific business requirements, scaling AI to detect suspicious patterns in vast datasets.
- Multi-match Age Verification: Using data capture and picture estimation to verify users instantly.
- Deepfake & Device Verification: The system includes deepfake detection and identifies the usage of shared devices – a common fraud indicator.
- Adaptive Learning: We used machine learning that adapts to evolving fraud trends, allowing the system to detect patterns that the human eye might miss.
- Seamless Migration: Beyond the AI implementation, intive took charge of the migration process, transferring the reporting platform and data into a new solution to ensure a smooth transition for all users without interrupting operations.
3. AI for Data Visualizations, Analysis, and Forecasting
We implemented advanced AI visualizations to help the business identify high-revenue users and keep them active:
- Key Influencers: This diagnostic tool analyzes data to find the top contributors (like customer age, contract length, or region) that drive specific metrics like sales or satisfaction.
- The Decomposition Tree: An AI visualization that automatically aggregates data and allows for ad-hoc exploration and root-cause analysis by drilling down into dimensions in any order.
- Explain the Increase: This feature automatically analyzes data points in a chart to identify which dimensions (such as product segments or regions) contributed most to a change, presenting findings through easy-to-understand visuals and text.
Watch: A 60-second breakdown of how we transformed security and acquisition for this UK finance leader.
Security: The Foundation of Fintech Growth
This collaboration proves that fintechs don’t have to sacrifice growth for safety. By automating compliance and fraud detection, the business was able to focus on its most valuable users while rebuilding global trust.
Security is a high-impact priority for modern fintechs – and we are proud to support our clients in doing it right. What we’ve seen is that security has the most influence when it is properly tailored to specific use cases and business needs.
That’s why we developed the "AI Sprint for Fintechs." This offering helps our clients identify where AI fits best within their specific business model – whether in operations, user experience, or risk management.
If you want to learn more, reach out to me or visit our landing page to see how we help you identify and prototype your highest-impact AI use cases in just four weeks: intive AI Sprint for Fintechs
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