With modern banks and fintechs processing billions of transactions every month, fraud detection is a growing problem that legacy technology is struggling to keep pace with. According to UK Finance, over £629 million was lost through scams and payment fraud in the first half of 2025 alone. That represents a 3% increase on the same period in 2024.
But, despite their reputation for over-flagging transactions as fraud, traditional systems aren’t identifying potential fraud quickly or accurately enough. Below, we’ll cover how to speed up transaction fraud detection, better identify fraud patterns, and future-proof your platform.
How can banks get faster transaction fraud detection?
AI and ML have often been touted as silver bullets for solving transaction fraud, but that’s not quite true.
Research by Datos Insights suggests that the “AML models that many FIs use routinely generate 90% to 95% false positive rates.”
In other words, they get it wrong. A lot.
Although it’s understandable that real-time fraud detection systems err on the side of caution when trying to identify payment fraud, inefficiency and inaccuracy remain huge problems.
We’ve written elsewhere about why false positives are bad for banks, but the headlines are that frustration caused by blocked transactions and erroneous alerts can lead to customer churn, reputational damage, and higher operational costs. The good news is that emerging technological developments mean that more accurate and faster transaction fraud detection is possible.
1. Leverage real-time transaction insights
Many banks and financial institutions rely on batch processing transaction enrichment. That means, although raw transaction data appears in real time, it may be a couple of hours before supplementary contextual information about the transaction is attached to it.
Without relevant real-time data like merchant names, geolocation, and accurate spending categorisation, systems don’t always have adequate information to effectively identify fraud as it’s taking place.
Translation: not all ‘real-time transaction monitoring’ is created equally.
Fraudsters can take advantage of that small window, say, to move the money elsewhere. Even the most sophisticated fraud prevention AI tools and machine learning algorithms can fail when they can’t apply the right mechanisms to temporarily incomplete datasets.
At Moneyhub, we categorise and enrich transactions in real real time, scaling all the way to Tier 1 institutions with millions of customers. By feeding directly into fraud detection algorithms, we reduce the latency between suspicious spending and putting a stop to it.
2. Improve categorisation and enrichment accuracy
When enrichment providers aren’t able to map raw transactions to useful data, customers might see a transaction string like MCRS*WDBRDC in their online banking app. When reviewing their spending later, they may not remember that as a visit to Woodbridge Coffee.
Moneyhub offers market-leading accuracy of 99% when it comes to merchant identification, as well as 98% in categorisation. Both of which represent vital ingredients of behavioural profiling for accurate personal finance analytics, lending decisions, and so on. They also help customers to maintain a clearer picture of their spending and identify potential fraud.
More precise transaction data helps banks to clarify spending patterns, using both AI fraud detection systems and manual reviews. To return to our Woodbridge Coffee customer example, perhaps they often visit different branches around the same time each morning. Data categorisation – food/drink, in this case – could offer a useful hint in this situation.
When something looks wrong, you can apply account controls more accurately and avoid frustrating customers or placing them into vulnerability, in compliance with Consumer Duty.
3. Rely on behavioural data for better segmentation and deeper KYC
Historically, banks have relied on sources like CRA (Credit Reference Agency) data and survey data from the Office of National Statistics to build a picture of their customers. However, that type of picture is based on a range of assumptions and projections. As such, it will always fail to capture the nuanced behaviours that different individuals exhibit.
With our Smart Segmentation functionality, banks can use real-time financial behaviour to create far more accurate pictures of their customers. Banks can deploy that analytics data to inform lending decisions, affordability models, and so on. And a better understanding of a customer’s habits makes it much easier to spot when they deviate from those behaviours.
Behavioural data can also be used in AI fraud detection to identify risk or unusual spending long before traditional systems ever could. Banks can take action as soon as they spot a deviation rather than having to wait for multiple anomalies or, even worse, a large drain.
What is the impact of faster fraud detection for Moneyhub clients?
The impact of faster transaction fraud detection is twofold:
- Improving customer experience
- Lowering costs for institutions
Accurate, proactive fraud warnings and correctly applied account controls lead to lower churn and improve Customer Lifetime Value (CLV). When customers are confident their money is being protected, without feeling smothered, they are more likely to stay with a bank and adopt more services from the product suite.
On the flipside of that coin, a reduction in the number of false positives means that your team has more time for manual investigations that are necessary and can get to them more quickly. Additional clarity around merchant identification and behavioural habits reduces the volume (and wasted cost) of false transaction disputes, including so-called “friendly fraud.”
The ease of integrating Moneyhub into legacy systems, working with and expanding on the information you already have about your customers, means you’re not starting from scratch. And the platform’s ability to categorise and enrich thousands of transactions per second offers extremely high potential for scaling up as your operation continues to grow.
Automation, speed, and accuracy all matter in fraud prevention
Payment volumes in the UK number in the billions every year, with total payments increasing 1.9% year on year to almost 49 billion in 2024 alone. Although volume is a key factor here, another one is that the behaviour of modern consumers is becoming more complex:
- Cross-border commerce
- Engagement with crypto
- Subscription based products and services
- Buy-now-pay-later
- Widespread use of VPNs
The above are just a few of the hurdles fraud teams now face when analysing spending.
Applying AI to fraud detection automates fraud prevention, cutting through noise to reduce the workloads associated with modern spending habits. However, these systems can only speed up transaction fraud detection if they have the right data available at the right time.
Effective fraud detection and smarter transaction monitoring facilitate faster payments and lower operational costs, as well as happier (and safer) customers. Now that’s a win-win for everyone involved.
About Matt Barr
Matt Barr is a Product Director here at Moneyhub. He’s been working either with or for banks since the mid-00s, solving all manner of problems. From ISA transfers to corporate actions, Matt now focuses on transaction categorisation and enrichment. When he’s not solving client problems, you can find Matt buried under his children’s laundry or stomping through the Peak District.
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