By Ben Booth, CEO, MaxContact
Fraud detection has moved away from just implementing AI into backend systems to scan transactions for anomalies. While traditional rule-based approaches still matter, AI now allows financial service organisations to combine fraud detection with real-time customer behaviour analysis.
Fraud prevention isn’t just about monitoring transactions; it’s about understanding behaviours.
For years, fraud prevention in financial services has prioritised monitoring transactions to identify anomalies and flag suspicious behaviours. This remains a hugely important process, but now, fraud prevention needs to start at a much earlier stage.
It needs to become an active part of every customer journey and be implemented across every micro interaction and customer touchpoint. That way, organisations can pinpoint the precise moments when customer trust is weakening, understand how and why personal decisions are being made, and identify early signs of increasing risk.
There is a growing trust gap in the finance and debt sector
A huge problem is emerging in the finance and debt sector. Customers trust it least, avoid it most, yet need to reach it the most urgently.
The problem that financial service organisations face currently isn’t just about detecting fraud, but about legitimately getting hold of customers in the first place.
Consumers are increasingly cautious about unknown contacts. Many now screen calls, ignore messages or avoid engaging altogether due to concerns about scams.
In fact, our own research published in our Voice of the Consumer UK 2026 report confirms that around half of consumers say they have ignored a legitimate message because they believed it could be fraudulent. In the finance, loans and debt sector, almost four in ten consumers say they would be least likely to answer a call.
This trust gap is now at a critical stage because there are many genuine reasons why a financial services firm may need to contact a customer directly. They may need to ask a question about account activity or missed payments. But, if a customer refuses to engage, it becomes much harder for organisations to verify identity, confirm suspicious activity or intervene early.
This avoidance is now a significant fraud risk.
Why contact centres are critical to modern fraud prevention
The contact centre is often the first point of interaction between a customer and a financial services provider. It is where decisions are made about whether to trust, respond or disengage.
That’s why the contact centre should be viewed as a frontline layer for fraud detection.
It’s also why prevention plans need to be better integrated into customer experiences.
Unlike transactional systems, which use AI to analyse data patterns, the contact centre is where you can capture real-time human interaction. Everything from customer tone and language to hesitation and behaviour can all provide valuable context that cannot be found within transactional monitoring.
When used effectively, this environment enables organisations to shift from reactive fraud detection to proactive risk identification.
Vulnerability signals are now early fraud indicators
One of the most important developments in this space is the growing link between vulnerability and fraud risk.
Customers experiencing financial difficulty rarely state it directly. Instead, they show it through behaviour. They may hesitate, become uncertain, avoid conversations or delay engagement. These behavioural signals can indicate that a customer is more likely to disengage from help or support and will be less confident in their decision-making (placing them at greater risk).
If a customer is disengaged from the outset, they will be far less likely to challenge suspicious contact or trust legitimate outreach. This can make them more susceptible to missing critical interventions.
Without these layers of insight, organisations are limited to detecting fraud after risk has materialised rather than identifying it at the point of customer interaction.
How AI in the contact centre closes the trust gap
Contrary to expectations, customers are more likely to respond to an AI or automated system when they are experiencing financial difficulty. This could be because it feels more private and less likely to make them feel ashamed of their financial situation.
That’s why vulnerability detection needs to be brought into the contact centre because it can use conversational intelligence to recognise real-time behavioural signals. Organisations can analyse tone and interaction patterns to flag when additional support or intervention may be needed, leading to earlier action, better outcomes and reduced risk exposure.
If a customer is more likely to respond to an AI or automated system, then that system needs to be able to seamlessly adjust the tone of communication and pick up signals that require escalation to a specialist team at the earliest opportunity.
This behavioural analysis overcomes the blind spot currently present in transaction monitoring.
Fraud prevention must be embedded into the customer experience
Fraud prevention can no longer be used as an isolated backend function. It must sit within the customer journey, with the contact centre acting as core detection and intervention.
When technical signals are combined with behavioural insights, it becomes much easier to transition from reactive detection to proactive prevention. It’s no longer enough to identify when fraud occurs; financial services teams need to recognise which customers are most likely to be exposed to fraudulent activity.
The best prevention plans will come from businesses that have technical safeguards in place and understand how customers think and why they behave the way they do. Those who do this successfully will be able to narrow the trust gap, reduce avoidance, and fundamentally strengthen long-term fraud-prevention outcomes.
About Author
Ben is the CEO and co-founder of MaxContact. As CEO, Ben sets the strategic direction for the business, leads the senior leadership team and champions MaxContact’s distinctive culture of distributed leadership and transparency. He oversees all aspects of the company’s growth, from product innovation and AI technology advancement to team development and market positioning
