The idea that people only fall for a scam once no longer holds true. Modern fraud has evolved into a sophisticated lifecycle where criminals deliberately return to the same victims, refining their tactics with each interaction.
Enabled by data harvesting, automation and global criminal networks, re-targeting scams have become one of the most damaging trends in financial crime, trapping individuals in prolonged cycles of exploitation that are increasingly difficult to detect or disrupt, according to RelyComply.
This pattern of repeat exploitation was highlighted in a recent episode of RelyComply’s Laundered podcast, where Gareth Dothie, head of bribery and corruption at City of London Police, warned that even mature economies like the UK are struggling to deal with fraud revictimisation. While scams themselves are nothing new, criminals are now returning to victims within months of an initial incident, using prior knowledge and emotional leverage to deepen the harm.
At the heart of this issue is the commodification of victim data. Once an individual’s information has been captured, it rarely stays with a single fraudster. Instead, it is shared, sold and reused across networks that include social engineers, money launderers and crypto specialists. Victims who have already been manipulated are seen as low-effort, high-value targets, allowing criminal groups to scale rapidly while extracting more from fewer people.
This business-like approach is reinforced by the sheer volume of personal data available online. From banks and healthcare providers to social media and e-commerce platforms, individuals routinely share sensitive information. When breaches occur, criminals gain the building blocks needed for follow-up scams. Many victims initially feel unlucky rather than targeted, while others experience shame, denial or emotional distress, making them more susceptible to further manipulation or blackmail.
On the dark web, this information is assembled into full identity kits, often referred to as “fullz”. These detailed profiles combine IDs, contact details, location data and scam histories, and are traded within criminal marketplaces that function much like customer relationship management systems. Armed with this intelligence, fraudsters can tailor scripts, switch scam types and approach victims repeatedly through calls, emails or messages in multiple languages.
One of the most common outcomes of this process is the rise of recovery scams. Here, criminals pose as trusted authorities or service providers, offering to help victims reclaim lost funds. Using stolen KYC details, they build credibility and exploit existing trust, extracting additional payments under the guise of fees or investigations. In some cases, victims are coerced into becoming money mules, moving funds on behalf of criminals under threat or false assurances of legitimacy.
These risks are exacerbated by fragmented data across financial institutions. Fraud and AML teams have traditionally operated in silos, meaning early scam reports are not always linked to later suspicious behaviour. Clear warning signs – such as repeated scam claims, unusual transaction spikes or sudden third-party deposits – can be missed without shared context, allowing exploitation to escalate.
To counter this, integrated protections for vulnerable customers are essential. Continuous behavioural monitoring, consolidated customer views and rapid information-sharing across banks, FinTechs, regulators and law enforcement can help identify repeat victimisation early. By connecting fraud prevention and AML systems, institutions can act before victims are pulled deeper into criminal networks.
As re-targeting scams become a defining feature of the modern fraud lifecycle, financial institutions face both a regulatory and moral obligation to respond. Breaking these cycles requires lifecycle intelligence, collaboration and technology that prioritises prevention as much as detection. Without it, fraud revictimisation will continue to undermine trust and inflict long-term damage on those caught in its grip.
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