The Future of Fraud Prevention Depends on Connected Intelligence and Early Intervention

Fraudsters do not operate within institutional boundaries. They move money across accounts and financial organisations, exploiting the gaps that exist between siloed intelligence and fragmented networks. Yet many fraud prevention decisions are still made using only a siloed view of risk, with financial institutions limited to the information available within their own environments at a single point in time.

This challenge is becoming increasingly difficult as the fraud threats grow more sophisticated and money mule networks evolve rapidly. The industry increasingly recognises that no single organisation, dataset or perspective can provide a complete picture of risk. The future of fraud prevention depends on connecting and orchestrating intelligence from multiple sources to proactively identify and disrupt the mule networks that enable the movement of illicit funds, before the damage is done.

At Mastercard, this principle is shaping the next phase of A2A fraud innovation through capabilities such as Consumer Fraud Risk (CFR) Data Insights and Money Mule Risk (MMR). By combining insights from multiple sources and perspectives, these solutions enable financial institutions to access richer intelligence, strengthen collaboration, and make more informed decisions earlier in the fraud lifecycle – before the victim has made a payment.

A key example is CFR Data Insights, an enhancement to Consumer Fraud Risk. Data Insights enables the secure sharing of additional account-level intelligence between sending and receiving institutions, enriching fraud decision-making at the point of payment. By appending these insights to CFR Outbound Risk Score responses and Inbound Risk Notifications within seconds of payment initiation, it provides both parties with greater context about the accounts involved in a transaction, including indicators such as age of relationship, average turnover and recent account activity.

Mastercard calculates, standardises and orchestrates the sharing of these data points centrally on behalf of participating institutions, removing the need for individual banks to perform the underlying data processing and analytics themselves. The insights are then delivered through existing CFR services. This scalable, one-to-many model removes the need for multiple bilateral data-sharing arrangements while helping institutions make more informed decisions, reduce unnecessary friction and strengthen fraud prevention across a network that already profiles more than 90% of UK Faster Payments and Bacs activity.

Upstream of the transaction itself is where fraud risk often originates, within networks of accounts used to conceal and disperse illicit funds. Accounts that appear legitimate today may develop characteristics associated with money mule activity tomorrow. This is where Money Mule Risk (MMR) represents a significant evolution in proactive risk management.

MMR moves beyond point-in-time assessments by continuously profiling account risk throughout the account lifecycle. Rather than evaluating risk only during onboarding or payment initiation, it will identify emerging behaviours, evolving risk indicators and suspicious patterns over time. Recognising that not all mules are complicit, and that intervention strategies can be complex, MMR will provide dynamic account-level risk intelligence to support more targeted proactive intervention.

Continuous monitoring becomes even more powerful when combined with trusted third-party intelligence. While payment networks reveal behavioural and transactional patterns, external sources such as identity data, known fraud associations, application data, cyber intelligence and other external risk signals provide additional context and can uncover connections that payment activity alone cannot.

Fraud crosses institutional boundaries, risk evolves over time, and different datasets provide different perspectives. Effective prevention therefore requires collaboration across the ecosystem, combining diverse sources of intelligence and transforming them into explainable insights that investigators and decision-makers can act on with confidence.

Our collaboration with Cifas reflects this philosophy in practice. Cifas is expected to become one of Mastercard's first external data partners for Money Mule Risk, demonstrating how trusted third-party fraud intelligence can be combined with network-level payment data to identify potential money mule activity earlier and with greater confidence. Over time, this intelligence could also enhance capabilities across Mastercard’s broader A2A fraud and financial crime portfolio, including Trace Financial Crime and Consumer Fraud Risk services. This illustrates the wider potential of applying complementary sources of intelligence across multiple solutions to support a more connected and holistic approach to fraud and mule prevention.

"No single organisation holds all the answers when it comes to tackling fraud and financial crime. The greatest progress is made when organisations bring together their expertise, and share data and intelligence in a responsible and transparent way. Collaboration is at the heart of everything Cifas does, and we are excited by opportunities to explore new approaches with Mastercard that could strengthen the industry's response to evolving threats, including money mule activity. Only by coming together can we better protect organisations and consumers." – Mike Haley, CEO, Cifas

Further supporting this vision is Mastercard's broader platform transformation through its partnership with Feedzai. The modernised platform is enabling enhanced capabilities across Consumer Fraud Risk, Trace Financial Crime and Money Mule Risk, including the new Trace user experience, while laying the groundwork for a future A2A Risk Solutions Hub. By bringing fraud prevention, scam detection and mule risk intelligence into a shared environment, the hub will create a scalable foundation for intelligence sharing, data ingestion and orchestration across the wider fraud prevention ecosystem.

Ultimately, the next generation of fraud prevention will not be defined by a single dataset, product or organisation. It will be built on connection: connecting institutions, connecting payment and non-payment intelligence, and connecting insight to action quickly enough to prevent harm. By combining network-level intelligence with trusted bank and third-party data, the industry has an opportunity to move beyond fragmented detection towards a more proactive, collaborative and effective defence against fraud.