In today’s digital age, scams, also known as authorised push payments, have become increasingly prevalent. Although there are no formal figures, it is believed that global scam losses surpass $1tn annually. The primary driver behind this surge is organised crime, with about 1.5 million individuals speculated to be operating as professional scammers.
The rise in scams can be attributed to an array of factors. The growing professionalism of scammers, the accessibility of ready-made scam tools like phish kits, high gross margins from scams, the digital shift in customer experiences, and insufficient law enforcement all contribute to the escalating scam incidents worldwide.
The regulatory expectations and the accompanying cost of compliance differ across markets. The UK stands at one extreme, where financial institutions are liable for up to £85,000 in most cases. As most regulators evaluate the “Shared Responsibility Framework,” it is likely that financial institutions will have to shoulder a more significant portion of this cost.
When choosing a financial institution, customers prioritise good fraud protection. According to a worldwide survey of 18,000 customers, about 60% listed “Good Fraud Protection” as their top or second priority. This was closely followed by “Ease of Use” (43%). Although these two elements may seem contradictory, they are in fact, two sides of the same coin. A high detection rate of scams with an acceptable level of false positives is crucial for business growth.
7 Steps to Scam Prevention
Effective scam prevention requires a multi-layered strategy, rather than a single solution. The following seven-step framework presents a practical, intelligence-driven approach to scam defence.
Understand Customer Susceptibility
Scam prevention begins with a proactive evaluation of customer vulnerability through sophisticated susceptibility scoring. This involves collecting data from monetary transactions and non-monetary events across all customer touchpoints to generate real-time, evolving customer profiles. It’s important to note that this approach must comply with local privacy and permissibility requirements.
Create Robust Customer Personas
Creating customer personas based on behavioural characteristics can help assess specific scam vulnerabilities. For instance, customers with high investible income involved in cryptocurrency trading may be more susceptible to investment scams. Understanding your customers is crucial in protecting them.
Deploy Targeted, Personalised, Proactive Communication and Education
General scam warnings often prove ineffective. Instead, the framework emphasises hyper-personalised, contextual messaging aligned to individual risk profiles and scam types, leading to more informed and alert customers. It’s crucial to break the scammer’s spell.
Alert and Amplify with the Susceptibility Score
Effective scam detection involves sophisticated monitoring of customer behaviour and activity. The framework recommends multi-layered decisioning that first identifies anomalies, then determines if they’re associated with scams or traditional fraud. Enterprise fraud capabilities can enhance transaction scores based on customer susceptibility and personas.
Build Dynamic In-Journey Engagement
Recognising that customers in ‘hot states’ often disregard generic warnings, the framework emphasises dynamic, personalised dialogue that creates appropriate friction and reflection opportunities. This could include cooling-off periods or post-transaction follow-up when customers are more receptive.
Close the Back Door
As stolen funds usually pass through mule accounts, the framework emphasises real-time intervention capabilities beyond traditional anti-money laundering controls. This involves shifting from monthly batch assessments to instantaneous monitoring and account freezing.
Collaborate Across the Ecosystem
Establishing a formal ecosystem across regulators, law enforcement, telcos, social media platforms, and industry bodies can facilitate data sharing and best practices. This is perhaps the most challenging task.
How to Operationalise the Enterprise Fraud Framework
Preventing scams involves more than traditional fraud controls — it necessitates a sophisticated, data-driven platform capable of analysing vast volumes of signals in real time, while maintaining the right balance between customer protection and experience. To operationalise this framework, financial institutions can incorporate advanced analytics, orchestration, and engagement capabilities across the customer lifecycle.
The Path Forward
The increasing prevalence of scams poses a significant threat to customer trust and institutional stability. Financial institutions that wait for regulatory mandates or perfect solutions may find themselves at a disadvantage.
Effective scam prevention requires rigorous measurement and tagging of both structured and unstructured data to create virtuous feedback loops. Constant engagement with operations teams and active monitoring are also critical to identify new vectors and anticipate emerging attack patterns.
As the threat landscape evolves, financial institutions must adopt proactive, intelligence-driven strategies that protect customers while maintaining operational efficiency. The seven-step framework can serve as a roadmap to transform scam prevention from a compliance obligation into a strategic advantage and position for success in an increasingly complex risk environment.
Vineet Saxena, Fraud and Credit Fellow at FICO
