by Tamas Kadar
In an age of omnipresent online transactions, fraud is growing alarmingly. Consumers reported1 losing over $10 billion to fraud last year, marking a 14% increase from the previous year. This figure underscores a critical misconception: many still view fraud as a singular event – a one-time act that can be easily detected and prevented.
But this mindset overlooks the reality: fraud is a constant, evolving threat, omnipresent and shifting in form. Even if you thwart one type of attack, it crops up through another vector, repeatedly and methodically searching for any opportunity to abscond with a profit. To tackle this challenge, businesses need more than isolated measures – companies today require a comprehensive, always-on approach. By shifting from static checks to continuous monitoring, layered defences and smarter transaction analysis, businesses can stay ahead of evolving fraud.
The Evolution of Fraud: Adapting to New Realities
As a dynamic threat, fraud evolves alongside technological advancements, constantly adapting to circumvent even the most sophisticated security measures. In the past, fraudsters relied on straightforward tactics such as forgery. Today, bad actors employ complex strategies – like social engineering and synthetic identity fraud2, where they fabricate identities using a blend of real and fake information – to exploit vulnerabilities.
Understanding fraud’s lifecycle is one key to combating it more effectively, as each phase presents unique opportunities. When organisations implement monitoring strategies designed to identify anomalies before they escalate across their customer experiences, they can prevent fraud events and reduce (or even eliminate) significant financial losses. By better understanding the various stages of fraud, organisations can better prepare themselves against this persistent threat.
Why Traditional Approaches Fall Short
Traditional fraud prevention methods and relying solely on static detection techniques, like point-in-time checks and manual reviews, can lead to significant security gaps. These methods frequently overlook the entire user journey, and since fraud can occur across stages, concentrating only on isolated incidents may mean that organisations miss critical opportunities for intervention.
While static solutions may catch overtly fraudulent activities, these methods typically lack the context needed to differentiate legitimate transactions from suspicious ones within broader user behaviour patterns. Another major limitation of traditional fraud measures is their reactive nature; as fraudsters refine their strategies, traditional methods simply can’t keep pace. With the focus on historical data rather than real-time analysis subtle anomalies – potential indicators of fraud – often go unnoticed until it’s too late.
Continuous Monitoring
Continuous monitoring tracks user behaviour at every stage of interaction with a platform, from registration and login to account updates and transactions. By scrutinising these details, patterns and contexts, organisations can identify anomalies that deviate from typical behaviour. For instance, if a user who usually logs in from a specific location suddenly attempts to change their password or account details from a high-risk country, this red flag can trigger an immediate investigation. Ongoing scrutiny enables businesses to respond swiftly to potential threats, often pre-emptively addressing issues before they escalate.
This approach also enhances compliance efforts and decision-making when manual reviews are required, while customers benefit from enhanced security measures that protect their data while enjoying uninterrupted access to services.
Behavioural Tracking
Behavioural tracking enhances fraud detection3 by analysing real-time data against established customer behaviours. This process works hand-in-hand with continuous monitoring and employs predefined rules and thresholds to identify deviations. If a fintech customer registered in the U.S. suddenly logs in from a different IP address and attempts to update account details or send money to a person who lives in a high-risk region, the system can flag these actions for further review. By comparing real-time data with historical patterns, organisations can effectively spot irregularities that may indicate fraudulent activity.
Machine Learning
Incorporating machine learning and artificial intelligence (AI) into fraud prevention strategies significantly boost an organisation’s ability to identify anomalies indicative of fraudulent behaviour. These technologies analyse vast amounts of data rapidly, uncovering even the most subtle irregularities across every interaction. By leveraging advanced algorithms, machine learning systems4 continuously learn from new data, adapting to emerging fraud patterns and improving detection capabilities over time. This additional layer of security ensures that organisations remain one step ahead of fraudsters, allowing for timely interventions stopping fraud before it happens that protect both the business and its customers.
The Best Defence is a Layered Defence
A robust defence against fraud requires a multilayered strategy that combines multiple protective measures to create a comprehensive barrier against fraudulent activities. This layered approach ensures that even if one method does not detect anything alarming, others might spot something suspicious, eliminating possibilities to circumvent security measures.
Together, these strategies embody a culture of continuous awareness and improvement. Staying ahead of fraud requires regular system updates, leveraging the latest intelligence and fostering vigilant, well-trained teams ready to detect and counteract emerging risks. By prioritising adaptability and vigilance, businesses can build a resilient defence that evolves just as swiftly as the threats it combats.
Looking Ahead: The Future of Fraud Prevention
The future of fraud will be defined by two opposing forces: the rapid advancement of technology empowering fraudsters with more sophisticated tactics and the strategic adoption of emerging solutions to stay one step ahead in fraud prevention. Innovations like artificial intelligence, automation, highly customisable rule systems and behavioural analysis are revolutionising detection capabilities. These technologies allow organisations to analyse vast amounts of data in real time, uncovering subtle patterns and outsmarting bad actors wherever they try to infiltrate their systems.
Yet, technology alone cannot effectively combat fraud. Collaboration across industries and within your own organisation across departments is essential to stay ahead. By sharing information and insights, organisations can develop more robust detection strategies and mount a unified response to evolving threats.
While there may not be a silver bullet solution for preventing fraud, implementing a well-designed and continuous fraud prevention strategy offers the best defence against an ever-evolving threat landscape.
(*2) https://seon.io/resources/synthetic-identity-fraud-prevention-and-detection/
(*3) https://seon.io/resources/fraud-detection-and-prevention/
(*4) https://seon.io/products/ai-machine-learning-solution/
The author, Tamas Kadar, is CEO of SEON.
