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Beyond averages: the power of anomaly detection in identifying and stopping payment fraud

Every minute, £1.73 million vanishes to payment fraud, leaving businesses and individuals vulnerable. Forget tired, average-based detection systems—the answer lies in anomaly detection, a revolutionary technology that hunts for the unexpected, the transactions whispering “fraud.”

This article cuts through the jargon, revealing how anomaly detection works, why it’s a game-changer, and how to leverage it for maximum impact. We’ll explore real-world success stories, peek into the future of fraud prevention, and empower you to take control of your financial security.

The limit of averages

Imagine a thief blending seamlessly into a crowd, not by mimicking the masses but by subtly tweaking his appearance. That’s the reality of sophisticated fraudsters exploiting the limitations of average-based detection systems.

These systems rely on historical data to define “normal” spending patterns, but crafty criminals navigate just outside these boundaries, often making small, frequent transactions that seem harmless on their own.

In 2022 alone, the US saw over 610,000 reported cases of account takeover, highlighting the vulnerability of average-based methods. 

Take the case of a recent data breach where hackers obtained credit card information. Instead of splurging on luxury items, they made numerous micro-transactions for everyday purchases, mimicking legitimate behaviour and evading detection for weeks.

This is where the power of anomaly detection shines. It doesn’t get caught up in averages; it hunts for the unusual, unexpected deviations in payment fraud that could signal fraud.

By analysing individual spending patterns and identifying subtle anomalies, anomaly detection can unmask these “average” thieves before they cause significant damage.

Enter the anomaly detectives

Forget clunky, outdated systems that rely on averages. Enter the world of anomaly detection, the game-changers of fraud prevention. Imagine a team of vigilant detectives meticulously analysing your financial data, not for what’s “usual,” but for what’s unusual—the tiny discrepancies that could whisper “fraud.” That’s the essence of anomaly detection.

How it works

Think of your financial behaviour as a unique fingerprint. Anomaly detection algorithms learn this fingerprint by analysing your past transactions, spending patterns, and preferences.

Any significant deviation from this established norm–a sudden spike in spending, a suspicious purchase from an unfamiliar location, or a flurry of unusual transactions–raises an alert.

It’s like the detectives noticing someone trying to use a forged fingerprint – a clear sign of something amiss.

But how do these detectives work? They come in different flavours, each with its strengths:

Statistical detectives

These rely on mathematical models to identify data points far outside the expected range. Imagine a bell curve of your typical spending. They flag anything that falls significantly beyond the curve’s edges.

Machine learning detectives

These are trained on vast amounts of data, learning to recognise patterns and anomalies independently. Think of them as experienced crime investigators, constantly honing their skills to spot suspicious behaviour.

Artificial neural network detectives

These complex systems mimic the human brain, analysing data through interconnected layers of neurons. They can detect subtle patterns and relationships that might escape other methods, acting like a team of highly specialised detectives collaborating to solve a complex case.

Anomaly detection doesn’t just sound cool; it’s already stopping millions of fraudulent transactions every year.

In 2022 alone, it prevented an estimated £23 billion in losses for financial institutions globally, according to Juniper Research. This is just the beginning – as anomaly detection evolves, it promises to become the ultimate guardian of your financial security.

Anomaly detection in action

Anomaly detection isn’t just theory; it’s a shield against real-world fraud. Here are a few examples of its impact:

Foiling account takeover

Able Company was the victim of a cyber-account takeover when malware on their computers hijacked a customer’s online banking session. The malware obtained credentials to create wire templates and initiate millions of unauthorised transactions. The fraud was discovered through the financial institution’s protocols, leading to the cancellation of the fraudulent wires and the disconnection of infected computers. The FBI was notified for further investigation.

Catching card cloning

IT expert Kenneth Gibson exploited his access to steal data from 2012-2017. He built software to create fake PayPal accounts and linked credit lines, siphoning millions by transferring small amounts and taking cash advances. His scheme netted $3.5 million before his 8,000 account fraud was discovered.

Thwarting social engineering

In July 2020, hackers exploited Twitter employees through social engineering, compromising high-profile accounts to promote a Bitcoin scam that netted $180,000. The incident caused a 4% stock price drop and triggered security protocol updates, highlighting the need for user entity and behaviour analytics (UEBA) and privileged access management (PAM) solutions to prevent such insider threats.

Beyond preventing fraud in online transactions, anomaly detection shines in other areas:

Identifying fake accounts

Online marketplaces use anomaly detection to analyse user behaviour and flag suspicious registrations. This helps weed out fake accounts created for fraudulent purposes, safeguarding the platform’s integrity and protecting legitimate users.

Detecting money laundering

IT expert Kenneth Gibson exploited his access to steal data from 2012-2017. He built software to create fake PayPal accounts and linked credit lines, siphoning millions by transferring small amounts and taking cash advances. His scheme netted $3.5 million before his 8,000 account fraud was discovered.

Anomaly detection isn’t magic, but it’s a powerful tool in the fight against fraud. By learning the unique fingerprint of your financial behaviour and constantly searching for deviations, it acts as a vigilant watchdog, protecting you and the wider financial ecosystem from ever-evolving threats.

Building an anomaly detection fortress

Anomaly detection is a powerful weapon, but like any weapon, it needs the right ammunition. That’s where data preparation and human expertise come in.

First, lay the foundation: Your anomaly detection system is only as good as the data it analyses. This means collecting the right financial data points, from spending patterns and transaction locations to device details and login times. Ensuring accurate, complete, and up-to-date data is crucial for effective anomaly detection.

Next, build the walls: Consider data preparation as cleaning and organising your data, making it clear and readable for the system. This involves removing anomalies in the data itself, handling missing information, and standardising formats. Preparing the battlefield before the fight is like ensuring smooth operation and accurate analysis.

But even the best tools need skilled hands. This is where human expertise shines: Humans interpret the alerts generated by the system, making crucial decisions about their legitimacy and potential actions. This is why the best bookkeeping courses, cyber security degree programmes, etc., increasingly integrate anomaly detection into their curricula.

Think of humans as the generals strategising with the insights from their AI soldiers. They assess the context, investigate suspicious activity, and decide whether to sound the alarm or initiate further investigation.

This synergy between humans and machines is what makes anomaly detection truly powerful. The system identifies the unusual, and humans provide the judgement and analysis needed to turn those clues into actionable insights.

Remember, anomaly detection isn’t about replacing human expertise; it’s about augmenting it with powerful technology to create a robust defence against fraud.

The future of fraud prevention

The fight against fraud never sleeps, and neither does anomaly detection. Here’s a glimpse into its exciting future through these payment trends:

  • Smart scoring: Anomaly scoring goes beyond simple alerts, assigning specific risk scores to each anomaly. This prioritises threats, guiding human investigators toward the most pressing cases.
  • Explainable AI: Think of AI as a black box; now, imagine unlocking it. Explainable AI helps understand how the system identifies anomalies, builds trust, and promotes informed decision-making.

But staying ahead requires teamwork:

  • Financial powerhouse collaboration: Sharing data and insights between banks, payment processors, and tech companies allows for comprehensive threat profiles and faster response times.
  • Joined forces with governments: Regulatory bodies and law enforcement collaborating with the private sector strengthen legal frameworks and disrupt global fraud networks.

Beyond averages: embrace the future of security

The era of “average” fraud prevention is over. Anomaly detection offers a revolution in protection, a proactive shield against hidden threats. We encourage businesses and individuals alike to embrace this powerful technology.

For businesses, it means safeguarding customers and building trust. For individuals, it’s the key to financial peace of mind.

Let’s join forces, harness the power of anomaly detection, and write a new chapter in the fight against fraud. In this chapter, security, not suspicion, defines the future of our financial lives.

Security statement

Security is our top priority at Trust Payments and we strive to ensure that all data is kept secure at all times We keep all customer data safe with AES256 encryption, SSL Certificates, and a minimum of TLS1.2, between your website and our datacentres.

Our systems are scanned quarterly using the Qualys PCI Platform, an independent Qualified Security Assessor (QSA) and approved vendors – Omnicybersecurity (UK) & Forgenix (US) – to ensure compliance with the security requirements of the card schemes.

We follow a number of rigorous security procedures on a daily basis including, but not limited to, continuous monitoring of our perimeter, dark web monitoring, and internal checks to ensure that CIA triad is maintained at all times.

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