Anomaly Detection Techniques in Frauds

Businesses, financial institutions, and even governments suffer plenty of frauds and lose billions every year. According to the Global Economic Crime Survey 2024 report, 59% of businesses have borne financial as well as economic fraud in the last two years. Anomaly detection came as a weapon against frauds that are increasing day by day. This guide will deliver the knowledge regarding the workings and cruciality of the anomaly detection in frauds.

What is Anomaly Detection?

Anomaly detection is the process of finding things that don’t fit. Simple as that. When things go out of control—whether it’s an unusual transaction, a strange pattern, or some unexpected behavior—that’s an anomaly. In fraud, these anomalies are like the red flags waving in your face, telling you something’s off.

Types of Anomalies in Fraud Detection

Before we get into the techniques, let’s quickly look at the different types of anomalies fraud detectors target:

• Point Anomalies: These are single data points that stand out like a discrepancy. Imagine a huge money transfer out of a bank account that’s usually inactive. It’s a huge red flag.
• Contextual Anomalies: Sometimes, data might look fine on its own but stands out in a certain context. Like if someone withdraws money at 3 AM instead of regular working hours. Odd? Yes. Fraud? Maybe.
• Collective Anomalies: This is when a bunch of data points together don’t match the usual pattern, even if each point seems okay. Multiple small transactions happening quickly? A definite fraud warning sign.

Key Anomaly Detection Techniques in Fraud Detection

Now, let’s talk about how to catch these weird patterns. Here are some of the top techniques used in fraud detection:

1. Statistical Methods

This is the classic approach—let’s establish a “normal” pattern and then see what is noticeable. Think of it as setting up a baseline and comparing everything else to it.

• Z-Score: A Z-score tells you how far a data point is from the average. If something’s way off—like a huge withdrawal when it’s not typical—this method will scream, “Look here!”
• Moving Averages: This method tracks data over time. If a transaction suddenly breaks from the usual trend, it’s flagged as suspicious. It’s like tracking your own spending habits and spotting when something’s way out of line.

These methods are quick and easy, but they might miss complex fraud patterns, especially in large datasets.

2. Machine Learning Algorithms

Machine learning (ML) is where things get really powerful. ML can learn from vast amounts of data and spot patterns humans can’t even imagine. Here’s how ML helps in fraud detection:

• Supervised Learning: This method trains the system using data that’s already labeled. It knows which transactions were legit and which ones were fraud. Over time, it learns to spot fraud on its own. Algorithms like decision trees and random forests are great here.
• Unsupervised Learning: This one’s for when you don’t have classified data. The system looks for irregularities, things that don’t fit the norm. Algorithms like collecting and nearby entities come into play here. It’s like trying to spot fraud without knowing exactly what it looks like.
• Deep Learning: For the really complex fraud patterns, deep learning steps in. Using neural networks, these models can learn super-complex patterns and flag hidden fraud. Think of it as catching fraud that’s camouflaged in a sea of data.

While powerful, machine learning requires a lot of data to be effective. More data means better fraud detection, but also more work.

3. Rule-Based Systems

Sometimes, fraud detection doesn’t need to be so fancy. Rule-based systems work by applying specific, predefined rules. If a rule gets broken, it’s flagged.

For example, a rule could say, “Flag any transaction over Rs1,00,000.” It’s simple, but effective for catching obvious fraud like huge withdrawals or transfers. The problem is, fraudsters know these rules too. They can easily work around them by breaking up transactions into smaller amounts.

4. Behavioral Analytics

Fraud often happens when people act out of character. That’s where behavioral analytics steps in. By analyzing how people usually behave, you can spot when something’s off. For instance:

• If an employee who never takes time off suddenly refuses to take a vacation, that’s odd.
• If someone’s online shopping history suddenly changes, like buying high-end electronics after only ever buying clothes, that’s suspicious.

Behavioral analysis is personalized and dynamic, adjusting to each person’s behavior, making it a powerful tool to spot insider fraud or identity theft.

5. Network-Based Anomaly Detection

Fraud doesn’t happen in isolation. It’s usually connected to other accounts, vendors, or customers. Network analysis looks at how all these entities are connected and spots any strange relationships or activities. For example:

• If a new account starts interacting with numerous unrelated accounts, it could signal fraud.
• Multiple transactions coming from different people but all directed to the same address? That’s another red flag.

Network analysis is a great way to spot fraud that flies under the radar of other techniques because it looks at the bigger picture.

6. Time-Series Analysis

Fraudsters often try to mess with the timing of transactions. Time-series analysis is about detecting strange patterns over time. For example:

• If transactions suddenly spike at odd hours, like at 2 AM, it could be fraud.
• Rapid consecutive transactions that don’t follow the usual pattern? A clear sign something’s off.

Time-series analysis helps catch these time-based anomalies, making it an essential tool for spotting fraud in real-time.

Challenges in Anomaly Detection for Fraud

Despite all these powerful techniques, detecting fraud is no easy task. Here are some challenges:

• False Positives: Not every anomaly means fraud. Some legitimate transactions get flagged, causing unnecessary work. Fine-tuning your system to avoid this is key.
• Data Quality: Anomaly detection systems rely on clean, accurate data. If the data’s messed up, the system won’t work right.
• Adapting to New Fraud: Fraudsters don’t sit still. They constantly change their tactics, so your detection system needs to keep up. Without regular updates, your system might miss new fraud schemes.

Questions to Understand your ability

Q1.) What’s an example of a point anomaly in fraud detection?

A) A tiny transaction happening at a weird time
B) A huge, out-of-place withdrawal from an account
C) A bunch of small transactions in a short span
D) Random connections between different vendors

Q2.) Which fraud detection method uses labeled data to teach the system what’s legit and what’s shady?

A) Supervised learning
B) Unsupervised learning
C) Rule-based systems
D) Network-based analysis

Q3.) What’s the biggest flaw of rule-based systems when detecting fraud?

A) They demand massive amounts of data
B) Fraudsters can simply split up large transactions to dodge rules
C) They can’t catch fraud in network connections
D) They can’t keep up with the constantly changing fraud tactics

Q4.) What does behavioral analytics focus on when detecting fraud?

A) Spotting odd financial transactions
B) Tracking how people act compared to their usual habits
C) Finding weird links between vendors
D) Hunting for strange patterns in time-based data

Q5.) What’s the biggest challenge with anomaly detection systems in fraud detection?

A) They don’t catch time-related fraud
B) They fail when the data isn’t clean or accurate
C) They only use statistical analysis
D) They’re too focused on spotting fraud from one vendor

Conclusion

Anomaly detection is a tool that can be related to a sword in a battle against fraud. There are several methods, such as statistical ones and machine learning, for detecting suspicious activity, but no method is reliable enough. Fraud is a transforming opponent, and so must be the systems meant to prevent it. Using a mix of methods and updating the system with time will assist the business to align with the new emerging patterns.