What is Fraud Score & How Does it Work? The Ultimate Guide

In 2022, there were about 678 billion credit card transactions worldwide, meaning; around 1.86 billion happened daily, 77.4 million each hour, 1.29 million per minute, and 21,510 every second. With so many transactions, an important question remains: How do fraud scores function?

What is a Fraud Score?

A fraud score is a number that helps businesses determine the risk level of a transaction so the higher the score, the more likely it is that the transaction could be fraudulent.

Fraud scores are different from credit scores, where the first considers factors like transaction type, amount, location, and any history of fraud, and the latter depends on a person’s credit history.

Software like FOCAL Fraud Prevention uses machine learning to calculate these scores, improving accuracy over time. The fraud prevention solution assesses risk by looking at user actions, like signing up, logging in, or making a payment, and data points, such as IP addresses, email addresses, and device details.

To put it into perspective, an email might be flagged if it's on a known blacklist, or an IP address might raise concerns if linked to Tor nodes, which are servers that collaborate to form a secure, private, and anonymous route for traffic within the Tor network. Each data point adds to the overall fraud score, with specific scores like IP fraud scores focusing on particular risks to provide a more precise risk assessment.

How Does a Fraud Scoring System Function?

Fraud scoring systems evaluate the risk of fraudulent activity in transactions or user actions by assigning a numerical value or score. This score helps businesses assess the likelihood of fraud and take appropriate measures.

1. Data Collection

Various data points are collected from transactions or user interactions, such as:

• IP Address: The geographical location and history of the IP address.

• Device Information: Details about the device being used, including type, configuration, and any prior association with fraudulent activities.

• Transaction Details: Information on the amount, frequency, and nature of transactions.

• User Behavior: Patterns in user activities, such as login times and browsing behavior.

• Historical Data: Previous interactions and any record of fraudulent behavior.

2. Algorithmic Analysis

The system processes the collected data using complex formulas (strengthened by machine learning and artificial intelligence as they continuously refine their accuracy by learning from past transactions). These formulas identify unusual patterns and irregularities that may indicate fraud!

3. Fraud Scoring

After analysis, every transaction or user behavior is assigned a fraud score, usually ranging from 0 to 100, where higher scores signal a higher likelihood of fraud.

4. Thresholds and Decision-Making

Organizations set threshold levels to determine the appropriate actions based on the fraud score:

• Low Score: Indicates low risk; the transaction is usually processed without further checks.

• Medium Score: This indicates moderate risk; additional verification steps might be triggered, such as two-factor authentication or manual review.

• High Score: Reflects significant risk; the transaction might be flagged for deeper review, rejected, or canceled.

5. Continuous Learning and Improvement

Fraud scoring systems are dynamic and continually evolve. They update their algorithms and fraud scoring models based on new data and feedback from detected fraud cases, which enhances their effectiveness and accuracy over time.

Example of Fraud Score Calculation

In an eCommerce transaction, the system collects details such as a) the buyer’s IP address, b) device type, c) shipping address, and d) purchase history. These data points are then processed through advanced algorithms designed to detect known fraud patterns, like mismatched shipping addresses or multiple transactions originating from different locations in a short period.

The system assigns a fraud score, let’s say 85 out of 100, which indicates a high level of risk. If the score exceeds a predefined threshold, such as 70, the transaction is flagged for manual review. Your fraud prevention team would then examine the flagged transaction to determine whether it should be approved or declined based on their findings.

What are the Key Factors Considered in Calculating a Fraud Score?

Despite the lack of consistency in standards across various companies regarding fraud rating, when fraud professionals need to calculate a fraud score, they must consider the following factors.

1. Examining historical data to identify patterns of fraudulent behavior.

2. Monitoring for unusual or suspicious behavior, such as changes in spending habits or transaction frequency.

3. Analyzing geolocation data to detect anomalies in transaction locations.

4. Assessing device characteristics, like IP addresses and browser types, to identify potential fraud.

5. Evaluating transaction velocity to flag unusually rapid transactions.

6. Analyzing account age and activity frequency to detect potentially fraudulent accounts.

7. Checking IP address reputation for any history of suspicious activity.

8. Verifying the identity of individuals involved in transactions through various authentication methods.

9. Screening against blacklists and watchlists of known fraudulent entities.

10. Utilizing machine learning algorithms to analyze data and detect fraud patterns.

11. Employing anomaly detection techniques to identify deviations from normal behavior.

12. Conducting social network analysis to uncover potentially fraudulent networks.

13. Considering industry-specific indicators to detect fraud relevant to the particular sector.

The Role of Fraud Scoring Systems

Fraud scoring systems work to stop many fraud types, like payment fraud, identity theft, and phishing. They look at different data, transactions, and user actions to find signs of fraud.

Fraud rating systems identify unauthorized purchases using stolen card details or fake payment methods to detect payment fraud. For account takeover (ATO), the fraud scoring system flags unusual activities like logins from unfamiliar locations or devices (which indicate potential unauthorized access).

Fraud scoring systems look at each transaction in real-time and quickly alert your team to risky activities, so you can act and make decisions faster.

8 Advantages of Fraud Scores

Fraud scores provide several advantages for businesses aiming to combat fraudulent activities in transactions:

1. Identifies fraud as it happens, allowing quick action.

2. Automates fraud detection for quicker transaction processing.

3. Reduces fraud impact, building trust and loyalty.

4. Cuts manual review costs and focuses resources on high-risk cases.

5. Adapts to growing transaction volumes without losing performance.

6. Can be tailored to specific business needs and industry standards.

7. Helps meet legal and regulatory requirements.

8. Uses algorithms to accurately spot fraud, reducing mistakes.

Key Challenges with Fraud Scoring Systems

Only a comprehensive fraud prevention solution can effectively strike the right balance in fraud detection sensitivity. While minimizing false positives (legitimate transactions mistakenly flagged as fraud) is crucial for ensuring a seamless customer experience, it's equally essential to reduce false negatives (fraudulent transactions that slip through undetected) to effectively combat fraud and protect against financial losses.

• Sometimes, fraud scoring systems can make mistakes, flagging real transactions as fraud (false positives) or missing actual fraud (false negatives).

• If the data isn't accurate or complete, the system won't give reliable results.

• Even though fraud scoring systems are automated, human review is still often required for flagged transactions.

Read more: Best Fraud Detection Software: 6 Companies Compared in 2024

Get Started with Assigning Accurate Fraud Score with FOCAL

Consider a scenario where a retail bank implements FOCAL's fraud scoring system to enhance its security measures.

1. User Registration

A new customer signs up for online banking services with basic information, such as name and email address.

2. Data Enrichment Process

The advanced modules of FOCAL conduct thorough digital footprinting and device fingerprinting analyses. These processes uncover the user's IP address, device specifications, browser details, and associated social profiles.

3. External Data Cross-Referencing

The collected data is enriched by cross-referencing it with external databases. FOCAL retrieves supplementary information, such as the user's email domain's reputation, any past fraudulent activity associated with the provided email address, or whether the user's device has been flagged in previous fraud cases.

4. Predictive Analysis

The enriched data undergoes rigorous analysis through the predictive rule engine of FOCAL. Complex algorithms assess various risk factors, considering the user's transaction history, geographical location, and behavioral patterns.

5. Fraud Score Generation

Based on the results of the predictive analysis, FOCAL generates a fraud score indicating the level of risk associated with the user. This score helps the business determine whether additional verification measures are necessary before granting access to sensitive banking services.

Conclusion

Fraud scoring systems are super important for all sorts of industries, like banking, e-commerce, and telecommunications. Fraud scores analyze transaction data in real time to spot anything that seems off (eg: strange spending patterns, mismatched shipping addresses, or transactions happening in different locations really quickly)!

In the banking industry for example, fraud scoring systems are great at spotting stolen card information or suspicious account access. For e-commerce, they help flag risky purchases before they go through. Telecom companies use them too, to detect account takeovers or SIM card swaps.

Feel free to reach out if you’d like to learn more about our fraud scoring system or any of our other fraud prevention products. Schedule a demo today!

Frequently Asked Questions Related to Fraud Risk Scoring

Q1. How accurate is fraud risk scoring?

The accuracy of fraud risk scores hinges on several factors, including the quality of available data, the sophisticated algorithms employed, and the effectiveness of the fraud prevention system. Regular monitoring and refinement of the fraud scoring model are vital for maintaining high accuracy levels.

Q2. Can fraud risk scoring be automated?

Fraud risk scores can be automated using machine learning and artificial intelligence algorithms. Automation allows for real-time transaction analysis, facilitating swift decision-making and aiding in fraud prevention, especially in fast-paced environments like online transactions.

Q3. What does a fraud score of 89 mean?

A fraud score of 89 signifies a substantial risk level associated with a transaction or activity.

Q4. What is the threshold for fraud score?

The threshold for a fraud score varies and is determined by factors such as organizational risk tolerance and specific fraud scoring systems. For example, one organization might set a threshold of 80 to trigger additional scrutiny, while another might set it at 90. Generally speaking, however, a fraud score >= 85 is highly suspicious.