What Is Fraud Scoring? A Guide for Businesses (2025)

In a perfect world, a business would only need to focus on offering products and services to customers. The customer would simply pay for a service and the business would simply provide it to them. 

However, with 67% of companies mentioning an increase in fraud attempts, businesses need to make sure they are doing all they can to protect themselves from fraudsters and their ever-adapting tactics. 

Fraud scoring software plays a key part in making our fraudster-laden reality closer to this perfect world. It does this by issuing “fraud scores”, which help prevent interactions with potential fraudsters while making sure genuine customers enjoy the simple streamlined services they expect. 

Not only does a fraud score help in compliance with Know Your Customer (KYC) and Anti-Money Laundering (AML) laws, but it also helps protect businesses from some of the most common types of fraud like account takeover fraud. 

Suggested read: Adaptive, Accurate, Efficient: How Dynamic Risk Scoring Elevates Compliance

What is a fraud score?

A fraud score is a number aggregated from multiple risk factors representing the risk of fraud for a specific user, account, or transaction. The higher the number, the higher the risk of fraud. 

Fraud scores are provided with the help of AI algorithms or machine learning models (AI/ML) that allow businesses to then act accordingly on this fraud score, either by automatically approving or blocking a transaction, or by requesting additional measures like 2FA (two-factor authentication) or a liveness check.  

Fraud scoring platforms use a vast array of factors to calculate fraud scores, including:

• device intelligence (device type and OS, browser fingerprinting, timezone and language settings, installed plugins, multiple accounts tied to one device, etc.)
• behavioral patterns
• IP address
• email address
• historical usage patterns. 

For example, if an email used for a transaction is known to have been exposed online, the IP address is suspected to be a Tor node, or a credit card reported stolen is used, it will affect the fraud score. 

Like credit scores, fraud score numerical ranges depend on the platform used. These may be expressed from 0 to 1000, 0 to 100, or even 0 to 10. Thresholds also vary. For example, a fraud score of 100 could be considered incredibly high or fairly low depending on the platform.

Generally, a low fraud score means the transaction will be automatically authorized. A medium fraud score may mean an additional form of verification is needed, such as a liveness check. A high fraud score may mean it is best to automatically block the transaction. 

Why is fraud scoring important?

Fraud scoring allows businesses with a high volume of online transactions—like trading platforms—to easily identify cases of suspected fraud at scale. Fraud scores help organizations quantify levels of risk and allow them to decide how to act accordingly. This means legitimate users can enjoy a smoother customer experience while cases of suspected fraud are either blocked or flagged for further investigation.  

Efficient fraud scoring saves resources and allows institutions to focus on legitimate threats. iFAST Global Bank, for example, uses Sumsub’s Email and Phone Risk Scoring alongside Fraud Network Detection to combat fraud more effectively.

Michael Dare, an iFAST fraud investigator, says this technology has significantly helped in identifying groups of fraudulent users in emerging markets: “By analyzing shared IP locations, devices, and fingerprints, we have been able to detect and prevent multiple fraudulent accounts operated by the same network.”

However, effective fraud scoring strategies are about more than just keeping businesses safe from fraud. At scale, fraud scoring places significant obstacles for fraud and increases the likelihood that criminals can be detected and automated actions taken to stop them prior to doing any harm. 

Fraud scoring can also play a part in the following: 

• Enhancing identity verification and KYC processes: Fraud scoring complements standard KYC processes by assessing a customer’s risk level using additional checks. In addition to verifying documents and cross-referencing trusted sources, fraud scoring leverages data points like information about the device (device types and OS, browser fingerprinting, accounts tied to one device, etc.) email reputation, phone verification, and digital behavior for enhanced identity verification.
• Supporting AML/KYC compliance: Fraud scoring assists organizations in meeting AML and KYC regulatory obligations by flagging suspicious activity. It also provides tangible evidence of compliance efforts, helping demonstrate adherence to regulatory guidelines.
• Spotting fraudsters early: Fraud scoring can help spot fraudsters early, potentially saving companies the cost of verifying the identities of fraudulent users.  
• Breaking up money laundering rings: By detecting the use of fake or stolen identities, as well as money mules, fraud scoring enables businesses to block fraudulent users, helping dismantle money laundering rings without disrupting the experience of legitimate customers.
• Better gaming experiences: Fraud scoring can help online gaming platforms by identifying cheats and fraudsters to offer a more secure gaming environment.
• Combatting chargeback fraud: E-commerce businesses may use fraud scoring to reduce instances of chargeback fraud. 
• Fighting payments fraud: Fraud scoring empowers businesses to block transactions involving stolen cards, fake accounts, and misused assets like gift cards or digital coupons.

Which industries benefit from fraud scoring?

Fraud scoring is useful in any industry where there is a risk of fraud. For example, it benefits industries that feature digital identities, confidential processes, or a large number of transactions.

Not only does fraud scoring help protect businesses from threats like payment fraud, but it also reduces the number of false positive reports. This makes fraud scoring particularly useful for large businesses that want to automate decisions at scale, helping to provide faster and more secure onboarding. It also aids in AML/KYC compliance, building user trust, and saving investigative resources.

Fraud scoring is an invaluable tool in many industries, including:

• Banking
• Fintech
• iGaming
• E-commerce
• Crypto and blockchain platforms
• Educational services
• Transportation services
• Travel and hospitality
• Trading and brokerage platforms
• Insurance

How is a fraud score calculated?

Different fraud score platforms use different methods of calculating risk. Generally, however, fraud scoring platforms analyze data on devices, transaction, account, and user behavior through set rules, statistical models, and AI/ML to determine risk levels.These factors are weighted for their relevance to the risk of fraud. They are then factored together to determine a combined numerical value as a fraud score. The higher the score, the higher the chance of fraud.

Key factors influencing fraud scores 

A large number of factors are considered in calculating fraud scores. These may include:

• Identity indicators—Email address age and reputation, email type (e.g., free or business), email verification, phone number verification, social media presence
• IP information—IP address analysis, logins from high-risk countries, IP associated with fraud, VPN usage, Tor network notes
• Device information—Device fingerprinting (i.e., identifying information on device software and hardware), rooted or jailbroken device, multiple accounts on one device
• Historical information—Account age, previous purchases, any earlier links to fraud
• Transaction patterns—Whether the shipping address and billing address match, risk of product type, time of transaction, risk of payment method (e.g., card details stolen and sold on the black market), unusual purchase amount
• User information—Previous purchase history, account history, navigation movements, account age, checkout completion time, geolocation, personal information crossmatches
• Blacklisted user—Names, email addresses, phone numbers, or devices previously linked to fraud or criminal activity 

As patterns of fraud are changing, these factors may not be enough to identify all instances of fraud and can often lead to false positives. This is why it is important to use a fraud scoring tool with access to real-time AI/ML algorithms and up-to-date databases.

Implementing fraud scoring in your business

Choosing the right fraud scoring tool

Fraud losses are on the rise and expected to surpass $40 billion by 2027. Accordingly, the fraud detection market is growing, resulting in a large number of fraud detection software and fraud scoring tools. Knowing which to choose comes down to the following questions:

• Does the software effectively use AI/ML? 
• Does it use the most up-to-date information for real-time monitoring?
• Does it have access to reliable data? 
• Does it provide enough fraud signals?
• Does it provide dynamic fraud scoring?
• Is it adaptable and user-friendly?
• Does it meet regulatory standards?   
• Does it meet the needs of my business? 

As every business has different requirements and the nature of fraud detection changes, it’s important to use a fraud scoring tool that’s adaptable to your business needs in real time. Good tools should have customizable risk models, AI/ML integration, and real-time scoring.

Trust, risk tolerance, and industry type are also key considerations. Good risk-scoring tools are transparent, scalable, and easy to use, helping you strike your unique balance between fraud detection and a smooth user experience for legitimate customers. 

Integrating fraud scoring

Fraud-scoring tools can be integrated into existing fraud management solutions to work within tech stacks. However, it is generally more convenient and more efficient to use one platform that combines identity verification and fraud scoring rather than integrating separate solutions. This can make both implementation and maintenance easier, which results in an improved risk-management solution.

This lets you easily and efficiently implement a fraud scoring solution that works for your needs, whether that’s for an e-commerce platform or crypto exchange. 

Setting thresholds and actions

Fraud scores typically fall on a numerical scale (e.g., 0-100 or  0-1000). Businesses are then free to set their thresholds for what is considered low, medium, and high risks of fraud and what actions to take. An e-commerce business may use the following approach:

High score (701–1000): Automatically decline the transaction and blacklist the user.

Medium score (301–700): Require manual review or extra verification (e.g., 2FA or liveness check)

Low score (0–300): Auto-approve

Considering the degree of risk, such as industry level of fraud, businesses can set their thresholds however they wish and decide what actions to take. It is important to stay aware of new fraud trends as the appetite for risk may change. Ultimately, there is no right answer for where to set thresholds, but it is about striking a fine balance between preventing fraud and minimizing friction for legitimate customers.

Suggested listen: Fraud-as-a-Service: How $20 Can Cause Millions in Damage | “What The Fraud?” Podcast

Challenges and limitations of fraud scoring

The key challenges of fraud scoring come down to the fine balance between preventing fraud and providing users with a frictionless experience. However, both of these are affected by new AI technologies that have revolutionized the fraud landscape.

Evolving fraud tactics

Patterns of fraud are changing. Due to recent advances in AI, fraud is becoming more complex and less predictable, which presents a major challenge for traditional approaches to fraud scoring. 

Using a traditional fraud scoring approach just a few years old would now be wholly unsuitable for the reality of AI-driven fraud. Sophisticated AIs that emulate human behaviors, deepfakes, and distributed networks make fraud even harder to detect.

Tools relying on outdated databases or fixed rules are now unable to detect threats and sophisticated fraud rings. This is why modern fraud scoring solutions place so much focus on adaptive technologies, including real-time threat intelligence and AI/ML algorithms.  

Suggested read: Fraud Trends for 2025: From AI-Driven Scams to Identity Theft and Fraud Democratization

False positives and negatives

Traditional approaches to fraud scoring can lead to high incidences of false positives and false negatives, prompting legitimate users for further verification or even to be blocked from a service outright. While it is important to catch fraudsters, customers will not appreciate it if their user experience is sacrificed to do so. 

False positives often occur due to customers using a new device or connecting to a VPN, giving them a high risk rating and putting them through a sometimes frustrating verification process. This can lead to lost sales, brand reputation damage, and a loss of future customers.

As AI technologies become increasingly able to mimic human behaviors designed to achieve low fraud scores, false negatives can also occur. 

As such, it is important to continuously finetune scoring models and thresholds, making sure that your fraud scoring tool isn’t causing customers unnecessary stress or letting fraudsters commit crimes.

Future trends in fraud scoring

As traditional fraud scoring tools may yield high false positive or false negative rates and are increasingly insufficient for AI-fueled fraud, fraud scoring technologies are likely to change fast to keep businesses and customers safe. Here are a few emerging trends:

1. Enhanced behavioral analysis

Fraud detection software already analyzes suspicious unhuman-like behaviors. However, fraud scoring tools could increasingly use behaviors like typing rhythm or screen pressure to add an extra layer of identity verification. 

2. More uniformity between platforms 

Fraud detection platforms could use shared fraud intelligence networks in consortiums to spot emerging trends to enhance fraud scoring models.

3. More advanced AI-powered fraud scoring

Fraud scoring tools have already moved away from traditional models used to calculate risk levels. In the years to come, as fraudsters change their strategies, even more complex systems could be used to detect unusual behaviors and provide more accurate, real-time scores in an AI arms race against fraudsters.

• What is a fraud score?

  A fraud score is a number representing the likelihood a transaction, user, or account is fraudulent, based on various risk factors. It can be used to make decisions about transactions, such as automatically approving them or referring them for investigation.

• What factors influence a fraud score?

  Factors that influence a fraud score include device fingerprinting, IP address information (e.g., VPN usage or links to a known Tor node), transaction history, behavior patterns, shipping and billing address details, and payment method (e.g., a card reported stolen).

• How do fraud scoring systems work?

  Fraud scoring systems use different methods to calculate fraud scores. Generally, they analyze data in real time using set rules and AI/ML algorithms to assign a score and guide decisions (e.g, to approve, verify, or block a transaction).

• What are the limitations of fraud scoring?

  Fraud scoring may result in false positives or negatives, which could frustrate users and lead to loss of customers. Fraud scoring platforms may also struggle to keep up with evolving fraud tactics.

• How can businesses implement fraud scoring?

  Businesses can integrate tools like Sumsub’s Fraud Detection & Prevention Solution with their existing tech stacks to easily build custom fraud scoring models. They can also adjust thresholds and actions as they wish to suit their risk tolerance and existing protocols.