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

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

The answers to every question you had about fraud scoring and protecting your business from fraudsters.

Luke Owain Boult
Content Writer

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.

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:

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:

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:

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:

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:

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.

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.

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.

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.