The Complete Guide to Fraud Detection Software in 2024

Payment fraud poses one of the biggest threats to businesses today, with global losses topping $28 billion in 2021. As commerce increasingly moves online, companies must leverage advanced fraud detection systems to identify suspicious transactions and stop fraud in real time.

In this comprehensive 2500+ word guide, we will delve into the key techniques, top vendors, implementation best practices, and future trends in fraud detection. With insights drawn from my decade of experience in the fraud prevention industry, I aim to provide merchant, banks, and processors with the knowledge needed to make informed decisions about fraud protection.

Understanding Payment Fraud Landscape

Payment fraud refers to criminals stealing money by making unauthorized credit card or online transactions using stolen card data or fabricated identities. The major types of payment fraud break down as:

Identity Theft

Fraudsters open credit cards or bank accounts using stolen personal information like social security numbers, birth dates, and addresses. Javelin Strategy estimates 13.1 million Americans were victims of identity theft in 2019, causing total losses of $16.9 billion.


Criminals pose as legitimate businesses through spam emails, calls, or texts to fool victims into revealing sensitive information like passwords and credit card details. The FBI reports that business email compromise schemes alone caused $26 billion in losses between 2016-2019.


Thieves install skimming devices on payment terminals, gas pumps, and ATMs to steal debit and credit card information, then produce cloned cards for fraudulent transactions. Global skimming losses reached $2 billion in 2020, per

Friendly Fraud

Customers make actual purchases from merchants, then dispute transactions as unauthorized to reverse charges and get products for free. Also called “chargeback fraud.” Reported friendly fraud rates range from 1-2% of online orders.

Transaction Laundering

Merchants disguise fraudulent transactions as legitimate sales to trick banks and card networks into approving them. For example, processing gambling payments as retail sales. The US Treasury estimates tens of billions annually are laundered through online transactions.

As shown below, global payment card fraud losses have climbed steadily over the past decade, reaching $28.65 billion in 2021:

Key drivers of rising fraud include:

  • Proliferation of data breaches providing criminals more stolen identities and payment card data.
  • Increase in online transactions where merchants can‘t physically verify identities or cards.
  • More sophisticated hacking tools and anonymization techniques like crypto currency laundering.

Without effective fraud detection systems, these alarming trends are likely to continue.

How Fraud Detection Software Catches Crooks

Modern fraud detection solutions leverage advanced techniques to analyze massive volumes of payment transaction data and identify suspicious patterns in real time. This enables companies to spot potential fraud and block criminal transactions.

Rule-Based Systems

Transactions are scored by predefined logic that reflects known fraud characteristics. Example rules may check if billing city matches IP location, or flag orders over a certain dollar amount.

Pros: Simple to understand, easy to update rules as new fraud patterns emerge.

Cons: Limited to detecting fraud types already known. Static rules get outdated.

Anomaly Detection

AI models profile normal behavior for each customer based on past transactions, then identify activities that deviate significantly from the norm. For example, flagging a $5000 order from a customer who usually only spends $100.

Pros: Detects new and unrecognized fraud types by focusing on abnormalities. Adaptive to evolving behaviors.

Cons: More complex implementation. Higher false positives until models train.

Machine Learning

Advanced machine learning algorithms uncover complex patterns and interactions across millions of transactions to accurately differentiate between legitimate and fraudulent activity. Models continuously enhance through new data.

Pros: Detects most sophisticated fraud techniques. Extremely fast analysis of massive datasets.

Cons: Requires extensive data for model training. Algorithms can be opaque.

Top solutions combine rules, anomaly detection, and machine learning for balanced detection. Leading vendors tout fraud detection rates over 99% with very low false declines that would annoy legitimate customers.

Key Benefits of Fraud Prevention Systems

Effective fraud detection conveys powerful advantages:

  • Avoid losses – Block criminal transactions before they process to eliminate fraud chargebacks and reimbursements.

  • Protect reputation – Reduce brand damage and loss of customer trust by stopping fraud quickly.

  • Improve CX – Minimize false declines of legitimate customers so more orders get approved.

  • Increase efficiency – Automate and streamline fraud screening to cut manual reviews.

  • Informed strategy – Fraud analytics provide visibility into current threats and trends.

For example, Signifyd claims its solution eliminates more than 99% of fraud for customers like BuildDirect while increasing order approvals by 10%.

Best Practices Beyond Fraud Detection Software

While fraud detection systems provide the core defense, companies should supplement with additional fraud prevention practices:

  • Tokenize payment data – Replace sensitive card info with unique tokens to eliminate exposure in breaches.

  • Screen third-party partners – Ensure vendors handle data securely.

  • Limit data collection – Only gather essential customer details. Delete old records.

  • Use multi-factor authentication – Require additional credentials beyond usernames and passwords.

  • Train staff – Educate employees on sound security principles for handling customer data and payments.

  • Monitor dark web – Search hacker forums and dark web markets for stolen company data.

Following fraud prevention best practices reduces vulnerabilities beyond just blocking fraudulent transactions.

Evaluating Top Fraud Detection Vendors

Many providers now offer fraud detection capabilities. We break down the landscape into:

Fraud-Focused Startups

Emerging fintechs devoted exclusively to fraud prevention. They leverage latest technology like AI, machine learning, and real-time analytics.


  • Founded: 2013
  • Employees: 101-250
  • Key Clients: Nordstrom, Priceline, Caesars

Forter delivers automated, real-time fraud prevention backed by identity-based detection. Claims near-zero false declines for top brands.


  • Founded: 2011
  • Employees: 101-250
  • Key Clients: Airbnb, Twitter, Zillow

Sift Digital Trust & Safety Suite detects sophisticated fraud across web, mobile, APIs. Leverages massive global data network.


  • Founded: 2008
  • Employees: 101-250
  • Key Clients: TSYS, Worldpay, Betfair

Featurespace ARICTM Risk Hub uses adaptive behavioral analytics to uncover unknown threats. Clients include major payments firms.


  • Founded: 2013
  • Employees: 101-250
  • Key Clients: Yatra, Renren, LendingClub

DataVisor uses unsupervised machine learning driven by massive unique datasets. Scans 4+ billion transactions daily.

Fraud Solutions from Tech Giants

Larger, established tech firms like IBM and Oracle offer enterprise fraud management based on vast resources and experience.

IBM Safer Payments

  • Founded: 1911
  • Employees: 350,000+
  • Capabilities: AI, machine learning, cognitive fraud detection

IBM Safer Payments leverages capabilities from Trusteer, Algorithmics, and Watson to combat fraud.

FICO Falcon

  • Founded: 1956
  • Employees: 3,414
  • Capabilities: Predictive modeling, explanatory AI, consortium sharing

FICO Falcon platform analyzes transactions across card, online banking, ATM networks. Used by ~2500 institutions.

Oracle Financial Crime and Compliance Management

  • Founded: 1977
  • Employees: 140,000
  • Capabilities: Graph analytics, centralized data, end-to-end coverage

Oracle FCCM provides holistic approach across AML, fraud, risk. Used by ~80% of world‘s banks.

SAS Fraud Management

  • Founded: 1976
  • Employees: 14,000+
  • Capabilities: Advanced analytics, data visualization, consortium data

SAS Fraud Management leverages AI and machine learning techniques. 6 of 10 largest US banks use SAS.

Evaluating Solutions

When evaluating fraud detection systems, key factors include:

  • Detection accuracy – Maximize fraudulent transactions caught while minimizing false positives. Look for rates above 99%.

  • Speed – Real-time capabilities critical to stop fraud in the moment before losses occur.

  • Data sources – More and higher quality data feeds improve profiling and detection accuracy.

  • Scalability – Solutions must handle massive, growing transaction volumes without delays.

  • Customer service – Vendors should provide responsive, high-touch support and fraud expertise.

  • Global coverage – System needs to detect cross-border and regional fraud patterns.

  • Pricing – SaaS subscriptions, transaction fees, hybrid models. Calculate potential ROI.

There is no one-size-fits all solution. Companies must balance advanced functionality against cost and ease of use based on their unique requirements and risks.

Ongoing Evolution of Fraud Detection Technology

As fraud techniques grow more sophisticated, vendors are innovating to stay steps ahead of criminals. Key trends shaping the future of fraud detection include:

  • Increasing use of artificial intelligence and machine learning to extract insights from massive, disparate datasets.

  • Shifting focus to real-time and predictive analytics rather than purely rules-based systems.

  • Expanding data sources beyond transactions to include IP, phone, email, biometrics for more comprehensive profiling.

  • Tighter, embedded integration between fraud systems and banking/payment infrastructure.

  • More self-service portals so merchants can customize fraud settings themselves without relying on vendors.

  • Consortium models that allow industry data sharing for stronger cross-network fraud intelligence.

  • Movement to cloud-based delivery for scalability, speed, and frictionless updating as new threats emerge.

Gartner estimates by 2023, at least 50% of large organizations will use advanced AI-powered fraud detection, up from less than 10% in 2020. Vendors must stay agile and invest constantly to out-maneuver increasingly sophisticated fraudsters.

Conclusion and Recommendations

This comprehensive guide provided an overview of the critical role fraud detection systems play in securing businesses against constantly evolving threats targeting cards and online transactions.

Key takeaways include:

  • Payment fraud causes tens of billions in annual losses globally – a figure set to rise as commerce digitalizes.

  • Advanced techniques like machine learning and behavioral analytics enable modern fraud solutions to spot complex and previously unknown fraud patterns.

  • Balancing accuracy, speed, and customer experience is essential when evaluating providers.

  • Fraud detection software delivers immense ROI but should be supplemented with broader security best practices.

  • As fraud dynamics shift, vendors must continually enhance technology through AI and expanded data sources.

My recommendation based on extensive industry experience is to consider options from both innovative startups and established technology leaders to determine the optimal balance of advanced capabilities and stability for your organization and use case.

With the right fraud prevention partner, companies can reduce losses while providing customers with a smooth, secure purchase experience critical for ongoing growth and success. Please don‘t hesitate to reach out with any additional questions.