8 Best Fraud Detection Software and Tools in 2024

SEON is the leading fraud prevention and anti-money laundering platform transforming how top-tier risk teams fight fraud. Founded initially to solve fraud problems in the crypto space, the company has evolved to protect over 5,000 global companies, including Afterpay, Revolut, Wise, Bilt and Branch.

SEON uses real-time digital footprinting, device intelligence, and a customizable AI-driven rules engine to help businesses detect and prevent threats. Specializing in iGaming, fintech, financial services, and ecommerce, SEON has prevented $200 billion in fraud. It is highly trusted, with over 300 positive reviews on G2, Capterra, and the AWS Marketplace.

SEON’s Features

• End-to-end solution: SEON offers a comprehensive solution for fraud and money laundering prevention, covering the entire customer journey. Its custom-built technology provides a seamless, integrated experience, boosting collaboration and efficiency among fraud and compliance teams while saving time, resources, and avoiding third-party fees.

• Extensive real-time data: SEON provides real-time, direct-to-source insights from 90+ digital and social sites, device intelligence, IP and BIN lookups, card checks, and extensive money laundering screening. This includes data on politically exposed persons, sanctions, crime lists, watchlists, and adverse media. Consolidating all essential data on a single platform helps detect complex patterns, preventing fraud and money laundering proactively.

• Transparent, AI-driven insights: SEON combines powerful blackbox algorithms with transparent whitebox models to detect emerging patterns and provide clear, actionable insights into suspicious activities. This dual approach allows you to understand detection logic and adjust strategies using AI-suggested, human-readable rules, keeping you one step ahead.

• Adaptable and customizable risk rules: SEON offers customizable risk rules to match your specific regulatory requirements, allowing you to create as many rules as needed. This flexibility ensures your fraud and money laundering prevention strategies can evolve with your business, maintaining robust protection at all times.

• Speedy integration: SEON simplifies integration with a single API connection. Our dedicated customer support ensures a quick, smooth setup and provides tailored assistance to help you fully utilize the platform and address any concerns.

SEON’s fraud detection platform not only meets the diverse needs of fraud and compliance teams but also turns fraud detection into a strategic advantage, enhancing competitiveness in the market.

Read More About our Reviews on G2

ThreatMetrix, founded in 2005 and now part of LexisNexis Risk Solutions, focuses on user identity verification and authentication using over 78 billion data instances. Despite its data capabilities and integration potential, it has limitations that may hinder its effectiveness as a comprehensive fraud prevention solution.

Here’s a summary of what ThreatMetrix offers:

• Near real-time data: Relies on consortium data which can be less accurate and comes with higher costs. It lacks unique digital and social checks for precise real-time intelligence.
• Blackbox machine learning models: With no transparency, businesses may struggle to understand and adjust decision-making processes behind fraud detection.
• Limited customization options: Restricts businesses from tailoring fraud prevention tools to specific needs and adapting to new fraud trends.
• Slow integration: Delays the deployment of fraud prevention measures and affects the time to value.

Sift, a fintech leader, offers tailored solutions for fraud-affected sectors like retail, food and beverage, travel, and ticketing. Its flagship product, the Digital Trust & Safety Suite, aims to reduce losses and enhance efficiency. However, it has significant gaps compared to a modern fraud prevention system, potentially hindering end-to-end protection.

Here’s a summary of what Sift offers:

• Transaction monitoring, chargebacks, content moderation, and two-factor authentication: While it covers multiple use cases, it lacks comprehensive coverage and AML tools, requiring additional solutions and increasing costs.
• Ability to analyze real-time data for fraud identification is unclear: This raises concerns about its effectiveness in responding to emerging threats.
• Opaque machine learning models:  It lacks transparency in decision-making processes, complicating understanding fraud patterns and adapting strategies.
• Customization available: The system is reported to be limited in adaptability, making it challenging to tailor it to specific needs.
• Straightforward to implement: Ease of integration does not compensate for its limited fraud protection capabilities.

ArkOwl enhances customer verification during registration and onboarding by aggregating live data points, focusing on email and phone verification. This helps companies understand customer identities in manual reviews. However, its limitations may affect its effectiveness as a standalone solution for broader fraud prevention strategies.

Here’s a summary of what ArkOwl offers:

• Registration and onboarding monitoring: It lacks post-onboarding fraud monitoring and AML capabilities.
• 81 real-time data points for email and phone verification: Its scope is limited and may not cover broader fraud detection needs.
• No AI or machine learning: No deeper insights or predictive modeling for fraud detection.
• No built-in data analysis or adaptation: Requires clients to integrate and interpret data themselves.
• Potentially quick integration: However, fine-tuning models with new data may take time.

Trustfull specializes in customer onboarding solutions using advanced digital footprinting and device intelligence analysis, enhanced by real-time data and IP-based geolocation. However, it lacks a comprehensive solution for the entire customer journey, including money laundering prevention, limiting its use for organizations seeking a complete fraud and compliance platform.

Here’s a summary of what Trustfull offers:

• Onboarding monitoring includes 37 social and digital checks: It lacks comprehensive fraud prevention and AML tools for the entire customer journey.
• A mix of real-time and daily updates: Does not provide fully real-time insights, potentially delaying fraud detection and response.
• Blackbox AI model: Lacks transparency in decision-making processes which can be problematic for understanding and improving fraud strategies.
• Templated rule creation: Limits deeper customization and decision-making analysis, hindering adaptation to specific business needs and evolving fraud tactics.
• Easy to set up: Integration support may be slow due to its small team.