Enterprise Fraud Management: Detection & Prevention

Fraud continues to be a costly adversary for businesses worldwide. The Association of Certified Fraud Examiners’ (ACFE) 2024 Report to the Nations reveals that organizations lose roughly 5% of their annual revenues to fraud. Globally, this translates to $4.7 trillion in losses.

Post-pandemic economic shifts have only intensified the challenge, exposing new vulnerabilities and pathways for fraudulent activities. In today’s digital-centric business environment, enterprise fraud management (EFM) has become essential for girding financial integrity and strengthening organizational resilience.

What is Enterprise Fraud Management (EFM)?

Enterprise Fraud Management (EFM) refers to organizations’ comprehensive approach to detecting, preventing, monitoring and managing fraud across their entire operations. EFM involves leveraging a centralized system that integrates various data sources, such as user, account and device information, analytics and real-time monitoring, to identify potential fraudulent activities, corruption and criminal behaviors.

EFM solutions are tailored to address the unique needs of different types of businesses. For example, in the financial services sector, banks utilize EFM to monitor transactions, detect anomalies in customer behavior and prevent fraudulent account access. Any sudden change in a customer’s usual spending patterns or an unexpected login from a different geographical location might trigger an alert for further investigation. Similarly, ecommerce platforms use EFM to identify suspicious activities such as high-value purchases from newly created accounts or multiple failed payment attempts, which can indicate potential credit card fraud.

EFM is equally important for other industries, such as igaming, where fraud can significantly impact revenue and customer trust. Any organization that handles a high volume of transactions, sensitive customer information, or digital interactions can benefit from EFM to safeguard against the ever-evolving landscape of fraud threats.

What Factors Determine Enterprise Fraud Management (EFM)?

EFM allows large-scale businesses to address all aspects of the fraud ecosystem, from data collection and analysis to real-time monitoring and investigation. The complexity of modern fraud necessitates a multi-layered approach, typically comprising five key layers: 

1. Endpoint-Centric Layer: This layer focuses on securing the user’s point of access through measures like 3D Secure (3DS), geolocation, device fingerprinting and other authentication solutions. It ensures that only legitimate users can access accounts and initiate transactions, helping prevent fraud at the entry point.
2. Navigation-Centric Layer: This layer compares previously gathered data with current user behavioral patterns to detect anomalies. For instance, it monitors actions such as rapid changes in browsing behavior or unusual login times to identify potential account takeover (ATO) attempts.
3. Channel-Centric Layer: This layer tracks an account’s activity within a single channel, such as mobile banking, ecommerce websites or payment platforms. It compares the current activities to the user’s historical behavior and established rulesets to spot irregularities – like an uncharacteristically large transaction.
4. Cross-Channel Layer: User behavior here is analyzed across multiple channels, including product usage, payments and customer service interactions. By cross-referencing activity across different channels, businesses can identify inconsistencies that may indicate fraudulent behavior, such as transactions originating from different devices in a short time span.
5. Entity Link Analysis Layer: This layer examines relationships between users, accounts and transactions to uncover potential fraud networks. It helps detect patterns such as multiple accounts linked to a single device or email address, providing insights into organized fraudulent activities.

EFM is not limited to a specific industry; it is required for any business managing high transaction volumes, sensitive data or multi-channel interactions. Whether it’s banking, financial services, ecommerce or igaming, having a robust EFM system is crucial to maintaining security, protecting customer trust and mitigating financial losses.

Four Common Types of Enterprise Fraud

Understanding the most prevalent types of fraud is essential for building an effective defense strategy. Here are four common types of enterprise fraud that organizations should be vigilant about.

1. Transaction Fraud and Chargebacks

Transaction fraud occurs when unauthorized purchases are made using stolen payment information, leading to chargebacks. Chargebacks not only result in lost revenue but also incur additional fees and can cause harm to a business’s reputation. This form of fraud is common in ecommerce, where criminals use stolen credit card information to make unauthorized purchases. The business faces a chargeback when the legitimate cardholder notices the fraudulent transaction and requests a refund. High chargeback rates can even lead to penalties from payment processors, making it vital for enterprises to have real-time monitoring and fraud detection systems in place.

Companies use machine learning models to analyze transaction patterns for anomalies and flag potentially fraudulent activities to combat chargebacks. As well, dynamic friction is also being applied, adding verification steps like SMS texts for confirmation of orders for high-risk transactions, to ensure that only legitimate customers can complete their purchases or the proof of purchase intent can be established if need be to refute chargeback claims.

2. Account Takeovers (ATO)

Account Takeovers (ATO) are a form of identity theft where fraudsters gain unauthorized access to a legitimate user’s account, often executed through methods like phishing, credential stuffing or using malware to harvest login details. Once inside an account, an attacker can make unauthorized transactions, change account information or use the account for additional fraudulent activities. ATO attacks are particularly damaging as they not only lead to financial losses but erode customer trust.

Preventing ATO requires a combination of real-time monitoring, multi-factor authentication (MFA) and behavioral analytics. By analyzing user behavior, such as login patterns, device usage and geolocation data, enterprises can detect unusual activities that may indicate a takeover attempt.

3. Fake Accounts From Stolen and Synthetic IDs

Fraudsters often create fake accounts using stolen or synthetic identities to exploit businesses, especially those in sectors like banking, fintech and ecommerce. Stolen IDs involve using real personal information obtained from data breaches, while synthetic IDs combine real and fake data to create a new identity. These fake accounts are then used for various fraudulent activities, such as applying for loans, opening lines of credit or making fraudulent transactions.

Effective prevention relies on systems that can cross-check user-provided information against various data sources, including social signals, digital footprints and device intelligence. Machine learning models can then further identify patterns indicative of synthetic identity creation, flagging suspicious activities for review.

4. Multi-Accounting for Bonus, Promo and Referral Abuse

Multi-accounting is when an individual or group creates multiple accounts to exploit bonuses, promotions or referral programs and is common in industries like gaming, online lending and ecommerce, where welcome bonuses and promotional offers are used to attract new customers. Fraudsters take advantage of these programs by creating multiple accounts using different identities or fake credentials to claim offers multiple times, leading to revenue losses and distorted customer data.

Detecting multi-accounting requires cross-referencing information like device fingerprinting, IP addresses and payment details across accounts. Advanced EFM systems uncover connections between seemingly separate accounts to identify fraud rings and prevent further abuse. By analyzing digital footprints and applying velocity checks, businesses can stop multi-accounting in its tracks and protect their promotional investments. 

How to Prevent & Detect Enterprise Fraud

Thanks to data analysis and interpretation, detecting fraud has never been easier yet fraudsters will continue to innovate so having an EFM system that focuses on the key areas of abuse your industry faces is important.

Depending on what’s required, your business can look at either working with a complete end-to-end EFM system or create a more tailored multi-layered approach built up of differing products. Some of the most important features to include in any EFM system are:

• Team Roles and Responsibilities 
• Real-time Transaction Monitoring
• Machine Learning 
• Behavioral Analytics
• Decision Making
• Access to Alternative Data
• Fraud Risk Scoring
• Reporting Procedures 
• Investigation Process 
• Multi-factor Authentication

Selecting an Enterprise Fraud Management System

Recent technological advancements have made fraud detection more accessible than ever. However, as fraudsters continue to evolve their tactics, it’s crucial to implement an EFM system that specifically addresses the key areas of abuse prevalent in your industry.

When evaluating solutions, key features to look for include: 

• Real-Time Detection: The system should be able to identify fraudulent activities as they occur, allowing for immediate intervention.
• Advanced Analytics: Look for machine learning and predictive modeling capabilities to uncover complex fraud patterns.
• Customizable Rules Engine: The ability to create and modify fraud detection rules based on your organization’s needs is essential.
• Multi-Channel Coverage: Ensure the system can monitor transactions across various channels (e.g., online, mobile, in-person).

How to Choose an Effective Enterprise Fraud Management Solution

Regardless of your company’s size, an effective EFM solution must meet key requirements beyond offering the features noted above.

Seamless Integration

Traditional legacy systems often have lengthy integration periods, complex setup processes and annual contracts riddled with steep, built-in costs. These can place a heavy burden on internal resources and delay the deployment of critical fraud defenses. In contrast, modern fraud platforms like SEON can be implemented in days, providing a more agile approach to risk management.

When considering an EFM solution, it’s crucial to weigh the pros and cons of integration. Will your IT team be bogged down by intricate coding projects, or is there a solution that offers seamless deployment through simple API calls? A truly seamless integration should also support flexibility, allowing you to layer multiple solutions as your business evolves. Look for platforms that offer plug-and-play capabilities, so they can easily adapt to existing systems and workflows without the need for extensive re-engineering.

As your business grows and transaction volumes increase, your fraud prevention solution must scale accordingly, without requiring constant manual adjustments. This scalability often includes features like modular add-ons and configurable settings, ensuring that the system remains effective as your risk landscape changes. Lastly, prioritize solutions that provide comprehensive documentation and dedicated support teams, ensuring a smooth transition and ongoing maintenance with minimal disruptions to your operations.

Real-Time Analysis and Results

Two key factors determining a solution’s effectiveness are how much data it can process and how quickly it can provide actionable insights. Real-time analysis isn’t just a luxury – it’s a necessity for identifying and stopping fraudulent activities before they escalate.

Speed is particularly important during KYC (Know Your Customer) checks, where every second counts. Prolonged delays create bottlenecks in your operational processes and risk alienating customers. In an environment where user experience is king, having an EFM solution that can assess risk almost instantaneously is invaluable. The best solutions provide near-instant results, reducing friction in the customer journey while maintaining high security.

Additionally, real-time data enrichment can significantly enhance your fraud detection capabilities. By pulling in diverse data points – such as digital footprints, email addresses or social media activity – an effective EFM can construct a more comprehensive user profile. These enriched data sources should be processed within seconds to ensure that your fraud models stay up-to-date and accurate. Any latency in this process can create gaps in your fraud defenses, potentially exposing your business to unnecessary risk.

Custom Models and Rules

You likely have existing fraud protection systems tailored by your risk teams, using rules honed over the years. As you adopt a multi-layered fraud strategy, ask: How easy is it to import your current models? Can you integrate an unlimited number of custom rules, or will you have to start from scratch?

Some EFM solutions come with pre-built templates tailored to specific industries, which can be useful for testing against your current models to enhance precision.

Compliance With Laws and Regulations

Your risk teams have likely invested years in developing fraud protection systems tailored to your business, using finely tuned rules that address your specific risk profile. As you shift toward a multi-layered fraud strategy, the ability to leverage these existing models is crucial. A key question to consider is: How easily can your current models be imported into the new EFM solution? Can the system accommodate an unlimited number of custom rules, or will you need to rebuild from scratch?

The ideal EFM platform should offer flexibility, allowing you to integrate your established models without compromising on their complexity or effectiveness. This means the solution should support advanced rule creation and modifications, empowering your risk teams to refine their approach as new threats emerge. Additionally, seamless import capabilities ensure a smooth transition, preserving the valuable insights and logic your teams have already developed.

Some EFM solutions come with pre-built templates designed for specific industries, which can serve as a valuable benchmark. These templates provide a starting point for testing against your current models, offering an opportunity to enhance precision by identifying gaps or areas for improvement. The best platforms allow you to test these templates and customize them further, aligning with your unique risk parameters and business objectives.

Whitebox vs. Blackbox Systems

Your EFM software generates risk scores that guide decision-making, but how these scores are derived can vary significantly. “Blackbox” systems operate with little transparency, concealing the underlying algorithms and making it difficult to understand the rationale behind each decision. While this approach might simplify initial implementation, it limits the flexibility and trust needed for effective risk management. On the other hand, “whitebox” systems offer full transparency, providing detailed, human-readable explanations for each risk score. This visibility allows your risk teams to grasp the underlying logic, make informed adjustments, and tailor strategies to fit your unique business needs.

It’s equally worth considering if the EFM system can suggest risk rules. Many modern solutions leverage machine learning (ML) and artificial intelligence (AI) to identify emerging patterns and propose new rules. However, a truly effective tool will be transparent in its AI-driven suggestions, explaining why each rule is recommended. This approach enhances your team’s ability to refine fraud detection tactics and ensures that they remain in control of the overall risk strategy. By opting for a whitebox system, you gain the clarity and adaptability to respond confidently to evolving fraud patterns.

Pricing Model

Modern EFM solutions now often adopt a straightforward SaaS model, providing more flexibility and reducing upfront costs. This shift allows businesses to scale their fraud prevention efforts without being locked into rigid, costly agreements.

The most adaptable options even offer pay-per-API-call pricing, giving you direct control over expenses and a clearer view of ROI. Regardless of the model you choose, it’s essential to prioritize solutions with transparent, easy-to-understand pricing structures. This ensures you avoid hidden fees and maintain a cost-effective approach as your fraud management needs evolve.

Enterprise Fraud: FAQ

You might also be interested in reading about:

• SEON: Fraud Risk Management: How to Implement It & Advanced Technologies

Learn more about:

Data Enrichment | Browser Fingerprinting | Device Fingerprinting | Fraud Detection API

External Sources:

• PWC: PwC’s Global Economic Crime and Fraud Survey 2020
• Infosecurity Magazine: Companies’ Stock Value Dropped 7.5% after Data Breaches
•  CPMares: Key Takeaways from the 2024 ACFE Report to the Nations