Fraud Detection 101

June 14, 2023

Zach Pierce
Risk Ops Lead

Table of Contents

1. Types of Fraud Detection and Prevention Solutions
2. Transaction Monitoring Best Practices
3. The Dynamic Approach to Fraud Detection
4. Risk Assessment Best Practices
5. The Future of Fraud Detection and Prevention
6. Key Takeaways

You know your fraudsters—the thieves, the con artists, the opportunists. You know how these bad actors steal and fabricate identities to take over accounts and unleash chaos. The question is how you can protect your organization and your customers from falling victim to fraud.

The answer is building a robust fraud detection system to help you remain one step ahead of bad actors and protect your business from reputational and financial harm.

To discuss this topic, we interviewed Zach Pierce, the Risk Operations Lead at Lithic, where he focuses on mitigating financial loss and building out the Risk team.

Types of Fraud Detection and Prevention Solutions

Organizations automate fraud detection with the help of rules engines, machine learning (ML) models, data enrichment tools, and hybrid solutions. Here’s how these solutions work:

1. Rules Engine

As Zach explains, “A rules engine is an application that allows fraud detection agents to define rules related to a number of data points including user activity, metadata, and self-reported user information.”

A rules engine generally works in three steps:

1. The engine is triggered due to a user action, like proceeding to checkout from their cart.
2. The engine uses the pre-set conditions and rules to decide which action to perform.
3. The engine performs the action based on the logic built into the rules.

The most prominent advantages of using a rules engine are:

• The rules are easy to deploy.
• They allow you to monitor high volumes of transactions.
• They help you respond to threats in a timely manner.
• They result in valuable data, such as false positives and false negatives, which you can then analyze to create a more efficient and complex rules engine.

However, there are also some challenges associated with rules engines. If you choose to build your own engine, you will need a team of developers dedicatedly working for months—there’s no guarantee that you will build the kind of engine you want on your first attempt.

2. Machine Learning

Machine learning (ML) models are trained with the help of data generated by the rules engine or a similar example dataset to detect fraudulent behavior and outsmart fraudsters. ML systems are designed to handle large volumes of data, evolve as they handle more of it, and can act instantly when they detect anomalies.

In supervised learning, the algorithm needs labeled data using classification techniques to determine outcomes. In unsupervised learning, the algorithm uses unlabeled data to find patterns and anomalies in data.

Zach notes, “Machine learning models can handle large volumes of data and identify new threats. But a drawback is that they are more time intensive and complex to set up and maintain than simple rules.”

3. Data Enrichment Tools

Data enrichment tools help you build a complete profile based on a few data points provided by the user. These tools augment data points such as email addresses, IP addresses, bank identification numbers (BINs), and device data.

“Working with data enrichment tools can be really helpful as they can give you information about a user that you wouldn’t have access to otherwise.”

4. Consortium Data

A fraud prevention consortium is an association of businesses working together for a common cause. Large repositories of fraudulent payment data are created through data sharing between multiple organizations. Joining the consortium and gaining access to consortium data can increase the efficiency of your rules engine as well as your ML model.

Transaction Monitoring Best Practices

1. Use a Customizable Solution for Fraud Risk Management

There are many transaction-monitoring vendors and pre-built solutions out there. Use those that can be customized to meet your specific requirements.

2. Be Proactive in Your Approach to Fraud Detection

Most companies are reactive in their approach to fraud detection. Here’s what you can do to be proactive in fraud detection and prevention:

• Use the expertise of fraud detection professionals to understand the potential threats in your industry.
• Create rules and describe in detail the actions that should be taken when suspicious activities are detected.

3. Focus on Data Governance To Get Higher-quality Data

Data governance is a collection of rules, policies, and processes that ensure the availability, integrity, and security of data in an organization.

4. Think Like a Fraudster

Study their tactics to see how they may attack you. This will help you understand vulnerabilities in your system.

The Dynamic Approach to Fraud Detection

When using a machine learning model, you can monitor each transaction using either a dynamic approach or a sequential approach. In the dynamic approach to fraud detection, ML models monitor real-time data to identify fraudulent activity.

Risk Assessment Best Practices

1. Data collection: Collect data from customer profiles, transactions, and external sources.
2. Risk scoring: Assign a risk score to each data point based on the likelihood of fraudulent activity.
3. Defining risk thresholds: Determine the minimum acceptable risk score of a transaction.
4. Establishing investigative procedures: Define these procedures for suspicious transactions.

The Future of Fraud Detection and Prevention

Fraud detection has evolved and there are out-of-the-box solutions available today that simplify the process.

Key Takeaways

Fraud detection is all about building a robust system that can quickly take you from being reactive to being proactive about fraudulent transactions. Use a combination of rules engines and machine learning models to improve your fraud detection system.