Case Study: The Importance of Auditability in Credit Card Fraud Detection

Introduction

In the high-stakes world of credit card fraud detection, the difference between an effective and ineffective system can mean millions in losses. Traditional machine learning approaches have delivered impressive accuracy but often operate as "black boxes," leaving fraud analysts with little insight into why specific transactions are flagged. This case study explores how auditable machine learning algorithms are transforming fraud detection processes, reducing costs, and improving efficiency for financial institutions.

The Credit Card Fraud Detection Challenge

Credit card fraud detection presents a unique set of challenges for financial institutions and merchants:

• Massive scale: A company like Target processes approximately 120 million transactions monthly, creating a dataset with 120 million rows and up to 3,000 columns of features

• Severely imbalanced data: Only 1-10% of transactions are actually fraudulent, though 5-10% are flagged for review

• Complex feature sets: Each transaction contains 1,000 raw elements (400 from credit card companies, 600 from vendors) that generate 2,000-3,000 synthetic features for analysis

• Time constraints: All flagged transactions must be reviewed within 24 hours for compliance

• Resource intensity: Each review takes analysts 5-15 minutes, requiring large teams (approximately 150 analysts for a company like Target) working 24/7

The financial consequences of missed fraud are significant. In the chain from merchant to credit card company, if an entity fails to detect fraud that another entity catches, the entity that missed it bears the financial responsibility for the fraudulent transaction.

The Problem with Black Box Models

In our interviews with executives in the credit card fraud space, a consistent pain point emerged:

"Current machine learning models don't tell analysts why a transaction was flagged as fraudulent - just a likelihood score. Analysts must then review thousands of features to determine if the flag is legitimate, which is extremely time-consuming and inefficient."

This black box approach creates several problems:

1. Inefficient manual review: Analysts spend 5-15 minutes reviewing each flagged transaction

2. Excessive staffing requirements: Large teams are needed to handle the volume

3. Potential for human error: With thousands of features to review, important indicators can be missed

4. Compliance risk: Failure to review all flagged transactions within 24 hours creates regulatory exposure

The Auditable ML Solution

Auditable machine learning models, particularly rule-based models, offer a powerful solution. These models not only predict fraudulent transactions but explicitly explain their reasoning through an if-then structure. This transparency allows for:

1. Automated review of clear-cut cases: When the model's reasoning is transparent, many decisions can be automated

2. Faster human review: For cases requiring human oversight, analysts can focus on the specific features that triggered the alert

3. Continuous improvement: Explicit rules can be refined based on feedback and new patterns

4. Enhanced compliance: The decision-making process is fully documented and traceable

Benchmark Results

To demonstrate the potential of auditable models, we benchmarked several explainable and black box machine learning models on the Kaggle Credit Card Fraud Detection dataset, which presents an extreme challenge with only 0.17% fraudulent transactions. The data set consists of data like the time and amount of the transaction and 28 (V1-V28) anonymized synthetic elements that are unique to the transaction. The anonymized elements are similar to the 2000-3000 synthetic features that are used in real world credit card fraud detection.

Note: SMOTE was applied to balance the dataset before training to address the extreme class imbalance. However, testing was done on the raw, unbalanced data. APLR is from the InterpretML package and TreeGAM is from the imodels package.

Sample Rules from Tree GAM Model

The Tree GAM model, is an additive model of decision trees. This means it can be broken down into auditable decision rules that exactly state why a decision was made. Some example rules are here:

• If V1 is greater than -0.8033 and V1 is less than or equal to 0.6217, then predict Fraud.

• If V5 is greater than -1.3929 and V5 is less than or equal to -0.8037, then predict Not Fraud

These explicit rules allow analysts to quickly understand why a transaction was flagged and make faster, more informed decisions. The rules are also easily checked and audited by automated software, something that is very difficult, if not impossible to do for black box machine learning models.

Conclusion

The credit card fraud detection landscape is ripe for transformation through auditable machine learning. By providing transparent, rule-based explanations for fraud predictions, these models not only maintain the high accuracy of traditional approaches but also dramatically improve operational efficiency and reduce costs.

For financial institutions and merchants processing millions of transactions, the ability to automate reviews and streamline analyst workflows represents a significant competitive advantage in the ongoing battle against fraud.