But traditional detection methods using one-off criteria—such as marking claims from a single IP address as fraudulent after reaching a certain threshold—often produce disappointing results. These simplistic approaches not only miss significant numbers of fraudulent claims but can also penalize legitimate claimants. To effectively combat fraud, claims administrators must move away from individual exclusion criteria and embrace a data-based review process that utilizes robust, multi-point backend analytics.
One recent case revealed the shortfalls of single-criteria fraud detection. A claims administrator was using one-off exclusion criteria, such as flagging all claims from an IP address used more than five times, with severely skewed results. This approach led to almost 1,500 legitimate claims being falsely marked as fraudulent while allowing more than 78,000 fraudulent claims to go undetected. The correction to this method—implementing a data-driven backend review—boosted payouts to legitimate claimants by 4.5 times and prevented nearly $4 million from slipping into the hands of fraudsters.
Lessons in Fraud Detection from Banking & eCommerce
Industries with a long history of tackling fraud, such as banking and e-commerce, have pioneered the use of backend, data-driven fraud detection systems. These systems aggregate multiple data points and use machine learning models to analyze patterns rather than relying on singular flags, which often lead to inaccurate results. By similarly leveraging backend analyses, class action claims administrators can efficiently identify and combat fraud with less risk of excluding legitimate claimants based on narrow criteria. A well-designed backend solution assesses multiple pieces of data, applying advanced algorithms to analyze complex patterns rather than isolated data points.
By shifting to this type of model-based review, administrators can minimize the rate of false positives while increasing true positives, both discussed more in-depth below. This balance is crucial in the claims administration process because it ensures that valid claimants are not wrongly excluded while maximizing the identification of fraudulent claims.
Key Metrics: True & False Positives, True & False Negatives, Precision, and Recall
To evaluate the effectiveness of any fraud detection process, it’s essential to understand a few core concepts relevant to machine learning in claim fraud detection:
- True Positives: These are correctly identified fraudulent claims. A higher true positive rate indicates that the fraud detection model is successfully catching fraudulent claims.
- False Positives: These are valid claims that are incorrectly flagged as fraudulent. High false-positive rates mean that legitimate claimants face unjust denial of benefits, eroding trust and requiring further costly investigations.
- True Negatives: These are correctly identified valid claims. A higher true negative rate indicates that the fraud detection model is successfully allowing valid claims to pass.
- False Negatives: These are fraudulent claims that are incorrectly approved. High false-negative rates mean that fraudulent claims are either driving individual payouts lower for common fund cases or driving total payouts higher for claims made cases.
Two additional metrics, precision and recall, help gauge the accuracy and comprehensiveness of the detection process:
- Precision: This metric calculates the accuracy of fraud detection by measuring the ratio of true negatives to all claims marked as valid. High precision indicates that most valid claims are indeed being approved, minimizing the number of innocent claimants incorrectly accused.
- Recall: This is the ratio of true positives to all actual fraudulent claims, measuring the model’s ability to capture all potential fraud. High recall ensures the model is effectively finding most instances of fraud within the dataset.
Being aware of these terms and the options in fraud detection approaches will help firms and administrators adopt better processes to reduce false positives and improve both precision and recall. By leveraging multi-point backend fraud detection systems, administrators can detect fraud more effectively while safeguarding the interests of genuine claimants. At ClaimScore, we continue to develop and implement these advanced models to ensure that claims administration remains fair, accurate, and fraud-resistant, prioritizing integrity in every claim processed.
