ML Models Struggle to Keep Pace with Sophisticated Fraudsters
Machine learning (ML) models are facing significant challenges in detecting and preventing mobile app fraud. According to experts, the complexity of fraudulent schemes, combined with the unpredictability of human behavior, makes it difficult for ML models to accurately identify and block fraudulent activities.
Challenges Faced by ML Models
- Networked Fraudsters: Fraudsters often network together, allowing them to quickly exploit new loopholes and evade detection by ML models.
- Unpredictable Human Behavior: The unpredictability of human behavior makes it challenging for ML models to detect anomalies and patterns in fraudulent activities.
Consequences of Lack of Clarity
- Delays in Taking Action: Brands may struggle to decide how to respond to detected fraud cases, leading to delays in taking action.
- Ambiguity Creates Opportunities for Fraudsters: The lack of clarity around consequences for fraud detection can lead to a delay in implementing corrective measures, allowing fraudsters to continue operating undetected.
Impact on Legitimate Customers
- Friction and Negative Experience: Fraud detection can create friction for legitimate customers, who may be mistakenly flagged as fraudulent or required to undergo additional authentication steps.
- Increased Churn Rates: This negative experience can lead to increased churn rates among legitimate customers.
Strategies to Address Challenges
To address these challenges, experts recommend the following three key strategies:
1. Specialized Fraud Analytics Capability Teams
- Group fraud analysts into specialized teams focused on specific areas of fraud analytics.
- Allow for greater expertise and specialization in each area, leading to more accurate detection and prevention.
2. Site Reliability Engineering Practices
- Apply site reliability engineering practices, including error budgets, to fraud prevention.
- Enable fraud analysts to experiment and learn from their mistakes while providing a framework for measuring performance and impact.
3. Operating Model for Fraud Incident Management
- Define an operating model for fraud incident management outlining the lifecycle of a fraud case and the departments involved.
- Ensure consistency across teams and provide a common language for communication around fraud incidents.
By implementing these strategies, brands can improve their ability to detect and prevent mobile app fraud while reducing friction for legitimate customers.