To establish a robust and scalable fraud detection system, SAS Viya offers industry-leading capabilities through a variety of modules and methodologies. Below are recommended approaches and tools tailored to address complex fraud scenarios:
1. SAS Fraud Decisioning
A cloud-native solution tailored for real-time fraud detection and decision-making. Key features include:
-
Real-Time Transaction Scoring:
- Utilizes in-memory processing to analyze 100% of transactions in real time with minimal latency.
- Ensures rapid risk assessment to detect and mitigate fraud immediately.
-
Adaptive Machine Learning:
- Incorporates advanced analytics and self-learning models to identify emerging fraud patterns such as synthetic identity fraud and account takeovers.
- Dynamically updates rules to adapt to evolving fraud trends.
-
Multi-Channel Monitoring:
- Integrates data from internal systems, third-party providers, and behavioral analytics to detect cross-channel fraud.
- Effective for payment fraud, money mule accounts, and other fraudulent activities across multiple touchpoints.
2. Semi-Supervised Learning with PROC SEMISUPLEARN
Designed for environments with limited labeled fraud data, this approach enhances fraud detection accuracy through semi-supervised learning:
-
Graph-Based Learning:
- Combines labeled and unlabeled data to propagate fraud labels across clusters, uncovering hidden fraud patterns (e.g., insurance fraud).
- Leverages the cluster assumption for global consistency in fraud detection.
-
Case Study Application:
- Applied to the PaySim dataset, where 430 labeled observations and 5,000 unlabeled observations were analyzed.
- Gaussian kernel-based similarity metrics were used to predict fraud in unlabeled data.
-
Flexibility:
- Supports dynamic parameter tuning (e.g.,
gammafor kernel width) to optimize the model for specific datasets and fraud scenarios.
- Supports dynamic parameter tuning (e.g.,
3. SAS Fraud Management
A comprehensive suite offering end-to-end fraud prevention and detection:
-
Behavioral Profiling:
- Leverages SAS’s patented Signature Technology to track historical transaction patterns such as spending velocity, geographic variance, and bill pay usage.
- Detects deviations from typical behavioral patterns to identify anomalies.
-
Dynamic Home Inference:
- Addresses unreliable or unavailable location data by inferring customers’ typical locations from clustered transactions.
- Reduces false positives caused by inaccurate fixed home locations.
-
Ensemble Modeling:
- Combines methodologies like gradient boosting, neural networks, and logistic regression to achieve high accuracy in detecting complex fraud cases (e.g., account takeovers).
4. Identity & Digital Fraud Analytics
Specialized for addressing risks in digital transactions and user identities:
-
Real-Time Data Orchestration:
- Integrates AI-driven insights from sources like device fingerprints and IP addresses to assess identity risks.
-
Risk Reason Codes:
- Outputs interpretable fraud risk scores (0–999) and provides prioritized reason codes (e.g., sudden beneficiary changes, unusual geographic activity) for actionable insights.
5. Advanced Analytics & Model Governance
Ensuring robustness and scalability in fraud detection models:
-
Variable Reduction:
- Applies statistical tests (e.g., Kolmogorov-Smirnov, correlation analysis) to eliminate unstable or irrelevant variables.
- Retains only the most predictive features for modeling.
-
Champion-Challenger Testing:
- A/B testing of data providers and fraud strategies enables rapid deployment of optimal solutions.
-
Cloud-Native Scalability:
- SAS Viya integrates seamlessly with open-source tools and supports scalable deployment, making it adaptable to evolving fraud landscapes.
Key Implementation Considerations
-
Data Integration:
- Combine internal data sources (e.g., transaction logs, customer profiles) with external fraud databases for a comprehensive analysis.
-
Ethical AI:
- Mitigate biases in fraud detection by incorporating governance frameworks like SAS’s AI governance tools.
-
Industry-Specific Models:
- Tailor solutions for specific sectors such as:
- Banking: Detect mobile payment fraud with behavior segmentation.
- Insurance: Identify fraudulent claims using semi-supervised learning techniques.
Case Studies
PaySim Fraud Detection:
SAS’s semi-supervised learning approach was applied to the PaySim dataset, achieving improved fraud detection accuracy by combining labeled and unlabeled data. Learn more in the PaySim fraud detection study.Gradient Boosting for Real-Time Payments:
Advanced fraud detection models using gradient boosting methods increased transaction detection rates by 17% and value detection rates by 11%. Details are available in the SEMISUPLEARN procedure guide.
By leveraging SAS Viya’s advanced machine learning capabilities and fraud detection modules, organizations can build a robust, scalable, and adaptive fraud prevention framework to protect customers and reduce financial losses.
Top comments (0)