Staying Ahead of Fraud. Protecting Public Trust.
Fraud, waste, and abuse of government programs can compromise public trust and drain valuable taxpayer funds if left unchecked. OST’s Threat & Fraud Detection solutions empower federal, state, and local agencies to stay ahead of emerging fraud tactics with AI-powered detection, real-time risk scoring, and intelligent automation.
OST integrates Artificial Intelligence (AI), Machine Learning (ML), Predictive Analytics, and Robotic Process Automation (RPA) into fraud prevention ecosystems that detect anomalies, prioritize high-risk cases, and reduce investigator workloads. We develop and deliver adaptive platforms that evolve with new fraud patterns and scale to meet the needs of agencies of all sizes and localities. Our expertise is grounded in enterprise-wide fraud detection, integrating anomaly detection models, event-driven automation, and enterprise case selection to systematically assess risks and optimize enforcement strategies. OST’s solutions drive better decisions, faster fraud identification, and more efficient use of limited enforcement resources—protecting both mission outcomes and public funds.
OST Threat & Fraud Detection – Enabling Greater Accuracy
- AI-driven, adaptive fraud detection models that evolve with new fraud tactics
- Predictive risk scoring to ensure investigators focus on the highest-priority fraud cases
- RPA automation to eliminate manual fraud verification steps and accelerate investigations
- Seamless integration with existing case management and compliance platforms
- Real-time, event-driven fraud monitoring instead of batch-based detection delays
- Scalability for large-scale fraud enforcement across federal and state agencies
CASE STUDY:
Driving Down Fraud: Detecting Insurance Scams with Over 85% Accuracy
Challenge
Fraudulent insurance and benefits claims remain a serious problem for both private insurers and government programs. These claims not only inflate costs and consume valuable time evaluating, but in public systems, they also erode trust. Complicating matters, aggressive fraud detection efforts run the risk of misclassifying legitimate claims, which can trigger customer dissatisfaction and added expenses. Striking a balance between accurate fraud detection and minimizing false positives is critical.
Solution
OST analyzed a large dataset of property and casualty insurance claims, engineering and testing nine machine learning models trained on features including various ranges of policy deductibles, a wide set of incident types, claim timing and reporting patterns, policy characteristics, financial and behavioral red flags, the level of documentation and evidence, geographical and environmental factors, historical and comparative data, and more.
Outcome
The Decision Tree algorithm correctly identified over 85% of fraudulent claims, a high rate compared to other algorithms. The model was rigorously evaluated using recall (sensitivity) and other performance metrics to ensure a balanced approach that minimized misclassification of legitimate claims.
The final model provided a scalable and accurate fraud detection solution, significantly reducing the need for manual reviews and cutting operational costs. Beyond property and casualty claims, this approach shows strong potential for other insurance sectors, including flood and property policies—paving the way for smarter, faster fraud prevention across the industry.