Today I'm excited to walk you through how we used SRE Principles and Advanced Mission Learning to solve a real business pain point fraud in MCC reimbursement, the fraudulent activities within the Michigan Catastrophic Association reimbursement System. Industry billions annually with insurance for 40 billion in across all non insurance sector. The c. C faces increasingly sophisticated challenges. The traditional detection on manual review and rule based system have proven in. Trans fraud prevention in MCC reimbursement landscape, so this scale of insurance fraud, the fraud in insurance is not new, but it's growing US Insurance fraud costs over 40 billion a year, excluding health insurance. MCCA reimbursements are particularly vulnerable due to high value nature of medical claims. Traditional systems catch fraud late or not at all. Imagine catching it after millions are paid out. That is not sustainable. We need a real time in intelligence system. The MCC. Auto insurance claims that exceed standard coverage thresholds, making it particularly vulnerable to high value fraud schemes. These sophisticated operations often involve networks of providers, attorneys, and claimants working in to exploit the system. The financial impact extended beyond direct losses affecting premium rates for all policyholders and. The limitation of fraud detection. A legacy method involves manual review and rule-based systems. Problems are, rules are outdated and easy for fraudster to work around. Manual process do not scale. We're talking about thousands of claims per week. Data silos across systems and direction limit context. The results, which is most fraud is detected after the post-payment and some is not caught at all. The traditional methods struggles to emerging fraud patterns. High value claims present, particular challenge environment where sophisticated fraud schemes can easily bypass conventional detection approach. So advanced mission learning advantages, the mission learning models learn patterns and it uncover and improves with the data. Flag suspicious claims before payout, reducing risk and operational burden. The mission learning at speed scale, consistency, the things which humans alone can't manage effectively. So advanced mission learning transforms broad detection by a. Processing vast amounts of data. Instant, these systems can identify SubT connection between seemingly unrelated claims and detect organized frauds that might otherwise remain hidden. Key mission learning technologies, which c. Scan images, data from medical documents and detect tampering or duplication, which is basically visualize the data such as medical images, extend photos and scan documents, and then it can detect manipulated or inconsistency. Esry often used in the. Excel, identifying suspicious patterns in claim histories, treatment sequences, and billing that human reviewers might miss. And the last one is transformer models. State of natural languages processing technology that examines narrative descriptions in the claims for inconsistencies. Language patterns associated, these technologies have demonstrated 23% improvement in fraud detection accuracy compared to early methods, substantially reducing losses while streamlining the claim process. Using modern streaming stream protic processing tools like Kafka Flink, we are enabled. Subsequent fraud detection claims are now screened in real time with the all generated before once released. This drastically reduces false. Focus on focus only on high risk prioritized cases. With the advanced machine learning system, it's gonna be enable near instance fraud detection, and it's gonna reduce the time between the claim submission and fraud identification. The speed is crucial for high value MCC claims where early detection can prevent substantial loss and reduce the complexity of recovery efforts. So the realtime analysis allows insurers to intervene before payments are issued. Shifting from a reactive to proactive fraud prevention approach. Feder learning cross jurisdiction data sharing. So a breakthrough in data collaboration, federated learning. It allows insurers are agencies to retain shared models without sharing sensitive data. Each party trained share raw. Compliance and privacy. This help us identify cross fraud drinks without violating data laws. So the Predator Learning allows in rare regulators and MCCA collaboration on fraud detection while maintaining data privacy and. Approach, 85 to 90% of centralized detection accuracy while keeping sensitive data within its original direction, enabling unprecedented cross organization collaboration. So the miserable business impact, and you. These results are speaking the volume, 50% drop in fraudulent payouts in pilot areas, reduce manual review by 70% and positive rate drop meaning fewer delays for genuine claims. All this leads to better customer satisfaction, which. So 50 to 60 reduction in losses within the first year of deployment, and additionally, there's a 40 to 50% decrease in manual as AI systems screening, allowing human investigators to focus on complex cases requiring. Friction in the claims claim process and increasing overall satisfaction, emerging technologies and future predictions directions. So these emerging technologies, what is next? Next? The process are evolving and so must we. So what are the next gen capabilities? Biometric, which is basically a multifactor that combines facial recognitions. Verification, this advanced system that can authenticate medical records, police reports, and other documentation by analyzing the microscopic printing patterns, signature and constant consistency, and the predictive modeling. This is next generation algorithm that identifies potential fraud schemes before by analyzing emerging patterns and anomalies across the industry. These technologies prevents cutting edge of fraud prevention, offering the MCC and in unprecedented capabilities to stay ahead of increasingly sophisticated fraud schemes. Each builds up upon the foundation of current mission learning language systems while addressing. So what are regulatory complaints, considerations when using the Sable results? That can be justified to regulators and policy holders. This includes documentation of training, data sourcing, and addition factors, data privacy, compliance, maintaining compliance with the state and federal privacy regulations while leveraging comprehensive data or fraud detection. This requires robust data. To prevent outcomes in automated fraud detection systems with regular audit, potential bias, human oversight intervention. So we need to establish protocols where human experts review algorithm decision, particularly for. Implementation, careful attention to regulatory requirements and ethical standards by designing system with the compliance in mind from the outset insurers, and avoid refall while maximizing fraud detection. So the implementation. First, we need to assess the current process, which is we need to evaluate the current vulnerability and data readiness. Started small. We need to start with the pilot program and start with the targeted implementation, high risk areas, trained models. It. Bring the staff and develop ai. Human collaboration scale gradually adding more claim types and integrating across regions. So the key was cross-functional collaboration and agile iterations. Successful implementation of A-M-L-M-C-C, A fraud prevention request, a strategic approach. Begin with the assessment. Current fraud vulnerabilities and data management capabilities. Develop a pilot program, target high risk claims categories to demonstrate early wins and refine the system. Gradually scale the solution across all claims types while fostering an organizational culture that effectively combines human expertise with the mission. Mission intelligence. This balance approach delivers a great impact while minimizing disruption to operations. Thank.