Greetings everyone. This is Vela. I'm working as a senior BI developer at Coach Vti. I have 10 plus years of expertise in leading highly technical MicroStrategy, Tableau, and healthcare data warehousing projects. So my today's topic is. AI Ready Healthcare data revolutionizing fraud detection with scalable data centric intelligence. So coming to the healthcare fraud crisis. So as you all might know, the healthcare fraud crisis is a significant and growing issue that impacts the healthcare systems worldwide. And most notably in countries like United States, where a large sums of money flow through both public and private healthcare systems. Healthcare fraud not only results in billions of dollars lost. In financial losses, but it also erodes trust, compromises patient care, and influences healthcare costs to everyone. So if you see here, there is almost like annual US fraud losses is almost like $300 billion. The healthcare fraud range as estimated of $300 billion from the US system annually, and it represents up to. The 10% of total healthcare spending and diverting critical resources. And if you see there is almost 75% of unstructured data that is impacting the healthcare's staggering. Three quarters of healthcare data remains unstructured, severely hindering automated analysis and the efficient detection of fraudulent patents. And. There are almost like 80% of the coding errors, so this is alarmingly. So there are four out of five medical bills that contain errors, creating significant vulnerabilities and opportunities for fraudulent activities, the healthcare fraud process, and immense and escalating financial threat to the industries. And the she scale and complexity of this challenge are gently demand a data driven solution to protect the vital resources and ensure equitable care. Going to the next slide. So the main challenge here in the healthcare fraud crisis is the data quality challenge. So despite like significant advancements in AI and mission learning, traditional fraud detection in healthcare remains alarmingly in effective. The fundamental flaw lies not only in the sophistication of our analytical models, but it is. In the deeply compromised data foundations, poor data quality is rampant and manifesting as inconsistent formatting across disparate systems and incomplete patient records written in duplicate entries and critical coding discrepancies. Let me come up with few scenarios where the data quality undermines detection, like how poor data quality is causing healthcare fraud crisis. So there is a scenario where a provider might bill multiple insurers for the same service and a slightly different patient, right? In such scenario, the consequence might be. It is missed as a duplicate because due to the inconsistent identifiers and in the second scenario. A machine learning model, which is trained on incomplete data. So this produces inaccurate risk scores and biases. And the third one is a telehealth session is bill, but patient data is incomplete or mis miss missing from the conversation. So in such case, a fraudulent billing may go unnoticed. So these healthcare organizations. Frequently find themselves lock in a frustrating cycle where increasing computational power and model complexity diminishing returns. This is not an algorithmic challenge. It is a foundational data quality crisis that undermines even the most advanced machine learning approaches. So the two potential of sophisticated AI for fraud prevention will remain unrealized. Coming to the AI models, so there are two types of AI models in my current analysis. One is the traditional model and the other is the data centric AI model. In the traditional model, the front detection focuses on refining algorithms and increasing model complexity. Viewing the data as an unchangeable input. This model centric approach is inherently limited by data quality. Even advanced AI struggles with inconsistent and erroneous regards leading to diminishing returns despite computational investment. Whereas the data centric AI seems to be unlocking true potential. It shifts the focus, it improving data quality is the most effective path. To robust ai, this approach prioritizes systematic data governance, continuous quality monitoring and proactive data enrichment By ensuring data integrity, we are building a strong foundation for AI success in fraud detection. So if you see the. The picture here, so it stating the fundamental shift from a model centric to a data centric mindset is not just an optimization, but it's critical reevaluation of how we approach AI challenges. For healthcare fraud detection, it means moving beyond React algorithm tweaks to pro two data excellence, and finally enabling AI to deliver on its promise. So why it matters. In healthcare, in sensitive and high stake domains. The healthcare fraud crisis plays a major role. So it is occurring recurring. It is giving like more recurring losses to the companies. So there is potential for a true data-centric AI model to get rid of all these inconsistencies. The bigger impact is on the performance than the small model improvements. A well labeled high quality data set can enable a simple logistic regression model to outperform a deep neural network trained on MC or a biased data, the transformational results. The impact of data-centric ai. Below are the main metrics that define the results in the data-centric AI elevated model accuracy. So here. Achieving 95% accuracy enables unprecedented precision in identifying fraudulent activities and thus protecting the vital resources and ensuring equitable care and significant false positive reduction. So an 95% reduction in false positive, it cons the investigator overhead and allows the healthcare teams to focus on legitimate threats and process the claims more efficiently, saving the valuable time and money. And the third one is the accelerated investigation cycles. A 70% foster fraud detection and resolution process allows healthcare organizations to quickly mitigate ongoing losses, recovers funds swiftly and streamline operational workflows through enhance data arability. And the final one is the substantial fraud loss reduction organizations realize. A 76% reduction in financial losses due to fraud, directly attributable to enhancer detection capabilities stemming from superior data quality. This translates into millions saved and reinvested in patient care these are the four key factors and continuous data quality improvement framework. It is essential to ensure that AI systems remain accurate, reliable, and fair over time. Unlike traditional software systems, AI is highly sensitive to the quality of data, and this directly influences performance bias and outcomes. This framework treats data as a living asset, not a static input requiring ongoing monitoring, evaluation, and refinement through the AI lifecycle. So coming to the first part, it's data profiling and assessment. The analysis of these data assets, identifying qualifying gaps, inconsistencies and structural issues across all healthcare data sources. The second one is the quality metrics definition, establishing. Quantifiable standards for completeness, accuracy, consistency, and timelines that align with fraud detection requirements. And the third one is automated monitoring. The real time quality monitoring systems that flag deviations have correct to actions before the take. Data quality es, and the final is the iterative improvement. So implementing these feedback loops that continuously refine the data quality processes based on model performance and the detection outcomes coming to the. Machine learning model performance, and these frameworks treat data as a living asset. It's not a static input requiring ongoing monitoring, evaluation, and refinement through the AI lifecycle. So if you see here with high quality, meticulously governed healthcare data, the methods like random forest accelerate uncovering. Complex fraud patterns and leveraging multiple decision trees. This model, the random forest offers robust pattern recognition and crucial interpretability essential for regulatory compliance and audit trails in healthcare. If you see the rest of the model models, they're like little underperforming when compared to the random forest. These strong performance metrics, they're very vital for healthcare organizations. High accuracy ensures reliable distinction between legitimate and frontline activities. Superior precision minimizes false positives, preventing unwarranted flagging of valid claims. Strong recall reduces false positives, this blend of advanced analytics and quality data and purpose organizations to detect fraud and improve the speed and the data quality. And coming to the methods, the blockchain for data integrity and in immutable audit trails, blockchain technologies provide 99% of data integrity assurance through cryptographically secure transaction logs. Every data modification creates an immutable record, and this enables. Comprehensive audit trails essentially for healthcare complaints. So the TAM proof transaction records, the real time integrity verification, distributed consensus mechanisms, and the regulatory compliance automation. And this approach basically eliminates the data manipulation vulnerabilities while maintaining the transparency required for effective fraud investigation and regulatory reporting. And the auto machine learning pipeline acceleration. This process involves the data addition where the automated pre-processing and feature engineering reduce the manual intervention by 90%. The model selection, intelligent algorithm selection optimizes performance across diverse fraud patterns, training acceleration. So the model training time through optimized resource allocation, so there is like a 78% reduction in the model training through. Optimized resource allocation deployment. So there is a seamless integration into existing healthcare IT infrastructure. So these are all the advanced fraud detection capabilities, and this enable healthcare organizations to deploy these sophisticated AI models and give them extension, extensive machine learning expertise in house. Coming to the cloud, NATO architecture benefits for healthcare fraud detection, the elastic scalability. So our cloud NATO solution dynamically allocates resources to precisely match the varying fraud detection workloads. So this means the healthcare organizations can effortlessly scale from routine monitoring to intense investigation periods. This ensures optimal performance. And optimized cost efficiency. Leveraging paper use pricing models, our architecture aligns your expenditure directly with actual usage. This significantly reduces the total cost of ownership by up to 60% when compared to the traditional on-premise solutions. Robust security and compliance built upon enterprise grade security frameworks with continuous automatic updates. Our platform ensures unwavering compliance with critical healthcare data protection like HIPAA and GDPR. This comprehensive approach safeguards sensitive patient information and thus reduces the regulatory risks and frees up your IT team from manual security patching. Seamless integration. So effective healthcare is dependent on deep seamless integration within organization's, existing critical systems, including electronic health records, claim management system, and health information system. Our sophisticated approach leverages standardized APIs. Robust healthcare interoperability protocols to ensure the data flows effortlessly without dis disrupting vital clinical and administrative workflows. This integration strategy directly addresses common challenges that often hinder proactive fraud detection. We also overcome issues such as disparate data formats, limitations of legacy systems, and the crucial demand for real-time data processing by rigorously adhering to fast healthcare interoperability resources, and HL seven messaging protocols. Our platform empowers healthcare organizations with comprehensive and timely data access, and this not only maintains operational continuity. But significantly enhances the accuracy, the speed, and enables P to identification of suspicious patterns and immediate response. The governance and compliance framework. So if you see the regulatory complaints. The data privacy and security, the quality assurance, the operational controls, the technical infrastructure. These are the five main topics under the governance and compliance framework. This ensures these five ensure that the fraud detection system meets stringent. Healthcare regulatory requirements while maintaining operational efficiency. This hierarchical approach addresses compliance obligations from technical infrastructure through to regulatory reporting, creating a sustainable audit ready fraud detection capabilities, and coming to the implementation roadmap In the phase one is this is an assessment phase, which is estimated to be one to two months. There is a comprehensive data audit, stakeholder alignment and infrastructure readiness evaluation, and coming to the phase two, there is a deployment, sorry, the phase two is the foundation, so the phase two, the data quality improvement, governance governance framework, establishment, and initial for. Model development, this is estimated to be around three to five months. Coming to the phase three is a deployment phase where there will be a pilot implementation system integration and performance validation across key fraud scenarios. And this is an estimation of six to eight months. The phase four is scale, which is around like nine to 12 months. It is comprises of like full deployment, continuous monitoring, implementation, and advanced analytics integration. To measure the success below are some of the key performance indicators. These are essential in measuring the impact of our fraud detection and safeguarding the healthcare resources and driving efficiency. So the financial metrics are reduced fraud losses where it prevents fraud claims, preserving healthcare projects, and optimized investigating costs, increased fund recovery, and lower total cost of ownership. This demonstrate economic efficiency and return investment, and the operational metrics are. Enhance detection accuracy rates. It measures precision in identifying fraud and minimizing false positives, significant fall, false positive reduction, accelerated investigation cycle time, and robust system uptime and reliability. This guarantees consistent availability and optimal performance. And beyond these metrics, our success ensures regulatory complaints. Boost productivity and improves the patient experience the path forward. By unifying ai data governance and cloud native technologies, organizations can replay billions in lost revenue while restoring trust in the healthcare system. And the immediate next steps would be. Conducting comprehensive data quality assessment across your organization, establish the cross-functional governance communities with clear cross now fraud detection mandates and begin a pilot implementation until a partnership with technology providers. So the AI healthcare data requires commitment, investment, and strategic vision to justify the effort required to implement this next generation fraud in capabilities. Thank you all for giving me this opportunity.