This is Bhaskara Biraka, CSN technology leader with extensive experience in designing and developing enterprise applications. My expertise spans distributed system, microservices, and cloud platforms, including AWS and Azure. I have proven track record in leading across the functional teams, architecting scalable solutions. And optimizing system performance, passionating about innovation. I continuously leveraging emerging technology to drive efficiency and business impact. Today my topic is adapting AI in insurance industries. Artificial intelligence is a resolutionized insurance industry, driving the transformative advancements across the key processes such as underwriting, claim management, fraud detection. And customer engagement. This presentation will provide the comprehensive technical framework for the implementation in insurance, highlighting practical deployment, strategy, the real world. Outcomes and a solution to the challenges attendees will gain the actionable insights into the leveraging AI to improve the efficiency, accuracy and customer satisfaction while positioning their organization for sustainable growth in an increasingly digital landscape. So EIA powered underwriting systems. Underwriting system is one of the key module in the insurance industries. enhance the risk assessment. advanced machine learning algorithm analyzing over 1000 data points per application improve the risk assessment accuracy by 85%. This system utilize the neural networks and gradient boosting to process the structured and unstructured data including the medical records. Credit, credit histories and IOT censored data, enabling more, non censored risk stratifaction, stratification, sorry. reduced processing time, automated underwriting systems, achieved 70 percent reductions in the processing time, cutting policy insurance from the week to hours. Natural language processing extracts the relevant information from the documents instantly, while parallel processing capabilities enable the simultaneous evolutions of multiple applications to dramatically increase the throughput. Improved the accuracy. Deep learning models. Reduce the human errors by 92 percent while increasing the pricing, pricing precisions by 76%. By continuously learning the from historical claims data and real time market conditions, this system optimize the premium calculations and reduce the loss ratios by an average of 18%. Directly improve the insurer's bottom line. AI in a claims management. Accelerate the settlement processing. Advanced predictive analytics frameworks have reduced the average claim settlement times from 30 days to just 4. 5 days, and 85 percent improvement. Machine learning algorithms analyze the historical claim data, policy terms, and risk factors to instantly validate the claims and calculate the optimal payout amounts. Enhanced Customer Experience The implementation of AI driven claims processing has driven customer satisfaction scores from 72 percent to 92%. The real time claims status updates, automated communication workflows, and faster settlements have resulted in a 45 percent increase in policy renewal rates. And a 38 percent rises in the customer referrals. Precision Damaged Assessment So the state of the art computer vision algorithm, powered by deep learning neural networks, have a revolutionized damaged evaluation. Assessment times have dropped from 48 hours to just 2. 4 hours. While achieving a 98. 5 percent accuracy in the damaged estimation, this system processes over 10, 000 images per day. Analyzing the damaged pattern across the multiple insurance categories. Fraud Detection Systems High speed processing. Advanced AI powered fraud detection system analyzing over 125k transactions per second using the parallel processing architecture. This system employs sophisticated neural networks to identify suspicious patterns and anomalies in real time, enabling immediate intervention when the potential fraud is detected. Enhanced Accuracy. Leveraging a deep learning algorithm, our fraud detection system achieves a 99. fraud tolerant activities. The system's multi layered neural networks continuously learn from new data veterans, adapting to emerging fraud tactics while maintaining the exceptional precision in the threat detection. Reduce the false positives. Through the sophisticated feature engineering and ensemble learning techniques, false positives or rates have been reduced to an industry leading 0. 05 percent. This breakthrough ensures minimal disruptions to legitimate the transaction while maintaining the robust security, resulting in improved customer experience and operational efficiency. Computer Vision in the Clients Processing Reduced assessment times Advanced Computer Vision Technology Accelerated Damage Assessment by 95 percent Revolutionizing the Clients Process Sophisticated AI algorithm analyzing the high resolution images and video footage of damaged assets to generate the precise repair cost estimation within the minutes. Improved the accuracy, the state of the art computer vision system achieves an unprecedented 98 percent accuracy in the damaged evolution. This system leverages the deep learning model to detect and classify even microscopic damaged patterns, ensuring the consistently precise claim estimation across all types of assets. Real time insights. Our cutting edge infrastructure process an industry leading 1. 5 million clients daily, delivering comprehensive analytical insights with response times under 200 milliseconds. This transformative process capability enables instant decision making and sets new standards for client handling efficiency. Predictive analytics for the real time insights. A real time data analysis. Our advanced predictive analytics engine processes an unprecedented 1. 5 million clients daily. Leveraging the sophisticated AI algorithms to analyzing the complex data sets, the system continuously monitoring 50 plus data points per client, enabling the instant risk assessment and automated decision making. Lightning fast response time. With the industry leading query, latency of under 200 milliseconds. Insurers can access the critical insights 75 percent faster than the traditional system. This unprecedented speed enables the real time fraud detection and instant client validation significantly improve the customer satisfaction. Data Driven Optimization By harnessing predictive analytics, insurers have achieved a 40 percent improvement in operational efficiency. The system's AI powered forecasting, the enabling process. Precise resource allocation, reducing the processing cost by 35 percent while maintaining the 99. 9 percent accuracy in the risk assessment, economic benefits of AI adoptions, cost reductions. Yay. Automation slashes operational cost by 38%. Through the intelligent process optimization, mission learning algorithm reduced their manual processing time base and percent while cutting error rates from the 12% to under 1% improved the NPS customer satisfaction source with the net prompter scores. Jumping 48 points from plus 12 to plus 60. Climb processing time drops from seven days to just to 24 hours. Driving unprecedented, customer loyalty and, word of mouth growth. Enhancing profitability. AI implementation delivers the 31. 2 percent ROI within the 18 months. Companies reported average profit margin increases of 15. 3%. Combined 2. 5 million annual, cost saving with 23 percent higher customer retention rates. Data Management Challenges Data silos, data quality, data security Insurance companies face critical data management hurdles that directly impact their AI effectiveness. Legacy systems create data silos that isolate up to 80 percent of valuable customer information across departments, making it nearly impossible to build comprehensive risk profiles. Poor data quality compounds this challenge. With inconsistent formats and outcomes. Outdated records leading E two A model that can, misclassify the risk by up to 30%. Furthermore, stringent data security requirements mandate the state of the art encryption and access control to protect the sensor to customer data as the average cost of insurance. Data breach now exceeds 4.2 millions legacy system integrations. APS, microservices, middlewares, legacy system integration requires a carefully orchestrated technical approach. Modern APS serves as a secure gateway, enabling standardized data exchange between the traditional systems and AI applications. While maintaining the data integrity, microservice architecture break down the complex integrations into the manageable independent component that can be updated without destructing entire system. Robust middleware acts as a translator between the legacy. Databases and new AI platforms ensure seamlessly communication and data transformations. Performance and Optimization and Distributed Processing Spark, Kafka, and Hadoop Modern distributed processing frameworks revolutionize insurance data operations. Apache Spark in memory processing accelerates complex calculations by 45%, while Apache Kafka real time streaming capabilities enable instant data flow between the systems, cutting operational costs by 82%. Supporting these technologies, Microsoft's architecture breaks down the monolithic system into manageable components. Slashing implementation time by 85 percent and virtually eliminating the system downtime with a 97 percent reduction in outages. Explainable AI Frameworks Explainable AI framework revolutionized insurance operations by transforming the complex algorithmic decisions into transparent, interpretable insights. This cutting edge system leverages sophisticated visualization tools and natural language processing to break down the AI decisions into clear. Step by step reasoning path with an outstanding 95 percent regulatory adherence rate. These frameworks generate the comprehensive audit trials that document every factor influencing automated decision. From risk assessment variables to climb the processing parameters. The implementation of explainable AI has proven transformative for the customer relationships. Driving an 85 percent reduction in disputes through the detailed, jargon free explanations of underwriting decisions and claim assessments by bridging the gap between advanced AI capabilities and human understanding, these frameworks have become indispensable tools for the insurers committed to build trust while maintaining technological innovations. Thank you so much.