Hello everyone. I'm Satish Kumar Puram. I work as a specialist leader in Deloitte Consulting, LLP based out of Atlanta office. I do lot scale SAP implementation across the globe, and I have been doing this from last 22 years in my career. And I've done multiple SAP implementations for big clients. From various industries, starting from manufacturing, insurance, retail all sorts of industries I would say that I've touched upon. As part of con 42 mission Learning 2025, I would like to present the driving digital transformation in insurance with SAP and and machine learning and a leadership perspective. Now in today's insurance landscape, the convergence of SAP expertise and machine learning technology is creating. Unprecedented opportunities for transformation. This presentation explores how forward thinking insurance companies are leveraging these powerful tools to overcome industry challenges and create sustainable competitive advantages. We'll examine practical applications across claims management, underwriting, and policy administration while highlighting the real world success stories. That demonstrate measurable dis impact. Now, if you come to the insurance sector, the insurance landscape, there are multiple challenges. Now, the notable challenges that I would say is your InsureTech disruption, like new digital first competitors are challenging traditional insurance models with agile operations and innovative product offers. At the same way you have limitations based on legacy system that is. You have an outdated infrastructure restricts innovations. It creates data silos and increases operational cost for established insurers. Same way, cybersecurity threats is a big concern. Increasing sophistication of attacks against sensitive policyholder data demands robust security solutions and proactive monitoring. Same way changing customer expectations. You have digital, having customers demand personalized, frictionless experience across. All touch points and immediate service resolution. Now answer this problems within the insurance landscape. I think SAP has come up with multiple solutions. Now, SAP solutions, powering insurance transformation has set a 61 percentage. A process time in reducing all processing time with SAP implementation, as well as it has provide 30% of efficiency gain by Im by improving in operational efficiency across functions. And there is approximately 40 percentage cost reduction in operational cost through automation. And at the same point time, there is 3.5 x. A written of investment within 24 months of deployment. These impressive metrics demonstrate why leading insurers are investing in SAP solutions. By leveraging these powerful platforms, companies are achieving unprinted levels of efficiency while simultaneously improving customer satisfaction and reducing operational costs. Now. How SAP is solving this problem, how core SAP solutions for insurance is helping solve the problems of insurance companies. Now, SAP comes with SAPS four hana, which brings in a real time data processing and an analytics foundation that supports intelligent enterprise operations for insurance. And there is a specific SAP insurance analyzer tool that has been specifically customized and designed for. Insurance companies, which specializes in risk analysis and complaints management for insurance specific regulatory requirements. And there is a systematic data maintenance sewer, which helps in comprehensive data integration, quality management, and governance for insurance information lifecycle. And in the same way there is S-A-P-C-R-M module. This is very specific to customers. It is customer centric platform enabling personalized policy holders, ex expertise experiences and relationship management. These integrated solutions create a foundation for insurance companies to build modern digital operations by connecting previously so solid functions, insurance gaining. Complete view of their business and can make data driven decisions with confidence. And with SAP implementation, even with machine learning integration there are certain practical applications that comes up with the advantages. The main advantages that we see out of machine learning integration is with claims processing automation, as an example. Now, machine learning algorithms review claim documents. Identify fraud indicators and automated straightforward claims approval, reducing processing time from days two minutes while retaining accuracy. At the same time, mission learning comes up with dynamic underwriting which helps in pre predictive models, analyze customer data to generate personalized risk profiles and premium calculations, enabling more accurate pricing. And expanded coverage options for previously underserved segments. Now, there is a AI driven customer service as well. The virtual assistance and sentiment analysis tools enhance customer interactions flow 24 by seven support which leads to proactive issue identification, and personalized recommendations based on policy details and history. This application demonstrate how SAP system. Enriched with mission learning capabilities deliver tangible business benefits. The integration creates intelligent workflows that continuously improves through data analysis and pattern recognization. Now, system integration strategies within SAP and mission learning that would come as business value alignment. This integration prioritized by business impact. API first architecture, standard connections between systems, data governance, frame framework, consistent data quality across platforms. Security by design protection embedded through I, I've seen security by design as one of an important component in my experience where insurance companies are very happy with now. Successful integration requires a strategic approach that balances technical requirements with business objectives. By establishing this foundational elements, insurance companies can create a flexible architecture that connects legacy systems with modern SAP solutions. While ensuring data integrity and security, this pyramid approach emphasizes starting with business alignment and binding upward through increasing technical layers, ensuring that integration efforts deliver maximum value while minimizing disruption to ongoing operations. One of the biggest challenges that I've seen in my entire career while implementing SAP, not only to insurance, even it applies to other. Industries is data migration. Now with SAP with machine learning, there is a data migration where we are putting in this practice now. One of those will, will cover them one by one, data discovery and assessment, comprehensive audit of existing data sources, quality assessment and mapping into target SAP structures to identify transformation requirements. Cleansing and enrichment. Of course, cleansing plays an important role with data migration. So what we do as part of cleansing and enrichment, systematic process to address data quality issues, eliminate redundancies, and enhance record with additional attributes to maximize value. Testing and validation. Of course, very important. Multiple test cycles with respective data samples to verify transformation rules, system performance and business process. Integrating post migration monitoring while once the migration is done will not stop. The monitoring will be continuously monitoring the system, the data that you're getting in. So what we do as part of post migration monitoring is to do an ongoing evaluation of data accuracy. System performance and business processes effectiveness to identify and address issues quickly. Insurance companies handle massive volumes of sensitive data across policies, claims, and customer records following the structured migration practices ensures. This valuable information transfers correctly to SAP platforms while maintaining the regulatory compliance and business continuity now process automation framework. What we do as part of this process automation framework is process discovery and analysis. The first step is identify the process, workflow, observation and documentation, identification of automation candidates written of investment calculation for each process. Stakeholder alignment on priorities. Then the next step would be automation, design and development process redesign for optimization, RPA bot programming and configuration ML model training for decision points. Of course, machine learning models will be very effective here and SAP system integration setup. Once this is automation, design and development is done naturally, we'll go into the testing and deployment mode wherein we do control testing. In staging en environments, user accept acceptance verification, then phased implementation approach. And then we do a performance monitoring dashboard setup. And after that, once deployment is done, there is always a lookout for a continuous improvement wherein we look into the regular process performance reviews. We do machine learning, model retaining with new data, as well as automation expansion to related processes, and then technology updates and enhancements. This comprehensive framework guides insurers through the automation journey from initial identification of opportunities through ongoing enhancements. By following this structured approach, companies can e effectively transform manual insurance processes into streamlined digital workflows. As part of this process, there are leadership challenges as well in SAP implementations, and one of the important challenge would be cross-functional team management. Successful SAP implementations require alignment between IT specialists, business units, and external consultants. Leader must bridge technical and business perspectives while managing competing priorities and resource constraints across departments. That is one of a big challenge that I would say. And then the next one would be business continuity during transition, maintaining uninterrupted insurance operations. During implementation is critical. Leaders must develop comprehensive transition strategies that minimize disruption to customer service, claims processing, and policy management throughout the transformation. Then the technical technical barrier resolution, complex technical challenges inevitably arise during implementation, effective leaders established robust problem solving frameworks. Empower technical teams to innovate solutions and maintain progress momentum despite optical. So I think all these three factors will be very critical, and I work on all these types of problems in my day-to-day SAP implementations that I work with. Now let's look into the case study how SAP and machine learning is helping out by taking an example of Northeast Insurance growth. Now if you see the graph itself indicates there is a pre-implementation and post-implementation claim processing time. We see that before SAP the claim processing time is very high. After SAP, the claim processing time is very low. Same with policy insurance time. Even the customer satisfaction, how it has enhanced after SAP and how it was less before SAP. Then how the operating costs have changed, how operating costs were high before SAP, and how it has cut down the cost. After SAP, so not insurance group, a midsize regional insurer implemented SAPS four HANA and integrated machine learning capabilities to transform their operations. This, the company faced increasing competition from digital first insurers. And struggling with aging systems that that limited innovation. Their phase implementation approach, prioritize claims processing and policy administration with careful attention to data migration and staff training. They, the results were Ed dramatically reduced processing times significantly significantly. They had a good amount of cost savings and. Remarkably improved customer satisfaction scores that revised a declining market share trend. So key takeaways and next steps with this strategy is. Strategic planning Align SAP implementation with business Global goals, team development, build cross function expertise and leadership. Phased implementation. Start with high impact areas for quick wins and then continuous innovation. Leverage machine learning for ongoing implement. SAP Solutions Learning Offer insurance companies. Powerful tools to overcome industry challenges and drive digital transformation. The companies that will thrive are those whose leaders view technology as a strategic enabler rather than just an operational necessity. We recommend being with a comprehensive assessment of your current system and processes to identify the highest impact opportunities, develop the a phase implementation roadmap that balances goals, most importantly, invest in developing both the technical and leadership capabilities needed to drive successful transformation. Thank you, and thank you for this opportunity. I.