Chetan Prakash Ratnawat
Madhav Institute of Technology and Science, Jiwaji University
Buffalo Grove, Illinois, United States
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Chetan Prakash Ratnawat is a seasoned Cybersecurity Architect and AI Strategist with over 25 years of experience driving innovation across the insurance and financial services sectors. Based in Chicago, IL, Chetan has played a pivotal role in shaping the future of cyber insurance through the integration of advanced AI technologies, predictive analytics, and secure digital transformation. His expertise spans the development of AI-driven risk scorecards, underwriting automation systems, DevSecOps platforms, and digital workflows that collectively improve operational efficiency and profitability.
Currently serving as a Senior Manager at Capgemini America Inc., Chetan leads transformative cybersecurity initiatives for clients such as Chubb Insurance and the Depository Trust and Clearing Corporation (DTCC). His most notable contributions include the Chubb Cyber Signals (CCS) platform, an AI-powered cyber risk intelligence system that reduces claim sizes and improves underwriting accuracy by leveraging real-time external risk signals. Chetan also spearheaded the development of the Cyber Multi-Quote (CMQ) engine and the Cyber ProERM scoring models, which revolutionized quote generation and profitability-based underwriting using machine learning and rule-based automation.
Chetan’s strategic vision has also shaped client engagement tools like the Extended Risk Report, which integrates third-party intelligence with internal risk assessments to provide tailored, industry-specific insights. His leadership in DevSecOps transformation at DTCC brought measurable improvements in deployment velocity, compliance governance, and risk monitoring through AI-augmented dashboards and secure CI/CD workflows.
A published author and recognized thought leader, Chetan’s research and innovations have been featured in leading industry publications, internal knowledge platforms, and AI workshops. His book, Driving Digital Empowerment Through Cybersecurity Innovation in Insurance, reflects his commitment to advancing the industry through responsible, explainable AI and data-centric security solutions.
Chetan holds a Master’s in Computer Applications and several high-impact certifications, including PMP, PMI-ACP, SAFe SPC, CSM, and AWS Cloud Practitioner. His core competencies include AI-powered cyber risk modeling, predictive underwriting, DevSecOps governance, and enterprise delivery management under Agile and SAFe frameworks.
With a passion for technology-driven impact and a talent for bridging business needs with advanced cyber capabilities, Chetan continues to guide organizations toward smarter, safer, and more scalable digital futures.
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Powering Cyber Risk Intelligence with SQL Server and AI for Smarter Insurance
The cyber insurance industry is undergoing a major shift—from static, questionnaire-based underwriting to dynamic, data-driven platforms that assess real-time risk. At the core of this transformation are AI-powered cyber risk scorecards—intelligent systems that analyze vast volumes of security and behavioral data to help insurers accurately quantify and price risk for Small and Medium Enterprises (SMEs).
In this session, we’ll explore how modern cyber risk platforms are built using the Microsoft data ecosystem, including SQL Server, Azure Synapse Analytics, Power BI, and ML services. Attendees will gain insights into how structured and unstructured security data—such as network vulnerabilities, breach histories, and user behavior—is ingested, cleaned, and modeled within SQL-based pipelines to power predictive risk scoring engines.
We’ll discuss the database design and data warehousing strategies that support scalable analytics and real-time decision-making, including the use of partitioning, columnstore indexes, and automated ETL workflows. You'll also learn how actuarial outputs and compliance mapping are supported through T-SQL reporting, Power BI dashboards, and data governance practices—all critical in the highly regulated insurance domain.
Real-world case studies will demonstrate how insurers are using these platforms to reduce policy issuance time, improve loss ratios, and enable proactive engagement with customers on cyber hygiene. This talk will highlight how SQL Server professionals can play a key role in building secure, AI-ready data architectures that support risk visibility at scale.
If you're working with SQL Server and interested in real-time analytics, cloud integration, or applying AI to business-critical applications, this session will provide practical examples and architectural patterns for transforming raw data into trusted cyber risk intelligence that drives meaningful impact.
Real-Time AI in Insurance: Driving Insights and Automation with StarRocks
The insurance industry is experiencing a profound transformation as AI technologies increasingly power core functions such as underwriting, claims triage, and fraud detection. Delivering these capabilities at scale requires not just advanced models, but high-performance analytics infrastructure capable of supporting real-time, low-latency decisioning across massive volumes of structured and semi-structured data. This session explores how leading insurers are leveraging StarRocks to enable high-speed analytics and intelligent automation across their enterprise data ecosystem.
Attendees will learn how StarRocks is used to unify streaming and batch data sources, support real-time dashboards, and deliver sub-second analytical queries that feed directly into AI decision engines. These capabilities power AI platforms that process thousands of applications per hour, auto-triage 85% of claims on first pass, and improve fraud detection rates by over 60%. The session will showcase how columnar storage, vectorized execution, and materialized views in StarRocks accelerate time-to-insight and support mission-critical AI use cases with millisecond-level responsiveness.
We’ll also discuss how StarRocks fits within a modern insurance data architecture—supporting scalable data ingestion, integration with feature stores, and downstream API delivery for predictive workflows. With strong support for SQL and real-time OLAP performance, StarRocks enables data teams to operationalize analytics and embed intelligence into decision systems at scale.
Ideal for data architects, AI engineers, and analytics leaders, this session delivers a blueprint for implementing StarRocks in AI-native environments to unlock speed, scale, and precision in the modern insurance enterprise.
Real-Time Risk Scoring with StarRocks: Powering AI in Cyber Insurance
AI-powered cyber risk platforms are redefining how insurers assess and manage SME risk—moving from static surveys to real-time, data-driven insights. This session explores how StarRocks enables high-performance analytics across streaming threat data, behavioral patterns, and telemetry to fuel dynamic underwriting decisions. Attendees will learn how StarRocks supports fast, federated queries over multi-source datasets, accelerating AI model training, risk scoring, and compliance reporting. We’ll share real-world use cases where StarRocks powers secure, scalable platforms that reduce policy issuance time and improve risk visibility. This talk is ideal for data engineers and architects building AI-native systems with high-throughput, low-latency analytical workloads.
Scalable AI for Insurance: Cloud-Native Automation in Action
As insurers adopt cloud-first strategies to modernize legacy systems, artificial intelligence (AI) is emerging as the engine for large-scale operational transformation. This session explores how global insurance companies are leveraging cloud-native AI to streamline underwriting, accelerate claims processing, and reduce administrative overhead—without sacrificing reliability, compliance, or customer trust.
Through real-world case studies, we’ll examine how enterprises are deploying modular, cloud-based architectures that integrate machine learning, natural language processing (NLP), robotic process automation (RPA), and predictive analytics. These systems are not just augmenting human decision-making—they're autonomously processing thousands of transactions per hour with near-perfect accuracy, auto-triaging over 85% of claims on first pass, and enabling up to 70% automation in daily operations.
The talk will showcase a scalable blueprint built on cloud computing principles, covering microservices design, container orchestration, serverless pipelines, and continuous integration of ML models. We’ll also explore real-time telemetry, monitoring, and analytics strategies for cloud-hosted AI workloads, all aligned with security and governance standards essential to the insurance industry.
Attendees will gain actionable insights into how intelligent automation on the cloud has driven measurable outcomes: cutting claims cycle times by more than half, reducing underwriting decisioning from weeks to hours, and improving fraud detection rates by over 60%.
Whether you’re a cloud architect, data engineer, or enterprise technologist, this session will equip you with patterns and practices to implement resilient, intelligent, and highly available AI systems in cloud-native environments. The talk draws from production implementations at scale and is backed by both quantitative metrics and strategic insights.
Secure by Design: Building Trustworthy AI-Native Cyber Risk Platforms for the Insurance Ecosystem
As organizations accelerate their shift toward AI-native systems, the intersection of machine learning, cloud infrastructure, and security has never been more critical. Nowhere is this convergence more visible than in the cyber insurance sector, where AI-powered risk scorecards are being used to assess exposure, determine policy terms, and recommend remediation in real time. This talk explores how these platforms are engineered for trust—through secure design, transparent modeling, and continuous risk validation.
Built on modern DevSecOps principles, these systems ingest real-time security telemetry, behavioral signals, and threat intelligence to produce dynamic, multidimensional risk profiles for Small and Medium Enterprises (SMEs). But beyond the model outputs lies a complex architecture that must maintain integrity, auditability, and regulatory compliance across the ML lifecycle. We'll examine how teams are securing training pipelines, managing data lineage, automating compliance mappings, and implementing controls like RBAC, CI/CD attestation, explainable AI, and runtime monitoring to meet insurer-grade reliability and governance standards.
Real-world case studies will highlight how secure MLOps and infrastructure-as-code practices are applied to deploy AI-driven systems in production environments—balancing model agility with operational resilience. Attendees will also learn how AI-native risk engines integrate into legacy underwriting workflows and reshape how insurers engage with clients—not just after a breach, but in active risk mitigation and security posture improvement.
If you’re a security engineer, ML practitioner, or DevSecOps leader navigating the challenges of securing AI systems in regulated domains, this session offers a blueprint for delivering not just performant models, but systems of trust. Learn how the cyber insurance industry’s transformation is setting the tone for securing the next generation of AI-native platforms—where accountability, explainability, and resilience are non-negotiable.
Securing AI-Native Transformation in Insurance: Threat-Resilient Automation at Enterprise Scale
As organizations shift to AI-native operations, security becomes a non-negotiable pillar of deployment. Nowhere is this more evident than in the insurance industry, where AI is automating high-stakes workflows—underwriting, claims processing, and agent support—at massive scale. But with this transformation comes new and evolving risks.
In this session, we’ll explore a security-first approach to implementing AI in regulated, data-sensitive environments. Drawing from real-world deployments in global insurance firms, we’ll walk through a modular AI architecture that combines machine learning, NLP, robotic process automation (RPA), and predictive analytics—augmented with layered security controls, auditability, and real-time monitoring.
You’ll learn how insurers have reduced fraud by over 60%, automated up to 70% of routine operations, and shortened decision cycles by more than 50%—all while embedding traceability, model risk management, and compliance with frameworks like SOC 2, HIPAA, and GDPR. Topics will include secure prompt engineering, output validation, adversarial input detection, and the implementation of observability stacks to detect model drift and access anomalies in real time.
This talk is designed for security engineers, DevSecOps leaders, and AI architects seeking to safeguard the shift to AI-native enterprise environments. We’ll offer practical guidance on integrating zero-trust principles into AI systems, establishing model governance workflows, and designing resilient pipelines that protect both the business and the customer.
Attendees will leave with a proven blueprint for deploying secure, explainable, and operationally reliable AI systems at scale—capable of transforming enterprise performance without compromising trust or compliance.
Securing the Cloud: Building AI-Powered Cyber Risk Platforms on Azure for Insurance Innovation
As SMEs face growing cyber threats, insurers are turning to AI-powered platforms built on Microsoft Azure to deliver real-time, cloud-native risk assessments. This session explores how Azure services—from Synapse Analytics and Azure Machine Learning to Logic Apps, Sentinel, and Defender for Cloud—are orchestrated to build intelligent cyber risk scorecards that transform traditional underwriting.
We’ll walk through how telemetry, threat intelligence, and behavioral data are ingested, modeled, and surfaced via scalable Azure pipelines to support dynamic policy decisions, automated compliance, and proactive remediation. Real-world use cases will highlight how cloud-native design, AI/ML, and integration with the Microsoft security ecosystem are reshaping cyber insurance from static coverage into continuous protection.
Whether you’re an architect, data engineer, or security professional, this session will offer a practical blueprint for applying Azure to high-impact, AI-driven industry problems—where risk visibility and cloud performance converge.
Transforming Cyber Insurance with Data: AI-Driven Risk Scoring for SMEs
In today’s rapidly evolving threat landscape, Small and Medium Enterprises (SMEs) are increasingly exposed to cyber risks. Yet, traditional insurance underwriting models—based on static questionnaires and infrequent assessments—are no longer effective. This session explores how data professionals are playing a vital role in the development of AI-powered cyber risk platforms that provide dynamic, real-time insights into organizational exposure and risk posture.
At the heart of these platforms are rich data pipelines that ingest and analyze diverse information sources—including network vulnerabilities, breach history, endpoint telemetry, and behavioral analytics. We’ll examine how modern data architecture—including SQL-based warehousing, cloud-native ETL workflows, and predictive analytics—is used to generate actuarial-grade risk scores that inform insurance pricing, coverage decisions, and proactive remediation.
The talk will focus on the full lifecycle of data in a cyber insurance context: from ingestion and data transformation, through modeling and governance, to visualizing insights with reporting tools like Power BI. Attendees will learn how to ensure data quality, implement schema design patterns that support scalability, and apply machine learning outputs responsibly within regulated environments. The session also covers how data teams manage compliance mapping (e.g., NIST, CIS), track lineage, and deliver transparency into AI decision-making pipelines.
Case studies will highlight measurable outcomes including reduced policy issuance time, improved underwriting accuracy, and enhanced client engagement. Whether you’re working in data engineering, BI, or analytics, you’ll leave with a practical framework for how your skills can drive meaningful innovation in a growing sector—where data is not just an asset, but the foundation for securing the digital economy.
Transforming Insurance with AI: From Concept to Impact
Artificial intelligence is reshaping the insurance industry, redefining how underwriting, claims management, and customer engagement are handled. This session explores how AI can be practically applied to transform operations, reduce inefficiencies, and enhance both accuracy and customer satisfaction.
Drawing on real-world examples, the presentation will show how intelligent workflow automation and AI-driven decision systems simplify complex processes, minimize manual intervention, and accelerate outcomes. Attendees will gain insights into how insurers are adopting AI-powered solutions to streamline underwriting, improve claims handling, and empower agents with real-time insights.
The session will also introduce a modular AI architecture that integrates machine learning, natural language processing, robotic process automation, and predictive analytics. Together, these capabilities enable continuous monitoring, real-time analytics, and seamless integration with existing enterprise systems. This approach ensures scalability, adaptability, and long-term sustainability.
Through case-based discussions, participants will explore how AI technologies are being leveraged not only to optimize operations but also to strengthen fraud detection, improve data quality, and deliver more personalized customer experiences. Beyond technical frameworks, the session emphasizes aligning AI adoption with organizational strategy to create meaningful, lasting business impact.
Designed for technical leaders and enterprise decision-makers, this presentation provides an actionable blueprint for scaling AI adoption. Attendees will leave with practical strategies to transform operational performance, enhance customer relationships, and build resilient organizations capable of thriving in an evolving and competitive insurance landscape.
Transforming Insurance Operations with AI: From Vision to Practice
The insurance industry is undergoing a major transformation fueled by artificial intelligence, reshaping how core functions such as underwriting, claims management, and customer support are delivered. This session will provide a practical and technically grounded perspective on how AI can be systematically integrated into operations to drive efficiency, accuracy, and customer satisfaction.
Drawing on real-world implementations, the talk highlights how intelligent automation and AI-driven decision systems streamline processes, minimize manual intervention, and reduce error rates across the value chain. Attendees will gain insight into how modular AI architectures built on machine learning, natural language processing, robotic process automation, and predictive analytics are being applied to deliver tangible improvements.
The session will also demonstrate how AI platforms enable continuous performance monitoring, real-time analytics, and seamless integration with existing insurance ecosystems. Through case-based evidence, participants will explore how leading insurers have adopted AI-enabled systems to improve fraud detection, enhance data accuracy, accelerate decision-making, and deliver faster, more personalized services at scale.
Designed for technical leaders and enterprise decision-makers, this presentation offers an actionable blueprint for implementing AI solutions that transform operational performance while strengthening competitive advantage. The insights are grounded in proven results across global insurers and supported by research from leading industry analysts.
AI-Powered Risk Scorecards: Transforming Cyber Insurance
Cyber insurance is entering a new phase of innovation as AI-powered risk scorecards reshape how insurers assess and manage exposure, especially for Small and Medium Enterprises (SMEs). Traditional methods based on static questionnaires are being replaced by intelligent platforms that synthesize diverse data sources such as real-time vulnerabilities, breach histories, and behavioral analytics into dynamic risk profiles. This approach gives insurers deeper visibility into cyber threats while providing SMEs with more transparent and equitable terms.
Unlike generic security tools, these scorecards are purpose-built for insurance. They integrate seamlessly into underwriting workflows, generate actuarial-ready outputs, and automate compliance documentation. Their precision in segmenting risk allows insurers to align pricing with actual exposure, while also giving SMEs actionable insights into their security posture. Beyond assessment, embedded remediation guidance and regulatory compliance mapping transform insurers into active partners in holistic risk management.
This session will explore the architecture behind AI-driven cyber risk scorecards, including how cloud-native technologies and scalable AI models power their capabilities. Real-world case studies will highlight how insurers are leveraging these platforms to improve efficiency, enhance decision-making, and strengthen client trust. Attendees will also gain a forward-looking perspective on innovations such as continuous risk reassessment, predictive breach modeling, and partnerships with managed service providers.
The discussion will be especially valuable for insurance leaders, cybersecurity professionals, and digital transformation strategists interested in how data-driven, AI-enabled solutions can redefine cyber underwriting and make insurance an integral part of enterprise resilience strategies
AI-Powered Risk Scorecards: Transforming Cyber Insurance with Data and Fabric
Cyber insurance is undergoing a profound shift as AI-powered risk scorecards replace static, questionnaire-based underwriting. Tailored for Small and Medium Enterprises (SMEs), these platforms integrate diverse data sources including vulnerabilities, breach histories, and behavioral analytics into dynamic risk profiles that reflect real exposure. This enables insurers to provide fairer policies while giving SMEs actionable insights into strengthening their security posture.
Unlike generic security solutions, these scorecards are built specifically for insurance. They integrate seamlessly into underwriting workflows, generate actuarial-ready outputs, and automate compliance reporting. By combining risk assessment with remediation recommendations and regulatory mapping, they help insurers evolve from passive policy providers to active partners in resilience and risk management.
This session will highlight the data architecture behind AI-driven cyber risk scorecards, with a focus on how Microsoft Fabric and modern cloud platforms make them scalable, automated, and reliable. Real-world examples will illustrate how insurers are using these solutions to enhance efficiency, strengthen trust with clients, and uncover new opportunities for innovation.
Looking ahead, we’ll explore how continuous risk reassessment, predictive breach modeling, and partnerships with managed service providers are reshaping the landscape. These capabilities point to a future where cyber insurance is no longer just a financial safeguard but a core element of enterprise resilience strategies.
Attendees, including data professionals, insurance leaders, and digital transformation experts, will gain practical insights into how AI, cloud-native data platforms, and Fabric can redefine cyber underwriting for the next generation.
AI-Powered Transformation in Insurance: From Strategy to Execution
The insurance industry is rapidly evolving under the influence of artificial intelligence, with innovations reshaping core areas such as underwriting, claims management, and customer support. This session will offer a practical roadmap for implementing AI to streamline operations, enhance efficiency, and deliver better customer outcomes.
Drawing from real-world examples, the talk will demonstrate how intelligent workflow automation and AI-driven decision systems simplify complex processes, reduce manual effort, and improve accuracy. Attendees will see how these technologies enable insurers to accelerate underwriting, improve claims handling, and empower agents with real-time insights.
The session will highlight a modular AI architecture that integrates machine learning, natural language processing, robotic process automation, and predictive analytics. Together, these capabilities provide continuous monitoring, real-time analysis, and seamless integration with existing systems ensuring solutions that are adaptable and scalable.
Case studies will show how insurers are applying AI to strengthen fraud detection, reduce administrative burdens, and deliver more personalized services to customers. Beyond technology, the discussion will focus on aligning AI adoption with strategic goals to drive lasting business value and competitive advantage.
Designed for technical leaders and enterprise decision-makers, this presentation provides an actionable blueprint for embracing AI at scale. Attendees will walk away with practical insights on building resilient, customer-centric operations that leverage AI to transform performance, accelerate innovation, and adapt to the evolving insurance landscape.
Building Intelligent Cyber Risk Platforms with Azure for SME Insurance
As SMEs face rising cyber threats, the insurance industry is responding with AI-powered risk platforms that shift underwriting from static surveys to real-time, data-driven decisions. This session explores how these intelligent systems are built on the Azure ecosystem—leveraging cloud-native services, machine learning, and secure data pipelines to enable next-generation cyber insurance for small and medium enterprises.
Attendees will gain a deep understanding of how Microsoft Azure services—such as Azure Machine Learning, Synapse Analytics, Azure Functions, and Defender for Cloud—are orchestrated to collect and process diverse data sources including live network telemetry, threat intelligence feeds, and behavioral analytics. These signals power AI models that generate dynamic, multidimensional risk profiles, helping insurers evaluate cyber exposure, recommend proactive remediation, and align policy pricing with real-world threat conditions.
We’ll walk through the architecture of a typical Azure-based risk platform, from data ingestion and feature engineering to model scoring and API-based integration with underwriting systems. You'll learn how platform builders are using Azure DevOps, Key Vault, Logic Apps, and compliance tooling like Microsoft Purview to ensure operational integrity, regulatory alignment, and explainable AI outputs.
Real-world case studies will illustrate how early adopters have achieved faster policy issuance, improved loss ratios, and enhanced customer engagement through these cloud-native solutions. This session will also highlight key DevOps and MLOps practices that support continuous model improvement and system scalability.
Whether you're building secure data applications, deploying machine learning in production, or enabling real-time intelligence on Azure, this talk provides a practical roadmap for turning cybersecurity data into business value—through a secure, scalable, and AI-powered cloud architecture.
Cloud-Native Cyber Risk Scoring: How AI and Cloud Platforms Are Transforming Insurance for SMEs
As cyber threats grow in complexity and frequency, Small and Medium Enterprises (SMEs) are increasingly vulnerable and often underprotected. Traditional cyber insurance underwriting methods, based on static questionnaires and periodic assessments, are no longer adequate in today’s real-time threat landscape. Enter AI-powered cyber risk scorecards: cloud-native platforms that synthesize live telemetry, vulnerability data, breach histories, and behavioral analytics to generate dynamic risk profiles for SMEs.
In this session, we’ll explore how cloud computing is enabling a new era of scalable, intelligent cyber risk management. Built on distributed cloud architectures, these platforms leverage services such as event-driven data pipelines, containerized microservices, and serverless functions to process vast security data sets in real time. AI and ML models—trained on diverse threat indicators—produce actuarial-grade outputs that support adaptive underwriting decisions and automated remediation pathways.
Attendees will gain an inside look at how cloud services (across Azure, AWS, and GCP) are orchestrated to support high-availability scoring engines, ensure data sovereignty and compliance, and integrate seamlessly with both insurer back-ends and client-facing dashboards. Case studies will highlight how early adopters have achieved measurable improvements in loss ratio, customer retention, and policy issuance time by adopting a cloud-first approach to cyber insurance.
Whether you're a cloud engineer, product lead, or security architect, this talk will demonstrate how cloud platforms are not just hosting environments—but foundational enablers of intelligent, real-time risk assessment at scale. Join us to learn how to build resilient, AI-driven insurance systems that transform cyber insurance from a passive safety net into an active driver of security posture and business resilience.
Driving AI-Powered Insurance Analytics with Scalable Data Architecture
As the insurance industry embraces artificial intelligence to streamline operations, data professionals are playing a central role in making it all work. From predictive underwriting to automated claims triage, AI systems depend on robust, well-architected data pipelines that can scale, comply, and deliver insights in real time.
In this session, we explore how global insurers are using enterprise data platforms—including SQL Server, Azure Synapse, and Power BI—to fuel AI-powered workflows that transform underwriting, fraud detection, and customer engagement. With real-world examples, we’ll demonstrate how intelligent data pipelines support automation across high-volume operations, where accuracy, latency, and governance are critical.
You’ll see how data teams enable machine learning, NLP, and robotic process automation by orchestrating secure, high-throughput environments using SSIS, SQL Server ML Services, and Azure Data Factory. These systems power real-time scoring, process thousands of applications per hour, and deliver fraud detection gains over 60%—all while adhering to regulatory frameworks like GDPR and SOC 2.
We’ll also cover how to embed predictive analytics and AI outputs directly into business intelligence layers using SQL-backed APIs and Power BI dashboards, creating a seamless experience from raw data to decision-making.
Attendees will walk away with a blueprint for scaling AI initiatives without reinventing their data stack. Learn how to integrate modern AI into existing Microsoft data ecosystems using familiar tools, and how to optimize data quality, model feedback loops, and observability in production environments.
This session is ideal for DBAs, data engineers, BI developers, and analytics leads looking to bridge the gap between data platforms and intelligent automation—unlocking measurable value and operational agility in enterprise insurance and beyond.
Enterprise AI with .NET: Automating Insurance Operations at Scale with ML and Workflow Intelligence
AI and automation are redefining how enterprise insurers manage underwriting, claims, and service ops—and .NET developers are at the forefront of enabling this transformation. This session explores how companies are building real-time, AI-powered systems using C#, ML.NET, Azure Cognitive Services, and .NET-integrated RPA to streamline operations, cut costs, and increase fraud detection accuracy.
We’ll dive into a modular architecture that combines ML, NLP, and orchestration frameworks with .NET-based APIs and background services. Learn how AI-driven decisioning and intelligent workflows built with familiar .NET tools process thousands of applications per hour and automate up to 70% of manual tasks, all while ensuring security and compliance.
With real-world implementations, performance benchmarks, and architecture diagrams, this talk equips .NET professionals with an actionable framework to scale AI inside regulated enterprise environments—delivering impact without abandoning proven technologies.
Modernizing Cyber Insurance with .NET: Building AI-Powered Risk Platforms for SMEs
Cyber insurance is entering a new era—one powered by AI, real-time telemetry, and robust software architecture built with .NET. This session explores how developers are using the .NET ecosystem to create scalable, intelligent cyber risk scorecard platforms that help insurers assess and manage SME risk more effectively.
We’ll walk through how ASP.NET Core, Entity Framework, Azure Functions, and ML.NET are leveraged to ingest vulnerability data, behavioral analytics, and threat intel into dynamic risk profiles. These applications integrate securely with underwriting systems, deliver actuarial-ready outputs, and support real-time compliance automation—all built on a cloud-native .NET stack.
Attendees will gain practical insights into designing microservices, implementing API-first strategies, and applying AI/ML models to drive smarter decisions in the traditionally slow-moving insurance industry. Real-world use cases will show how .NET developers can lead the charge in building high-impact, secure, and AI-enabled systems that don’t just monitor cyber risk—they reshape how it’s quantified and mitigated.
Whether you're working in fintech, insurtech, or enterprise-grade SaaS, this session will give you a blueprint for applying your .NET skills to solve mission-critical problems in cybersecurity and risk intelligence.
Operationalizing AI with Data: Transforming Insurance with Scalable Intelligence
The future of enterprise transformation lies at the intersection of data and AI. Nowhere is this more evident than in the insurance industry, where intelligent automation is revolutionizing how organizations process claims, assess risk, and engage customers. But to succeed at scale, these AI systems depend on robust data pipelines, quality governance, and analytics that deliver real-time insights.
In this session, we’ll explore how data teams at major insurers are powering AI-driven workflows using familiar data platform tools such as SQL Server, Azure Data Factory, Power BI, and modern data lakes. You’ll see how scalable architectures support machine learning, natural language processing (NLP), and robotic process automation (RPA)—cutting underwriting time from weeks to hours and triaging 85% of claims automatically.
We’ll walk through how structured and unstructured data from diverse sources is unified, transformed, and used to train and serve models across secure, auditable pipelines. Real-world implementations show over 60% improvements in fraud detection, 70% automation in routine operations, and sub-second predictive response times—all achieved through optimized data infrastructure.
The session will also detail how to embed AI insights directly into dashboards, API endpoints, and operational systems to deliver immediate business value. You'll learn strategies for model monitoring, feedback loops, and data observability to ensure trust, accuracy, and compliance in production.
Whether you're a data engineer enabling machine learning workflows or a BI professional looking to integrate predictive insights into business processes, this talk provides a practical, real-world blueprint for data-first AI transformation in highly regulated environments like insurance.
Attendees will leave with an understanding of how to build scalable, intelligent systems using modern data tools that bridge the gap between analytics and enterprise automation.
AI in Insurance: Driving Operational Excellence
Artificial intelligence is transforming the insurance industry, reshaping core functions such as underwriting, claims, and customer support. This session will provide a practical, data-driven framework for adopting AI to streamline operations, reduce inefficiencies, and enhance customer engagement.
Drawing from real-world implementations, the presentation will explore how intelligent workflow automation and AI-based decision systems simplify complex processes, reduce manual workloads, and improve decision-making. Attendees will gain practical insights into how AI solutions can accelerate underwriting, optimize claims handling, and deliver actionable insights to agents and customers in real time.
A key focus will be on a modular AI architecture that integrates machine learning, natural language processing, robotic process automation, and predictive analytics. These technologies collectively enable continuous monitoring, advanced analytics, and seamless integration with existing platforms, ensuring flexibility and scalability across the enterprise.
Through case-based discussion, participants will see how insurers are applying AI not only to improve operational efficiency but also to strengthen fraud detection, enhance data quality, and personalize customer interactions. The session will emphasize aligning AI adoption with organizational strategy to achieve sustainable business impact and long-term competitive advantage.
This presentation is designed for technical leaders and decision-makers who want to move beyond theory to actionable implementation. Attendees will leave with a clear blueprint for scaling AI adoption in ways that transform operations, strengthen customer trust, and build organizational resilience in a rapidly evolving insurance landscape.
AI in Insurance: Practical Pathways to Operational Transformation
Artificial intelligence is reshaping the insurance industry, transforming how underwriting, claims, and customer support are managed. This session will explore practical strategies for applying AI to optimize operations, reduce inefficiencies, and deliver improved outcomes for both organizations and customers.
Through real-world examples, the presentation will show how intelligent workflow automation and AI-driven decision systems streamline processes, minimize manual intervention, and enhance accuracy. Attendees will gain insights into how insurers are leveraging AI to accelerate underwriting, enhance claims management, and empower agents with timely, data-driven insights.
The discussion will introduce a modular AI architecture that combines machine learning, natural language processing, robotic process automation, and predictive analytics. These technologies together enable continuous performance monitoring, real-time analytics, and seamless integration with existing insurance platforms. By adopting this approach, organizations can ensure scalability, adaptability, and measurable improvements across operations.
Case studies will highlight how insurers have successfully used AI to strengthen fraud detection, improve customer interactions, and reduce administrative burdens. Beyond the technical aspects, the session will also focus on aligning AI initiatives with business priorities to ensure meaningful, long-term impact.
Designed for technical leaders and enterprise decision-makers, this session provides an actionable blueprint for scaling AI adoption. Attendees will leave with clear strategies for driving operational transformation, enhancing customer experiences, and building resilient organizations that are prepared to thrive in an evolving and competitive insurance landscape.
AI-Driven Cyber Risk Scorecards for Smarter Insurance
Cyber insurance is entering a new era as insurers adopt AI-powered risk scorecards designed specifically for Small and Medium Enterprises (SMEs). Traditional underwriting has relied on static questionnaires, but these modern platforms bring together diverse data sources such as real-time vulnerabilities, breach histories, and behavioral insights, to create dynamic risk profiles that reflect the true cyber posture of each business. This shift enables insurers to streamline operations, enhance decision-making, and build stronger relationships with their clients.
Unlike generic cybersecurity tools, these scorecards are purpose-built for insurance. They integrate seamlessly with underwriting systems, produce actuarial-ready outputs, and automate compliance documentation. Their precision in segmenting risk allows insurers to align premiums with actual exposure, giving SMEs fairer terms while also providing actionable recommendations to strengthen their defenses. By offering both risk assessment and remediation guidance, these platforms transform insurers into trusted partners in proactive risk management.
This session will explore the architecture behind AI-driven cyber risk scorecards and highlight real-world applications that showcase their value. Attendees will learn how these platforms support not just accurate risk quantification but also strategic differentiation in a highly competitive insurance market. The discussion will also look ahead to emerging capabilities such as continuous risk reassessment, predictive breach modeling, and collaboration with managed service providers illustrating how cyber insurance can evolve from a safety net into a core component of enterprise resilience strategies.
This talk is designed for insurance leaders, cybersecurity professionals, and digital transformation strategists who want to understand and leverage the next generation of data-driven cyber underwriting.
AI-Powered Risk Scorecards: Redefining Cyber Insurance
Cyber insurance is being reshaped as AI-driven risk scorecards replace outdated, questionnaire-based underwriting models. Built with Small and Medium Enterprises (SMEs) in mind, these intelligent platforms combine data from vulnerabilities, breach histories, and behavioral analytics to deliver dynamic risk profiles. The result is a more accurate understanding of exposure, enabling insurers to offer fairer policies and providing SMEs with actionable insights to strengthen their cyber resilience.
Unlike traditional security tools, these scorecards are designed specifically for insurance. They integrate seamlessly with underwriting workflows, produce actuarial-ready outputs, and automate compliance documentation. Beyond risk assessment, they provide tailored remediation guidance and regulatory mapping, positioning insurers as proactive partners in risk management rather than passive providers of coverage.
This session will explore the data architecture and AI technologies behind cyber risk scorecards, showing how they deliver scalability, precision, and trust. Real-world examples will demonstrate how insurers are applying these tools to enhance efficiency, improve decision-making, and build stronger client relationships.
Looking ahead, we will also consider emerging innovations such as continuous risk reassessment, predictive breach modeling, and partnerships with managed service providers. These developments point toward a future where cyber insurance evolves into an integrated component of enterprise resilience strategies.
Attendees, including technology leaders, cybersecurity professionals, and digital transformation strategists, will gain a clear understanding of how AI-powered platforms are transforming the landscape of cyber insurance and setting the stage for the next generation of risk management.
AI-Powered Insurance Automation with Azure: Scalable, Secure, and Measurable
Artificial Intelligence is driving enterprise transformation—and nowhere is this more impactful than in the insurance sector, where operations are data-intensive, highly regulated, and cost-sensitive. In this session, we explore how insurers are using Microsoft Azure to build scalable, intelligent automation frameworks that optimize underwriting, claims processing, and customer engagement.
Using production examples, we’ll walk through a modular AI architecture that integrates Azure Machine Learning, Cognitive Services, Logic Apps, and Azure Functions to create intelligent workflows. These systems process thousands of policy applications per hour, triage 85% of claims on first attempt, and reduce decision cycles by over 50%—all while maintaining sub-second predictive response times and near-perfect data accuracy.
The session will showcase patterns for designing secure, compliant, and high-throughput AI pipelines on Azure. You’ll learn how insurers use Azure Synapse Analytics for real-time insights, implement secure model hosting with ML endpoints, and build continuous feedback loops using Azure Monitor and Application Insights. We’ll also cover approaches to integrating AI into existing .NET- and API-driven backends for seamless enterprise-wide adoption.
For teams navigating governance and data protection, we’ll touch on aligning these systems with GDPR, FCA guidelines, and best practices for explainable AI and audit trails—ensuring your cloud-native AI remains both compliant and transparent.
Ideal for Azure architects, cloud engineers, and data professionals, this session provides a practical, referenceable blueprint for delivering AI-powered transformation in real-world environments. Attendees will leave with actionable insights into deploying scalable, intelligent, and resilient automation frameworks using Microsoft’s cloud-native ecosystem.
AI-Powered Risk Scorecards: The Future of Cyber Insurance
The world of cyber insurance is changing rapidly as AI-powered risk scorecards replace outdated, questionnaire-based underwriting models. These platforms, tailored for Small and Medium Enterprises (SMEs), aggregate diverse data sources ranging from real-time vulnerabilities and breach histories to behavioral analytics to build dynamic, multidimensional risk profiles. This enables insurers to better align coverage with actual exposure while empowering SMEs with fairer terms and actionable insights into their security posture.
Unlike traditional security tools, these solutions are designed specifically for the insurance industry. They integrate seamlessly with underwriting workflows, generate actuarial-ready outputs, and simplify compliance reporting. By embedding remediation guidance and regulatory mapping, they help insurers evolve from passive risk bearers into proactive partners in enterprise security and resilience.
This session explores the architecture and design principles behind AI-driven cyber risk scorecards, emphasizing how intelligent systems and modern platforms make them scalable and reliable. Real-world examples will highlight how insurers are leveraging these tools to improve underwriting processes, strengthen client trust, and create opportunities for innovation in risk management.
Looking ahead, emerging capabilities such as continuous risk reassessment, predictive breach modeling, and collaboration with managed service providers will shape a future where cyber insurance becomes an active component of enterprise strategy rather than a reactive safeguard.
Attendees including insurance executives, cybersecurity specialists, and digital transformation leaders, will leave with a clear understanding of how AI, data, and modern platforms are redefining the next generation of cyber underwriting.
AI-Powered Insurance Automation on Azure: Real-World Impact with Scalable Cloud Architectures
Discover how enterprise insurers are leveraging Azure’s AI and automation capabilities to transform operations across underwriting, claims, and customer support. This session presents a modular, cloud-native architecture using Azure Machine Learning, Cognitive Services, Azure Logic Apps, and Synapse Analytics to drive real-time performance and decision automation.
Learn how insurers achieved over 60% improvements in fraud detection, 70% task automation, and sub-second predictive response times using Azure-based AI workflows. With case-based insights, we’ll cover best practices for model deployment, secure integration, and monitoring in regulated environments. Attendees will leave with a blueprint for implementing scalable, intelligent, and secure AI systems on Azure.
AI-Powered Cyber Risk Scorecards: Transforming Insurance for SMEs
Cyber insurance is evolving rapidly as insurers move beyond static, questionnaire-based underwriting toward dynamic, AI-powered risk scorecards tailored to Small and Medium Enterprises (SMEs). These platforms synthesize diverse data sources such as network vulnerabilities, breach histories, and behavioral analytics into multidimensional profiles that provide insurers with a more accurate understanding of risk exposure. By doing so, they improve underwriting efficiency, streamline operations, and strengthen customer relationships.
Unlike general cybersecurity tools, these solutions are designed specifically for insurance. They integrate seamlessly into underwriting workflows, generate actuarial-ready outputs, and automate compliance documentation. Their ability to segment risk precisely enables insurers to align premiums with actual exposure, offering SMEs fairer terms while equipping them with actionable insights into their security posture. In addition, built-in remediation guidance and regulatory compliance mapping reposition insurers as proactive partners in risk management rather than passive policy issuers.
This session will explore the architecture of AI-driven cyber risk scorecards and highlight real-world case studies that demonstrate measurable business impact. Attendees will gain practical insights into how these platforms enhance both risk quantification and competitive differentiation in an increasingly complex insurance market. Looking ahead, emerging capabilities such as continuous risk reassessment, predictive breach modeling, and ecosystem partnerships with managed service providers point to a future where cyber insurance becomes an integral part of enterprise risk strategy.
This talk will be especially valuable for insurance executives, cybersecurity leaders, and digital transformation strategists seeking a practical roadmap to lead in the next era of cyber underwriting.
AI-Powered Cyber Risk Scorecards: Transforming Underwriting and Risk Management for SMEs
The cyber insurance industry is entering a new era, with AI-powered cyber risk scorecards redefining how insurers assess, price, and manage exposure, particularly for Small and Medium Enterprises (SMEs). Unlike static, questionnaire-based methods, these platforms integrate diverse data streams real-time vulnerability scanning, breach histories, and behavioral analytics to produce dynamic, multidimensional risk profiles. By enabling precise segmentation and actuarial-ready outputs, they not only improve underwriting efficiency but also deliver measurable business outcomes, including faster policy issuance, better loss ratios, and stronger client retention.
Purpose-built for insurance, these platforms offer seamless integration with underwriting workflows, automated compliance documentation, and tailored remediation insights. This empowers insurers to provide fairer pricing while equipping SMEs with actionable strategies to strengthen their cyber resilience. As insurers evolve from passive risk carriers to proactive partners, they gain a distinct advantage in an increasingly competitive and regulation-driven market.
This session will unpack the technology architecture behind AI-driven scorecards and share real-world case studies that highlight their strategic impact. Attendees will learn how emerging capabilities such as predictive breach modeling, continuous risk reassessment, and ecosystem partnerships with managed service providers are positioning cyber insurance as a core element of enterprise risk strategy rather than a reactive financial safeguard.
AI-Driven Risk Scorecards: Transforming Cyber Insurance
Cyber insurance is undergoing a major shift as AI-powered risk scorecards replace traditional, questionnaire-based underwriting. Designed with Small and Medium Enterprises (SMEs) in mind, these platforms bring together diverse data sources such as real-time vulnerabilities, breach histories, and behavioral analytics to generate dynamic, multidimensional risk profiles. The result is more accurate underwriting, better alignment between exposure and premiums, and clearer insights for SMEs into their own cyber resilience.
Unlike general-purpose cybersecurity tools, these solutions are built specifically for the insurance industry. They integrate directly with underwriting workflows, generate actuarial-ready outputs, and streamline compliance documentation. By providing actionable recommendations alongside risk assessments, they empower insurers to move beyond risk transfer and become proactive partners in enterprise security and resilience.
This session will examine the architecture and data foundations behind AI-driven cyber risk scorecards, highlighting how cloud platforms and scalable AI models enable their impact. Real-world examples will illustrate how insurers are using these solutions to improve decision-making, strengthen client trust, and unlock new opportunities for innovation.
Looking ahead, we will explore emerging capabilities such as continuous risk reassessment, predictive breach modeling, and collaborations with managed service providers. These developments point toward a future where cyber insurance is not just a financial product but an integrated part of an enterprise’s risk strategy.
Attendees including insurance leaders, cybersecurity professionals, and data-driven strategists, will leave with practical insights into how AI and cloud technologies are redefining the future of cyber insurance.
AI-Driven Risk Scorecards: Transforming Cyber Insurance with Data
Cyber insurance is evolving as AI-powered risk scorecards replace traditional, questionnaire-based underwriting. Designed for Small and Medium Enterprises (SMEs), these platforms bring together diverse data sources such as vulnerabilities, breach histories, and behavioral insights, to build dynamic risk profiles that reflect real exposure. This shift enables insurers to offer fairer policies, while giving SMEs actionable insights to strengthen their cyber resilience.
Unlike general security tools, these scorecards are created specifically for the insurance industry. They integrate smoothly with underwriting workflows, generate actuarial-ready outputs, and simplify compliance documentation. Beyond risk quantification, they provide remediation guidance and regulatory mapping, helping insurers move from passive policy providers to proactive partners in risk management.
This session explores the data and architecture underpinning AI-driven cyber risk scorecards, focusing on how modern data platforms and cloud-native technologies make them scalable and effective. Real-world examples will demonstrate how insurers are using these systems to streamline operations, improve client trust, and unlock new business value.
Looking ahead, innovations such as continuous risk reassessment, predictive breach modeling, and partnerships with managed service providers illustrate a future where cyber insurance is not just financial protection but a core element of enterprise resilience.
Attendees, including data professionals, cybersecurity specialists, and digital transformation leaders, will gain a practical understanding of how AI and modern data platforms are redefining cyber underwriting and risk management, and how these advancements align with broader trends in cloud, data, and analytics.
Chetan Prakash Ratnawat
Madhav Institute of Technology and Science, Jiwaji University
Buffalo Grove, Illinois, United States
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