Keerthana Madisetty

Partnered with cross-functional stakeholders to elicit reporting requirements, document business objectives in Confluence, and translate them into actionable user stories in JIRA, ensuring smooth delivery of the Liberty Claims360 analytics platform. • Extracted, cleansed, and consolidated claims, policy, and fraud datasets using SQL and Excel, leveraging AWS S3 for centralized data storage and AWS Glue for data preparation to enhance claims analysis accuracy and operational decision-making. • Designed and delivered dynamic Power BI dashboards for claims performance, fraud detection, and settlement trends, integrating with AWS Redshift to enable real-time analytics and improving insights visibility by 40%. • Applied predictive analytics using Python and AWS SageMaker to detect fraudulent claims early and collaborated with operations teams to streamline claims workflows, reducing manual review dependencies and improving fraud detection efficiency by 22%. • Collaborated with data engineering, underwriting, and claims operations teams to optimize the Liberty Claims360 data pipeline using AWS Glue, Power BI, and SQL to automate workflows and accelerate claims settlement cycles by 33%.

Designed Power BI & Tableau dashboards to track claims, fraud patterns, and credit exposure. Built predictive risk scoring models in Python, improving fraud/loan detection accuracy by 23%. Automated regulatory reporting with SAP BusinessObjects, reducing reporting time significantly. Improved loan approval accuracy by 17% through data-driven insights.