Juliya Sebastian

Analyzed large-scale insurance claims data using SQL and Power BI to detect fraud patterns and anomalies, resulting in a 10–15% reduction in fraudulent claims for clients like Swiss Re and Accenture.
Designed and delivered high-quality labeled datasets to support predictive modeling for claims risk assessment and fraud detection.
Collaborated with cross-functional teams to improve data quality and annotation workflows, ensuring precision and consistency in insurance datasets.
Translated actuarial and operational insights into AI-compatible data structures, enabling machine learning models to better recognize claim irregularities.
Created interactive Power BI dashboards for management reporting, tracking fraud indicators and claims KPIs.
Acted as a liaison between business and technical teams to optimize data pipelines and model training inputs.

Analyzed large-scale insurance claims data using SQL and Power BI to detect fraud patterns and anomalies — resulting in a 10–15% reduction in fraudulent claims for clients like Swiss Re and Accenture., Designed and delivered high-quality labeled datasets to support predictive modeling for claims risk assessment and fraud detection., Collaborated with cross-functional teams to improve data quality and annotation workflows, ensuring precision and consistency in insurance datasets., Translated actuarial and operational insights into AI-compatible data structures, enabling machine learning models to better recognize claim irregularities., Created interactive Power BI dashboards for management reporting, tracking fraud indicators and claims KPIs., Acted as a liaison between business and technical teams to optimize data pipelines and model training inputs.

Processed and analyzed IP litigation datasets, identifying potential copyright and patent risks through data mining and reporting.
Used Excel and internal systems to label, clean, and validate data for machine-readable accuracy, improving dataset reliability by 25%.
Partnered with legal teams to generate 10–15 daily data alerts, helping clients proactively mitigate legal and compliance risks.

Processed and analyzed IP litigation datasets, identifying potential copyright and patent risks through data mining and reporting., Used Excel and internal systems to label, clean, and validate data for machine-readable accuracy — improving dataset reliability by 25%., Partnered with legal teams to generate 10–15 daily data alerts, helping clients proactively mitigate legal and compliance risks.