Empowering insurers with advanced catastrophe risk analytics for a resilient future
Genpact's exposure analytics solution powered by AWS aims to address a critical business problem in commercial insurance by streamlining data management, risk assessment, and underwriting for properties exposed to various perils (earthquakes, floods, and fires.) Fragmented data (structured/unstructured), nonstandard broker submissions, and manual processes lead to inefficiencies, long cycle times, and lost opportunities for carriers.
AI/ML and gen AI based data cleaning, enrichment, policy coding, and results compilation streamline exposure management and reducing time and effort.
Reduce your manual effort by up to
60%
with our smart data cleansing solution
Unified translation and extraction layer
Think of our translation and extraction layer as a wrapper that brings a unified view. It translates documents into English using generative AI and transforms unstructured data into a structured format, allowing extraction of labels and values.
Lowering manual effort for data standardization and cleansing
Our solution addresses the challenge of deriving varied datasets from brokers. We use smart data scrubbing to standardize and cleanse structured and unstructured data, enhancing it for exposure analysis.
End-to-end catastrophe risk and exposure management
Our services encompass the entire process, including:
- Contract, submission, and deal review
- Catastrophe modeling
- Automation
- Deal pricing optimization
- Exposure aggregation
- Portfolio analysis and reporting
- Research and development
- Raters' preparation
Automation all the way
Our solution uses automation to cut overall processing time by up to 40%. From data scrubbing to results compilation, Genpact leverages automation, smart data validation, and rule-based processes to supercharge your modeling and underwriting efforts.
Streamline operations with our innovative and proprietary digital solution
Our solution helped a leading global insurer streamline data standardization and cleansing and enrich geolocation, leading to a 20%–30% reduction in cycle time, a 2%–4% improvement in GWP, and a 40%–50% efficiency in data scrubbing.