90% faster reserve analysis
AI agent for Reserving Actuaries
From weeks of analysis to hours
Delegate the data-intensive work of IBNR reserve estimation to a specialized AI agent. It analyzes historical claims data, identifies loss development patterns, applies chain ladder and Bornhuetter-Ferguson methods, and validates assumptions, allowing actuaries to focus on strategic reserve decisions and regulatory compliance.

Ideal for
Reserving Actuaries
Actuarial Analysts
Chief Actuaries
Time comparison
Traditional way
2-3 weeks per quarter
With V7 Go agents
4-6 hours
Average time saved
90%
Why V7 Go
Analyzes all reserve data sources
To deliver comprehensive IBNR estimates.



Import your files
Microsoft Excel
,
Snowflake
,
Tableau
Import your files from whereever they are currently stored
All types of Insurance documents supported
Once imported our system extracts and organises the essentials
Connect AI to your actuarial methods.
Finance
•
Legal
•
Insurance
•
Tax
•
Real Estate
Answers
What you need to know about our
AI agent for Reserving Actuaries
How does the agent handle different actuarial methods?
The agent applies multiple standard actuarial methods including chain ladder, Bornhuetter-Ferguson, expected loss ratio, and Cape Cod approaches. It calculates reserves using each method and provides comparative analysis, allowing actuaries to select the most appropriate estimate based on data quality and business conditions.
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Can it work with our existing claims data format?
Yes. The agent is designed to handle various claims data formats, from standard loss triangles in Excel to raw claims extracts from policy administration systems. It automatically identifies accident years, development periods, and claim amounts regardless of the source format.
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How does it validate the reasonableness of reserve estimates?
The agent performs multiple validation checks, including comparing current estimates to prior period reserves, analyzing development factor stability, testing sensitivity to key assumptions, and flagging unusual patterns in loss emergence. It also benchmarks results across different actuarial methods to identify potential issues.
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Does it handle tail factor selection?
Yes. The agent analyzes historical development patterns to recommend tail factors, considering both fitted curves and industry benchmarks. It provides multiple tail factor options with supporting analysis, allowing actuaries to make informed selections based on line of business characteristics and claim settlement patterns.
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Can it analyze reserves by different segments?
Absolutely. The agent can segment reserve analysis by line of business, state, coverage type, policy year, or any other dimension present in your claims data. This granular analysis helps identify specific areas requiring reserve strengthening or release.
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How does it support regulatory reporting?
The agent generates comprehensive documentation of reserve calculations, including detailed methodology descriptions, assumption justifications, and sensitivity analyses. This documentation is structured to meet regulatory requirements for Schedule P filings and actuarial opinions, with full audit trails linking estimates to source data.
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Next steps
Still building loss triangles manually every quarter?
Send us your claims data, and we'll show you how our AI agent can deliver IBNR estimates in hours instead of weeks.












