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
See AI agent for Reserving Actuaries in action
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See AI agent for Reserving Actuaries in action
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See AI agent for Reserving Actuaries in action
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Q4 2024 IBNR Reserve Analysis
See AI agent for Reserving Actuaries in action
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Time comparison
Time comparison
Traditional way
2-3 weeks per quarter
With V7 Go agents
4-6 hours
Average time saved
90%
Why V7 Go
Why V7 Go
Automated Claims Triangle Processing
Extracts and validates claims data from loss runs, policy systems, and historical triangles, automatically organizing data by accident year, development period, and line of business for immediate analysis.
Automated Claims Triangle Processing
Extracts and validates claims data from loss runs, policy systems, and historical triangles, automatically organizing data by accident year, development period, and line of business for immediate analysis.
Automated Claims Triangle Processing
Extracts and validates claims data from loss runs, policy systems, and historical triangles, automatically organizing data by accident year, development period, and line of business for immediate analysis.
Automated Claims Triangle Processing
Extracts and validates claims data from loss runs, policy systems, and historical triangles, automatically organizing data by accident year, development period, and line of business for immediate analysis.
Loss Development Pattern Recognition
Identifies historical development patterns using chain ladder, Bornhuetter-Ferguson, and expected loss ratio methods, calculating age-to-age factors and tail factors with statistical confidence intervals.
Loss Development Pattern Recognition
Identifies historical development patterns using chain ladder, Bornhuetter-Ferguson, and expected loss ratio methods, calculating age-to-age factors and tail factors with statistical confidence intervals.
Loss Development Pattern Recognition
Identifies historical development patterns using chain ladder, Bornhuetter-Ferguson, and expected loss ratio methods, calculating age-to-age factors and tail factors with statistical confidence intervals.
Loss Development Pattern Recognition
Identifies historical development patterns using chain ladder, Bornhuetter-Ferguson, and expected loss ratio methods, calculating age-to-age factors and tail factors with statistical confidence intervals.
Multi-Method Reserve Calculation
Applies multiple actuarial methods simultaneously, comparing results across chain ladder, BF, and Cape Cod approaches to provide a comprehensive view of reserve adequacy and methodology sensitivity.
Multi-Method Reserve Calculation
Applies multiple actuarial methods simultaneously, comparing results across chain ladder, BF, and Cape Cod approaches to provide a comprehensive view of reserve adequacy and methodology sensitivity.
Multi-Method Reserve Calculation
Applies multiple actuarial methods simultaneously, comparing results across chain ladder, BF, and Cape Cod approaches to provide a comprehensive view of reserve adequacy and methodology sensitivity.
Multi-Method Reserve Calculation
Applies multiple actuarial methods simultaneously, comparing results across chain ladder, BF, and Cape Cod approaches to provide a comprehensive view of reserve adequacy and methodology sensitivity.
Assumption Validation and Testing
Tests key assumptions by analyzing historical accuracy, identifying outliers in development patterns, and flagging potential issues with selected loss ratios or development factors before final reserve selection.
Assumption Validation and Testing
Tests key assumptions by analyzing historical accuracy, identifying outliers in development patterns, and flagging potential issues with selected loss ratios or development factors before final reserve selection.
Assumption Validation and Testing
Tests key assumptions by analyzing historical accuracy, identifying outliers in development patterns, and flagging potential issues with selected loss ratios or development factors before final reserve selection.
Assumption Validation and Testing
Tests key assumptions by analyzing historical accuracy, identifying outliers in development patterns, and flagging potential issues with selected loss ratios or development factors before final reserve selection.
Regulatory Documentation Generation
Creates audit-ready documentation of reserve calculations, including methodology descriptions, assumption justifications, and sensitivity analyses required for regulatory filings and internal governance.
Regulatory Documentation Generation
Creates audit-ready documentation of reserve calculations, including methodology descriptions, assumption justifications, and sensitivity analyses required for regulatory filings and internal governance.
Regulatory Documentation Generation
Creates audit-ready documentation of reserve calculations, including methodology descriptions, assumption justifications, and sensitivity analyses required for regulatory filings and internal governance.
Regulatory Documentation Generation
Creates audit-ready documentation of reserve calculations, including methodology descriptions, assumption justifications, and sensitivity analyses required for regulatory filings and internal governance.
Cited and Verifiable Calculations
Links every reserve estimate back to the specific claims data, development factors, and assumptions used in the calculation, providing complete transparency and auditability for actuarial review and regulatory examination.
Cited and Verifiable Calculations
Links every reserve estimate back to the specific claims data, development factors, and assumptions used in the calculation, providing complete transparency and auditability for actuarial review and regulatory examination.
Cited and Verifiable Calculations
Links every reserve estimate back to the specific claims data, development factors, and assumptions used in the calculation, providing complete transparency and auditability for actuarial review and regulatory examination.
Cited and Verifiable Calculations
Links every reserve estimate back to the specific claims data, development factors, and assumptions used in the calculation, providing complete transparency and auditability for actuarial review and regulatory examination.
Analyzes all reserve data sources
To deliver comprehensive IBNR estimates.
Get started
Get started
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
Customer voices
Customer voices
Connect AI to your actuarial methods.
Connect AI to your actuarial methods.
Turn claims data into confident reserve decisions faster.
Turn claims data into confident reserve decisions faster.
Finance
•
Legal
•
Insurance
•
Tax
•
Real Estate
Finance
•
Legal
•
Insurance
•
Tax
•
Real Estate
Finance
•
Legal
•
Insurance
•
Tax
•
Real Estate
Customer Voices
Industrial equipment sales
We are looking for V7 Go and AI in general to be the beating heart of our company and our growth. It will make us more productive as a company, liaising with customers, automating tasks, even finding new work.
Read the full story
Industrial equipment sales
We are looking for V7 Go and AI in general to be the beating heart of our company and our growth. It will make us more productive as a company, liaising with customers, automating tasks, even finding new work.
Read the full story
Insurance
We have six assessors. Before V7 Go, each would process around 15 claims a day, about 90 in total. With V7 Go, we’re expecting that to rise to around 20 claims per assessor, which adds up to an extra 30 claims a day. That’s the equivalent of two additional full-time assessors. Beyond the cost savings, there’s real reputational gains from fewer errors and faster turnaround times.
Read the full story
Insurance
We have six assessors. Before V7 Go, each would process around 15 claims a day, about 90 in total. With V7 Go, we’re expecting that to rise to around 20 claims per assessor, which adds up to an extra 30 claims a day. That’s the equivalent of two additional full-time assessors. Beyond the cost savings, there’s real reputational gains from fewer errors and faster turnaround times.
Read the full story
Real Estate
Prior to V7, people using the software were manually inputting data. Now it’s so much faster because it just reads it for them. On average, it saves our customers 45 minutes to an hour of work, and it’s more accurate.
Read the full story
Real Estate
Prior to V7, people using the software were manually inputting data. Now it’s so much faster because it just reads it for them. On average, it saves our customers 45 minutes to an hour of work, and it’s more accurate.
Read the full story
Industrial equipment sales
We are looking for V7 Go and AI in general to be the beating heart of our company and our growth. It will make us more productive as a company, liaising with customers, automating tasks, even finding new work.
Read the full story
Insurance
We have six assessors. Before V7 Go, each would process around 15 claims a day, about 90 in total. With V7 Go, we’re expecting that to rise to around 20 claims per assessor, which adds up to an extra 30 claims a day. That’s the equivalent of two additional full-time assessors. Beyond the cost savings, there’s real reputational gains from fewer errors and faster turnaround times.
Read the full story
Real Estate
Prior to V7, people using the software were manually inputting data. Now it’s so much faster because it just reads it for them. On average, it saves our customers 45 minutes to an hour of work, and it’s more accurate.
Read the full story
Finance
“Whenever I think about hiring, I first try to do it in V7 Go.” Discover how HITICCO uses V7 Go agents to accelerate and enrich their prospect research.
Read the full story
Finance
The experience with V7 has been fantastic. Very customized level of support. You feel like they really care about your outcome and objectives.
Read the full story
Features
Features
Results you can actually trust.
Reliable AI document processing toolkit.
Results you can trust.
Trustworthy AI document processing toolkit.
Supporting complex data formats.
From any source system.
Claims data comes in countless formats. This agent handles everything from standard loss triangles in Excel to raw claims extracts from legacy policy systems, automatically identifying accident years and development periods regardless of structure.
Input types
Multiple Data Sources
Complex Triangles
Historical Data
Multi-Segment
Document types
Excel Models
Database Extracts
Complex Tables
Time Series
CSV Files
Vendor_US.xlsx
3
Supply_2023.pptx
Review_Legal.pdf
Supporting complex data formats.
From any source system.
Claims data comes in countless formats. This agent handles everything from standard loss triangles in Excel to raw claims extracts from legacy policy systems, automatically identifying accident years and development periods regardless of structure.
Input types
Multiple Data Sources
Complex Triangles
Historical Data
Multi-Segment
Document types
Excel Models
Database Extracts
Complex Tables
Time Series
CSV Files
Vendor_US.xlsx
3
Supply_2023.pptx
Review_Legal.pdf
Supporting complex data formats.
From any source system.
Claims data comes in countless formats. This agent handles everything from standard loss triangles in Excel to raw claims extracts from legacy policy systems, automatically identifying accident years and development periods regardless of structure.
Input types
Multiple Data Sources
Complex Triangles
Historical Data
Multi-Segment
Document types
Excel Models
Database Extracts
Complex Tables
Time Series
CSV Files
Vendor_US.xlsx
3
Supply_2023.pptx
Review_Legal.pdf
Reach 99% accuracy rate
through GenAI reasoning.
Reserve calculations demand precision. The agent uses sophisticated pattern recognition and statistical validation to ensure development factors, loss ratios, and ultimate loss estimates are calculated with actuarial-grade accuracy, ready for regulatory filing.
Model providers
Security note
V7 never trains models on your private data. We keep your data encrypted and allow you to deploy your own models.
Answer
Type
Text
Tool
o4 Mini
Reasoning effort
Min
Low
Mid
High
AI Citations
Inputs
Set a prompt (Press @ to mention an input)
Reach 99% accuracy rate
through GenAI reasoning.
Reserve calculations demand precision. The agent uses sophisticated pattern recognition and statistical validation to ensure development factors, loss ratios, and ultimate loss estimates are calculated with actuarial-grade accuracy, ready for regulatory filing.
Model providers
Security note
V7 never trains models on your private data. We keep your data encrypted and allow you to deploy your own models.
Answer
Type
Text
Tool
o4 Mini
Reasoning effort
Min
Low
Mid
High
AI Citations
Inputs
Set a prompt (Press @ to mention an input)
Reach 99% accuracy rate
through GenAI reasoning.
Reserve calculations demand precision. The agent uses sophisticated pattern recognition and statistical validation to ensure development factors, loss ratios, and ultimate loss estimates are calculated with actuarial-grade accuracy, ready for regulatory filing.
Model providers
Security note
V7 never trains models on your private data. We keep your data encrypted and allow you to deploy your own models.
Answer
Type
Text
Tool
o4 Mini
Reasoning effort
Min
Low
Mid
High
AI Citations
Inputs
Set a prompt (Press @ to mention an input)
Trustworthy results,
grounded in reality.
Verify every reserve estimate with confidence. Each calculation, development factor, and assumption is visually linked to the specific claims data and historical patterns used, providing complete transparency for actuarial review and regulatory examination.
Visual grounding in action
00:54
Deliberate Misrepresentation: During the trial, evidence was presented showing that John Doe deliberately misrepresented his income on multiple occasions over several years. This included falsifying documents, underreporting income, and inflating deductions to lower his tax liability. Such deliberate deception demonstrates intent to evade taxes.
Pattern of Behavior: The prosecution demonstrated a consistent pattern of behavior by John Doe, spanning several years, wherein he consistently failed to report substantial portions of his income. This pattern suggested a systematic attempt to evade taxes rather than mere oversight or misunderstanding.
Concealment of Assets: Forensic accounting revealed that John Doe had taken significant steps to conceal his assets offshore, including setting up shell companies and using complex financial structures to hide income from tax authorities. Such elaborate schemes indicate a deliberate effort to evade taxes and avoid detection.
Failure to Cooperate: Throughout the investigation and trial, John Doe displayed a lack of cooperation with tax authorities. He refused to provide requested documentation, obstructed the audit process, and failed to disclose relevant financial information. This obstructionism further supported the prosecution's argument of intentional tax evasion.
Prior Warning and Ignoring Compliance
![](data:image/svg+xml,<svg display="block" role="presentation" viewBox="0 0 198 15" xmlns="http://www.w3.org/2000/svg"><defs ><clipPath id="J0QEw1Ctk-2037569988-stroke"><use href="%23J0QEw1Ctk-2037569988"/></clipPath><path d="M 0 1 C 0 0.448 0.448 0 1 0 L 197 0 C 197.552 0 198 0.448 198 1 L 198 9 C 198 9.552 197.552 10 197 10 L 76 10 L 76 14 C 76 14.552 75.552 15 75 15 L 1 15 C 0.448 15 0 14.552 0 14 Z" id="J0QEw1Ctk-2037569988"/></defs><use fill="rgba(124, 58, 237, 0.1)" height="15px" href="%23J0QEw1Ctk-2037569988" id="J0QEw1Ctk" width="198px"/><use clip-path="url(%23J0QEw1Ctk-2037569988-stroke)" fill="transparent" href="%23J0QEw1Ctk-2037569988" stroke-dasharray="0" stroke-linecap="butt" stroke-linejoin="miter" stroke-miterlimit="4" stroke-width="1" stroke="rgb(124, 58, 237)"/></svg>)
02
01
01
02
Trustworthy results,
grounded in reality.
Verify every reserve estimate with confidence. Each calculation, development factor, and assumption is visually linked to the specific claims data and historical patterns used, providing complete transparency for actuarial review and regulatory examination.
Visual grounding in action
00:54
Deliberate Misrepresentation: During the trial, evidence was presented showing that John Doe deliberately misrepresented his income on multiple occasions over several years. This included falsifying documents, underreporting income, and inflating deductions to lower his tax liability. Such deliberate deception demonstrates intent to evade taxes.
Pattern of Behavior: The prosecution demonstrated a consistent pattern of behavior by John Doe, spanning several years, wherein he consistently failed to report substantial portions of his income. This pattern suggested a systematic attempt to evade taxes rather than mere oversight or misunderstanding.
Concealment of Assets: Forensic accounting revealed that John Doe had taken significant steps to conceal his assets offshore, including setting up shell companies and using complex financial structures to hide income from tax authorities. Such elaborate schemes indicate a deliberate effort to evade taxes and avoid detection.
Failure to Cooperate: Throughout the investigation and trial, John Doe displayed a lack of cooperation with tax authorities. He refused to provide requested documentation, obstructed the audit process, and failed to disclose relevant financial information. This obstructionism further supported the prosecution's argument of intentional tax evasion.
Prior Warning and Ignoring Compliance
02
01
01
02
Trustworthy results,
grounded in reality.
Verify every reserve estimate with confidence. Each calculation, development factor, and assumption is visually linked to the specific claims data and historical patterns used, providing complete transparency for actuarial review and regulatory examination.
Visual grounding in action
00:54
Deliberate Misrepresentation: During the trial, evidence was presented showing that John Doe deliberately misrepresented his income on multiple occasions over several years. This included falsifying documents, underreporting income, and inflating deductions to lower his tax liability. Such deliberate deception demonstrates intent to evade taxes.
Pattern of Behavior: The prosecution demonstrated a consistent pattern of behavior by John Doe, spanning several years, wherein he consistently failed to report substantial portions of his income. This pattern suggested a systematic attempt to evade taxes rather than mere oversight or misunderstanding.
Concealment of Assets: Forensic accounting revealed that John Doe had taken significant steps to conceal his assets offshore, including setting up shell companies and using complex financial structures to hide income from tax authorities. Such elaborate schemes indicate a deliberate effort to evade taxes and avoid detection.
Failure to Cooperate: Throughout the investigation and trial, John Doe displayed a lack of cooperation with tax authorities. He refused to provide requested documentation, obstructed the audit process, and failed to disclose relevant financial information. This obstructionism further supported the prosecution's argument of intentional tax evasion.
Prior Warning and Ignoring Compliance
02
01
01
02
Enterprise grade security
for high-stake industries.
Claims data contains highly sensitive policyholder information and proprietary loss experience. V7 Go processes all reserve calculations within your secure environment, ensuring confidentiality and compliance with insurance data privacy regulations.
Certifications
GDPR
SOC2
HIPAA
ISO
Safety
Custom storage
Data governance
Access-level permissions
Enterprise grade security
for high-stake industries.
Claims data contains highly sensitive policyholder information and proprietary loss experience. V7 Go processes all reserve calculations within your secure environment, ensuring confidentiality and compliance with insurance data privacy regulations.
Certifications
GDPR
SOC2
HIPAA
ISO
Safety
Custom storage
Data governance
Access-level permissions
Enterprise grade security
for high-stake industries.
Claims data contains highly sensitive policyholder information and proprietary loss experience. V7 Go processes all reserve calculations within your secure environment, ensuring confidentiality and compliance with insurance data privacy regulations.
Certifications
GPDR
SOC2
HIPAA
ISO
Safety
Custom storage
Data governance
Access-level permissions
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Answers
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.
+
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.
+
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.
+
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.
+
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.
+
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.
+
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.
+
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.
+
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.
+
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.
+
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.
+
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.
+
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.
+
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.
+
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.
+
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.
+
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.
+
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.
+
Next steps
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.
Uncover hidden liabilities
in
supplier contracts.
V7 Go transforms documents into strategic assets. 150+ enterprises are already on board:
Uncover hidden liabilities
in
supplier contracts.
V7 Go transforms documents into strategic assets. 150+ enterprises are already on board: