90% faster data prep
AI agent for Actuarial Analysts
From weeks of data prep to hours
Delegate the tedious work of loss data preparation to a specialized AI agent. It cleans historical claims data, standardizes formats, categorizes losses by type and severity, validates data integrity, and delivers model-ready datasets that feed directly into your pricing models.
Ideal for
Actuarial Teams
Pricing Analysts
Reserving Actuaries
See AI agent for Actuarial Analysts in action
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See AI agent for Actuarial Analysts in action
Play video
See AI agent for Actuarial Analysts in action
Play video
Q4 Loss Data Preparation
See AI agent for Actuarial Analysts in action
Play video
Time comparison
Time comparison
Traditional way
2-3 weeks
With V7 Go agents
3-4 hours
Average time saved
90%
Why V7 Go
Why V7 Go
Automated Data Cleaning
Identifies and removes duplicate claims, corrects formatting errors, standardizes date formats, and validates data integrity across thousands of records in minutes.
Automated Data Cleaning
Identifies and removes duplicate claims, corrects formatting errors, standardizes date formats, and validates data integrity across thousands of records in minutes.
Automated Data Cleaning
Identifies and removes duplicate claims, corrects formatting errors, standardizes date formats, and validates data integrity across thousands of records in minutes.
Automated Data Cleaning
Identifies and removes duplicate claims, corrects formatting errors, standardizes date formats, and validates data integrity across thousands of records in minutes.
Intelligent Loss Categorization
Automatically categorizes claims by line of business, coverage type, loss cause, and severity using your firm's classification schema and historical patterns.
Intelligent Loss Categorization
Automatically categorizes claims by line of business, coverage type, loss cause, and severity using your firm's classification schema and historical patterns.
Intelligent Loss Categorization
Automatically categorizes claims by line of business, coverage type, loss cause, and severity using your firm's classification schema and historical patterns.
Intelligent Loss Categorization
Automatically categorizes claims by line of business, coverage type, loss cause, and severity using your firm's classification schema and historical patterns.
Outlier Detection
Flags statistical outliers, unusual claim patterns, and data anomalies that require actuarial judgment, ensuring your models are built on reliable data.
Outlier Detection
Flags statistical outliers, unusual claim patterns, and data anomalies that require actuarial judgment, ensuring your models are built on reliable data.
Outlier Detection
Flags statistical outliers, unusual claim patterns, and data anomalies that require actuarial judgment, ensuring your models are built on reliable data.
Outlier Detection
Flags statistical outliers, unusual claim patterns, and data anomalies that require actuarial judgment, ensuring your models are built on reliable data.
Format Standardization
Converts inconsistent data formats from multiple sources into a single, standardized schema that feeds directly into your actuarial modeling software.
Format Standardization
Converts inconsistent data formats from multiple sources into a single, standardized schema that feeds directly into your actuarial modeling software.
Format Standardization
Converts inconsistent data formats from multiple sources into a single, standardized schema that feeds directly into your actuarial modeling software.
Format Standardization
Converts inconsistent data formats from multiple sources into a single, standardized schema that feeds directly into your actuarial modeling software.
Missing Data Handling
Identifies missing critical fields, applies appropriate imputation methods based on your guidelines, and flags records requiring manual review for completeness.
Missing Data Handling
Identifies missing critical fields, applies appropriate imputation methods based on your guidelines, and flags records requiring manual review for completeness.
Missing Data Handling
Identifies missing critical fields, applies appropriate imputation methods based on your guidelines, and flags records requiring manual review for completeness.
Missing Data Handling
Identifies missing critical fields, applies appropriate imputation methods based on your guidelines, and flags records requiring manual review for completeness.
Audit Trail Documentation
Creates a complete record of all data transformations, cleaning steps, and categorization decisions with citations to source records for regulatory compliance and peer review.
Audit Trail Documentation
Creates a complete record of all data transformations, cleaning steps, and categorization decisions with citations to source records for regulatory compliance and peer review.
Audit Trail Documentation
Creates a complete record of all data transformations, cleaning steps, and categorization decisions with citations to source records for regulatory compliance and peer review.
Audit Trail Documentation
Creates a complete record of all data transformations, cleaning steps, and categorization decisions with citations to source records for regulatory compliance and peer review.
Processes any claims data format
To deliver model-ready datasets.
Get started
Get started
Import your files
Microsoft Excel
,
Snowflake
,
Google Sheets
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 standards.
Connect AI to your actuarial standards.
Turn raw claims data into model-ready datasets automatically.
Turn raw claims data into model-ready datasets automatically.
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 documents.
Up to 200 pages.
Actuarial data comes in countless formats. This agent handles everything from legacy Excel files with inconsistent schemas to scanned loss run reports and multi-tab spreadsheets with embedded calculations.
Input types
50+ languages
Legacy Formats
200 pages
Multi-modal
Document types
PDFs
Complex Tables
Nested Spreadsheets
Scanned Documents
CSV Files
Vendor_US.xlsx
3
Supply_2023.pptx
Review_Legal.pdf
Supporting complex documents.
Up to 200 pages.
Actuarial data comes in countless formats. This agent handles everything from legacy Excel files with inconsistent schemas to scanned loss run reports and multi-tab spreadsheets with embedded calculations.
Input types
50+ languages
Legacy Formats
200 pages
Multi-modal
Document types
PDFs
Complex Tables
Nested Spreadsheets
Scanned Documents
CSV Files
Vendor_US.xlsx
3
Supply_2023.pptx
Review_Legal.pdf
Supporting complex documents.
Up to 200 pages.
Actuarial data comes in countless formats. This agent handles everything from legacy Excel files with inconsistent schemas to scanned loss run reports and multi-tab spreadsheets with embedded calculations.
Input types
50+ languages
Legacy Formats
200 pages
Multi-modal
Document types
PDFs
Complex Tables
Nested Spreadsheets
Scanned Documents
CSV Files
Vendor_US.xlsx
3
Supply_2023.pptx
Review_Legal.pdf
Reach 99% accuracy rate
through GenAI reasoning.
Pricing models demand precision. The agent uses sophisticated validation logic to ensure claim amounts, dates, and categories are extracted with near-perfect accuracy, eliminating the data errors that compromise model reliability.
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.
Pricing models demand precision. The agent uses sophisticated validation logic to ensure claim amounts, dates, and categories are extracted with near-perfect accuracy, eliminating the data errors that compromise model reliability.
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.
Pricing models demand precision. The agent uses sophisticated validation logic to ensure claim amounts, dates, and categories are extracted with near-perfect accuracy, eliminating the data errors that compromise model reliability.
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.
Every data transformation is auditable. Each cleaned record, categorization decision, and outlier flag is linked back to the source data, providing complete transparency for actuarial review and regulatory compliance.
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.
Every data transformation is auditable. Each cleaned record, categorization decision, and outlier flag is linked back to the source data, providing complete transparency for actuarial review and regulatory compliance.
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.
Every data transformation is auditable. Each cleaned record, categorization decision, and outlier flag is linked back to the source data, providing complete transparency for actuarial review and regulatory compliance.
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.
Historical loss data contains sensitive claim information. V7 Go processes all data 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.
Historical loss data contains sensitive claim information. V7 Go processes all data 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.
Historical loss data contains sensitive claim information. V7 Go processes all data 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 Actuarial Analysts
How does the agent handle inconsistent claim data formats?
The agent is designed to process real-world messy data. It recognizes common variations in date formats, currency notations, and claim descriptions, then standardizes them according to your firm's schema. For ambiguous cases, it flags records for manual review rather than making assumptions.
+
How does the agent handle inconsistent claim data formats?
The agent is designed to process real-world messy data. It recognizes common variations in date formats, currency notations, and claim descriptions, then standardizes them according to your firm's schema. For ambiguous cases, it flags records for manual review rather than making assumptions.
+
How does the agent handle inconsistent claim data formats?
The agent is designed to process real-world messy data. It recognizes common variations in date formats, currency notations, and claim descriptions, then standardizes them according to your firm's schema. For ambiguous cases, it flags records for manual review rather than making assumptions.
+
Can it work with our existing actuarial software?
Yes. The agent outputs data in standard formats compatible with major actuarial platforms including Emblem, ResQ, ICRFS, and custom Excel-based models. We configure the output schema to match your specific modeling requirements.
+
Can it work with our existing actuarial software?
Yes. The agent outputs data in standard formats compatible with major actuarial platforms including Emblem, ResQ, ICRFS, and custom Excel-based models. We configure the output schema to match your specific modeling requirements.
+
Can it work with our existing actuarial software?
Yes. The agent outputs data in standard formats compatible with major actuarial platforms including Emblem, ResQ, ICRFS, and custom Excel-based models. We configure the output schema to match your specific modeling requirements.
+
How does it categorize claims by loss type?
The agent uses your firm's classification taxonomy stored in a Knowledge Hub. It analyzes claim descriptions, loss codes, and historical categorization patterns to assign appropriate categories. You can refine the categorization logic based on your specific lines of business.
+
How does it categorize claims by loss type?
The agent uses your firm's classification taxonomy stored in a Knowledge Hub. It analyzes claim descriptions, loss codes, and historical categorization patterns to assign appropriate categories. You can refine the categorization logic based on your specific lines of business.
+
How does it categorize claims by loss type?
The agent uses your firm's classification taxonomy stored in a Knowledge Hub. It analyzes claim descriptions, loss codes, and historical categorization patterns to assign appropriate categories. You can refine the categorization logic based on your specific lines of business.
+
What happens to claims it cannot categorize?
The agent flags any claims it cannot categorize with high confidence for manual review. It provides suggested categories with confidence scores, allowing your actuaries to make the final determination while still saving significant time.
+
What happens to claims it cannot categorize?
The agent flags any claims it cannot categorize with high confidence for manual review. It provides suggested categories with confidence scores, allowing your actuaries to make the final determination while still saving significant time.
+
What happens to claims it cannot categorize?
The agent flags any claims it cannot categorize with high confidence for manual review. It provides suggested categories with confidence scores, allowing your actuaries to make the final determination while still saving significant time.
+
How does it ensure data accuracy for pricing models?
Every data transformation is documented and linked back to the source record. The agent performs validation checks against expected ranges, identifies statistical outliers, and cross-references claims data with policy information to ensure consistency before the data enters your models.
+
How does it ensure data accuracy for pricing models?
Every data transformation is documented and linked back to the source record. The agent performs validation checks against expected ranges, identifies statistical outliers, and cross-references claims data with policy information to ensure consistency before the data enters your models.
+
How does it ensure data accuracy for pricing models?
Every data transformation is documented and linked back to the source record. The agent performs validation checks against expected ranges, identifies statistical outliers, and cross-references claims data with policy information to ensure consistency before the data enters your models.
+
Can it handle multi-year historical data?
Absolutely. The agent processes historical loss data spanning multiple years, accounting for changes in data formats, coding systems, and business practices over time. It normalizes historical data to current standards while preserving the integrity of loss development patterns.
+
Can it handle multi-year historical data?
Absolutely. The agent processes historical loss data spanning multiple years, accounting for changes in data formats, coding systems, and business practices over time. It normalizes historical data to current standards while preserving the integrity of loss development patterns.
+
Can it handle multi-year historical data?
Absolutely. The agent processes historical loss data spanning multiple years, accounting for changes in data formats, coding systems, and business practices over time. It normalizes historical data to current standards while preserving the integrity of loss development patterns.
+
Next steps
Next steps
Still spending weeks preparing data for pricing models?
Send us a sample of your historical claims data. We'll show you how the agent can deliver a cleaned, categorized dataset ready for modeling.
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: