AI In Insurance:
A Practical Guide For Leaders

An AI hallucination occurs when a model generates plausible but incorrect or fabricated information.

This is a key risk in insurance applications, where inaccurate outputs can lead to compliance issues or financial exposure — and why grounding AI in real data is critical.

Explainability refers to the ability to understand and interpret how an AI system makes its decisions or outputs.

In regulated industries like insurance, explainability is essential for audit trails, regulatory compliance, and internal trust in AI-enabled workflows.

Brisc includes built-in explainability, allowing users to see why an agent flagged a submission, routed a claim, or validated a specific field.

Human-in-the-loop involves keeping a person in the decision-making process, typically to review, approve, or override an AI-generated output when needed.

In insurance, this ensures that outputs are safe, auditable, and aligned with human expertise — especially in high-stakes workflows like claims triage or underwriting exceptions. 

Brisc is built with HITL by design: it flags exceptions and anomalies for expert review, learns continuously from human refinements, and evolves to reflect your organization’s unique logic over time.

Large Language Models are powerful AI models trained on massive datasets to understand and generate human language.

LLMs power tools like ChatGPT, Gemini and Claude, and are increasingly being used in insurance to answer questions, extract insights, and process complex documents.

Machine learning is a subset of AI that enables systems to learn from data and improve their performance over time without being explicitly programmed.

For insurers, this powers models that can assess underwriting risk, detect claims anomalies, or prioritize high-value submissions based on historical trends.

Natural Language Processing is a branch of AI that enables machines to understand, interpret, and generate human language.

Brisc uses NLP to extract key fields from freeform submissions, identify missing information in bordereaux files, and interpret nuanced claims narratives, turning unstructured text into actionable data.

Structured data refers to clearly organized information like spreadsheets or databases, while unstructured data includes free-form content like PDFs, emails, and scanned forms.

Brisc processes both — extracting and validating data from submissions, bordereaux, and claims documents that were previously siloed and transforming it into insights readily available to teams.

Vertical AI is artificial intelligence built specifically for a particular industry, using domain-specific data, workflows, and compliance logic.

Brisc is an example of Vertical AI purpose-built for the insurance sector, trained on the language, documents, and operational needs unique to analysts, underwriters, and operations teams.

Agentic AI refers to AI systems designed to act autonomously toward specific goals within a set of boundaries, often executing multi-step tasks without human intervention.

With Brisc, agentic AI powers domain-trained agents that manage end-to-end workflows — such as processing a submission, validating data, and routing it for quote-readiness — while surfacing exceptions for human review and learning continuously from expert feedback.

Data extraction is the identification and capture of key information from structured or unstructured sources.

In workflows like underwriting or claims intake, this includes pulling values like limits, deductibles, coverage types, or named insureds from supporting documents.

Data validation involves checking that extracted data is complete, properly formatted, and logically consistent.

AI-enabled validation helps flag missing fields, detect mismatches, or enforce business rules before data is passed along to downstream systems.

The process of reading and parsing documents across various file types — including PDFs, Word files, Excel sheets, and scans.

In insurance, this enables AI systems to process ACORD forms, bordereaux files, or claims submissions directly from their original formats.

Exception handling is the ability of AI systems to identify cases that fall outside of normal patterns or rules and route them for expert review.

Brisc, for example, automatically flags incomplete submissions or bordereaux discrepancies, prompting human intervention where needed.

Feedback loops enable AI systems to improve over time by learning from corrections, overrides, or human-provided inputs.

In practice, this helps refine extraction accuracy, reduce false positives in validations, and better align with how insurance teams actually work.

Operational AI refers to applied AI systems that are embedded into day-to-day business processes to carry out tasks with speed, consistency, and minimal oversight.

Unlike research-focused or experimental AI, operational AI in insurance must integrate with existing workflow, handle edge cases, and produce auditable outputs.

Workflow automation uses AI to streamline multi-step, rule-based processes and reduce manual effort.

In insurance, this includes automating everything from submission intake to claims document classification, helping teams move faster while reducing repetitive work.

Bordereaux reconciliation is the process of validating reported data against expected formats, rules, or historical norms.

AI supports this by identifying mismatches, highlighting anomalies, and flagging incomplete or inconsistent records for review.

Claims segmentation is the practice of grouping claims by complexity, severity, or type to streamline handling.

AI enables this by identifying patterns in structured and unstructured data to triage routine claims and escalate those that require expert oversight

Claims triage refers to the classification and prioritization of claims at intake.

AI supports this by assessing completeness, identifying missing documents, and directing cases to the appropriate handler or system. 

Exception flagging is the identification of inputs or cases that fall outside of defined rules or patterns.

While handled as a system capability in Brisc, it also serves as a key workflow outcome, surfacing the cases that require human attention.

Policy review automation uses AI to scan, interpret, and compare insurance policies or endorsements for completeness, consistency, and risk exposure.

This enables underwriters and analysts to focus on exceptions and strategic decisions rather than manual document reviews.

Quote readiness refers to the process of determining whether a submission contains the necessary information to generate a quote.

AI can evaluate completeness, extract key fields, and flag gaps — helping underwriters prioritize submissions that are ready to move forward. Brisc’s intake tools support quote-readiness out of the box.

Straight-through processing is the ability to move a task or transaction from intake to output with no manual intervention.

In insurance, AI enables STP by validating documents, extracting data, and routing decisions in real-time, with oversight and exception handling in place for outliers. Brisc’s platform enables STP while preserving compliance and control.

Submission triage refers to evaluating and prioritizing inbound submissions based on business rules such as appetite, geography, or completeness.

AI-powered submissions triage enables organizations to increase intake capacity and underwriters to prioritize high-potential opportunities and accelerate time to quote.

Underwriting intake automation refers to the use of AI to process submissions, extract relevant data, and determine next steps.

It enables faster triage, quote readiness assessments, and streamlined handoff to underwriting teams to reduce cycle time and improve throughput.

AI-driven decision support uses intelligent systems to augment — not replace — human judgment and expertise. AI is employed to help teams make faster, more informed decisions by surfacing insights, identifying patterns, and reducing time spent on manual analysis.

With Brisc Insights, teams can query operational data in plain language, detect anomalies, monitor key trends, and inform decisions across underwriting, claims, and finance, all grounded in their own data and context.

AI governance involves setting policies, standards, and oversight mechanisms to ensure responsible and compliant AI use.

This is critical in regulated sectors like insurance, where auditability and risk management are essential. Brisc Insights supports governance by offering full traceability, explainability, and decision logs.

AI maturity describes how advanced an organization is in adopting and operationalizing AI, from early experimentation to fully embedded, high-impact systems. Insurance teams move along this curve as they progress from basic automation to end-to-end workflows powered by AI.

Brisc accelerates AI maturity by providing domain-trained agents tailored to insurance operations, enabling organizations to realize ROI quickly without needing to build custom models or workflows from scratch.

AI strategy refers to how an organization plans, aligns, and scales AI adoption across teams and functions.

In insurance, this includes choosing the right use cases, integrating AI with existing systems, and aligning with business outcomes. Brisc helps clients move from proof-of-concept to production through domain-focused deployment.

Change management in AI adoption means minimizing disruption while driving adoption. The challenge isn’t just technology; it’s aligning people, processes, and confidence in new ways of working.

Brisc is built to facilitate this: intuitive interfaces reduce training time, human-in-the-loop workflows preserve oversight, and automation mirrors how insurance teams already operate, making rollout smoother and adoption stickier.

Data readiness refers to how well an organization’s data is prepared for use in AI systems. This includes structure, cleanliness, accessibility, and consistency across formats and sources. Without strong data readiness, even the best models can produce poor outputs.

Brisc improves data readiness through advanced ingestion, extraction, and validation capabilities, turning both structured and unstructured data into clean, usable inputs for insurance workflows.

Model agnostic platforms are not locked into a single model. Instead, they can integrate and switch between multiple models, choosing the best one for each task. 

Brisc is LLM agnostic and adaptable, selecting the best model for each workflow and ensuring future-proofing and flexibility as new models or regulations emerge.