Explainable AI (XAI) in Insurance: Moving Beyond the “Black Box”
AI is no longer a future ambition for insurers—it’s here, transforming how submissions, claims, and operations are managed every day. But with adoption comes a critical question: Can teams trust the outputs? This is where explainable AI (XAI) comes in.
By making AI-driven insights transparent and auditable, explainable AI ensures decisions are traceable. Insurance teams can see what data was used, how it was interpreted, and why it led to a certain result.
Key Takeaways
- Explainable AI ensures AI decisions are transparent, interpretable, and justifiable—critical in regulated industries like insurance.
- From submissions and claims to fraud detection and operations, explainability helps teams validate AI outputs by showing where data came from and why decisions were made.
- Brisc’s Submissions Agent provides a clear example of explainability in action, with features like data lineage, decision rationale, and human-in-the-loop verification.
- The result: faster decisions, fewer errors, greater adoption of AI, and stronger trust across the organization.
Bridging The AI Trust Gap
A growing number of MGAs, carriers, and reinsurers are investing in AI, with the percentage of insurers fully embedding it into their value chain jumping from just 8% last year to 34% in 2025. But alongside this adoption comes a challenge: trust.
Many AI solutions operate like a “black box.” They deliver outputs, but users don’t know how the AI came to the conclusions it did. Teams are left asking: Where did this information come from? Why was this decision made? Can I trust it?
In an industry that is both heavily regulated and highly dependent on accuracy, trust is essential. Without transparency, AI can be perceived as another risk factor—something insurers are reluctant to embed in their core workflows.
Explainability closes this gap. By surfacing the reasoning behind every AI-driven decision, explainable AI builds confidence, accelerates adoption, and enables faster, more reliable decision-making across insurance operations.
What Is Explainable AI?
Explainable AI (XAI) refers to artificial intelligence systems that make their decision-making processes understandable to humans. Instead of simply presenting an output, explainable AI shows:
- What data was used
- How it was interpreted
- Why it produced a specific result
This focus on traceability—the ability to link an output back to its source—sets explainable AI apart from “black box” models.
It’s also important to distinguish between interpretability and explainability. Interpretability means a model’s structure can be understood in theory—for example, a decision tree splitting on variables. Explainability ensures that in practice, teams can trace and validate an output after it is generated.
For insurers, this distinction matters. Explainable AI ensures operations, underwriters, and claims teams can trust and act on results.
Learn how explainability and other key AI concepts are reshaping insurance operations. DownloadDecoding AI in Insurance.
Why Explainability Matters in Insurance
Building Trust Across Teams
Insurance operations involve many moving parts—distribution, product development, operations, claims, and underwriting. Explainable AI gives all of these teams confidence by showing how outputs were generated and why they can be relied on.
Strengthening Auditability & Compliance
As a heavily regulated industry, insurance requires that every decision be defensible—whether to auditors, regulators, or even in court. Explainable AI provides the transparent trail needed to validate decisions, justify outcomes, and ensure results stand up to scrutiny. This not only reduces risk but also builds confidence that AI can be safely embedded into core insurance workflows.
Accelerating Adoption
Teams are more likely to embrace AI when they can see how it works. By providing transparency into reasoning and results, explainable AI reduces resistance and accelerates adoption across the organization.
Examples of Explainable AI in Insurance
Explainable AI is most powerful when applied to real insurance workflows. By making outputs transparent and auditable, it ensures teams know exactly where information came from and why a decision was made. For example:
- AI that extracts and prioritizes data from broker submissions shows teams exactly where each field came from, the confidence level in the extraction, and alternative values considered.
- AI that reconciles thousands of records provides a clear audit trail showing how mismatches were detected, which records were matched, and why certain items require manual review.
- AI that classifies and prioritizes incoming claims documents and explains why certain notifications were flagged as urgent, linking directly to the key fields that triggered the decision.
Each of these examples reflects the same principle: explainability transforms AI from a black box into a transparent partner, helping insurance teams move faster while staying in control.
Case Example: Brisc Submissions Agent
Brisc’s Submissions Agent illustrates explainability in practice. It allows underwriting teams to process broker submissions in seconds while maintaining full visibility into how data was processed and extracted, from where, and why.
- Data lineage: Every field—whether pulled from a PDF, spreadsheet, or SOV—can be traced back to its source.
- Decision rationale: Underwriters can review why fields were extracted and confidence levels for each field.
- Source transparency: Underwriters can see exactly where data came from.
- Interactive review: Teams can validate or adjust extracted information before it flows into systems of record.
- No black box: Every output is backed by visible, auditable evidence.
The Business Benefits of Explainable AI
Explainability isn’t just about making AI transparent—it translates into measurable business value for insurers:
- Lower operating costs: Reducing manual corrections and rework saves teams time, freeing capacity for higher-value tasks.
- Faster speed-to-market: With explainable insights, teams can process submissions, reconcile bordereaux, or triage claims faster—shortening cycle times and increasing responsiveness.
- Higher win rates: Faster, more accurate quoting and decision-making strengthens broker relationships and improves the likelihood of winning profitable business.
- Improved scalability: Transparent, auditable AI outputs make it easier to expand across additional lines and regions with confidence.
- Better decision-making: Clear reasoning behind outputs helps leadership align teams on priorities and make data-driven business choices.
Conclusion
As insurers embed AI more deeply into their operations, explainability will determine which solutions earn lasting trust and adoption. From bordereaux reconciliation and claims triage to underwriting and submissions, explainable AI ensures that every output is transparent, interpretable, and auditable.
For insurers, it’s the foundation for faster decisions, stronger collaboration, and sustainable growth.
Brisc’s Submissions Agent is one example of explainability in action, combining speed, accuracy, and transparency to help insurance teams make better decisions, faster.