By combining a trusted digital certificate with data controls and automation, the AI for insurance It can support customer service, document analysis, claims triage, back-office operations, and underwriting. The concept brings together analytical models, generative artificial intelligence, natural language processing, optical character recognition, and rule-driven automation.
These technologies identify patterns, extract information, generate summaries, and suggest next steps, but they should not replace human oversight in decisions regarding coverage, pricing, compensation, or policyholder rights.
Safe adoption defines the role of each system. AI organizes data and reduces repetitive tasks, while experts validate exceptions and sensitive decisions. The same logic applies to AI with electronic signatures: documents, approvals, and records need to maintain integrity and traceability.
Summary
- AI can support customer service, claims, document analysis, back-office operations, and underwriting.
- Lower-risk processes should be prioritized before sensitive decisions.
- Permitted data, human review, bias testing, security, and logs form the basis of governance.
- Average Handling Time (AHT), Service Level Agreement (SLA), accuracy, Net Promoter Score (NPS), rework, and escalation help measure results.
Quick facts
- A General Protection Law It ensures the right to request a review of decisions based solely on automated processing that affect the interests of the data subject.
- Os OECD AI Principles They promote innovative and reliable systems, aligned with human rights and democratic values.
- A ISO/IEC 42001:2023 standard It guides the creation, maintenance, and continuous improvement of artificial intelligence management systems.
Where can AI be applied to lower-risk insurance?
Initial projects should focus on frequent, reversible, and auditable tasks, such as status queries, message classification, field extraction, and summaries. However, coverage denial, pricing, and compensation decisions require stronger controls. Risk classification prevents a speed-focused solution from influencing results dependent on legal, technical, or actuarial analysis.
Initial care and triage
Chatbots can answer questions about channels, documents, steps, and deadlines. Integrated with online serviceThey identify intent, summarize conversations, and route the insured party. The workflow should inform automation, limit responses to authorized databases, and offer human escalation. Complaints, vulnerabilities, and coverage interpretations should not be resolved solely by the system.
Claims, documents and back-office
OCR can extract data from forms, notes, and reports, reducing manual data entry. Analytical models identify missing fields, discrepancies, or duplicates. The team confirms the reading when the file quality is low. A routine of document analysis It should record the source, the reliability of the extraction, and the corrections made by the reviewer.
Generative AI can summarize policies, files, and contacts. In Document management with AIThe template should use controlled versions and indicate sources. Summaries do not replace the original. In claims, inconsistencies serve for screening purposes, never as automatic proof of fraud or as the sole basis for denying a claim.
Subscription support
In underwriting, models can gather data, compare rules, and prepare a consolidated view. Their use becomes more sensitive when it influences acceptance, price, or coverage. The insurer must document criteria, sources, and margins of error, as well as allow for challenges and revisions. The analysis of legal risk It should consider the effects on the consumer and indirect discrimination.
How do you separate low-risk from high-risk processes?
An inventory identifies what can be automated, what requires approval, and what remains under human decision. The assessment combines impact on the insured, data sensitivity, reversibility, explainability, and error detection. The greater the financial or legal impact, the less autonomy the system should have.
| Production | risk level | The role of AI | Human control |
|---|---|---|---|
| Email classification | Low | Identify theme and queue. | Review samples and errors |
| Summary of policies | Medium | Locate clauses and synthesize content. | Validate before guiding the client. |
| Claims triage | Medium | Organize documents and note pending issues. | Confirm priority and routing. |
| Pricing or negative | High | Provide analytical support | Decide, justify, and review. |
According to NAIC guidanceInsurance companies remain responsible for insurance rules, consumer protection, accuracy, fairness, and non-discrimination in the use of AI. This recommends written policies, designated accountable parties, and discontinuation criteria when errors exceed limits, groups are harmed, or the outcome cannot be explained.
Governance, data, and human review.
Governance defines who approves the case, what data enters the system, where the results are stored, and who is responsible for incidents. According to... definition presented by ANPDAutomated processing occurs when systems, including AI, collect, store, or make decisions based on personal data without direct human intervention.
Permitted data and security
The company must limit access, separate testing and production, and prevent sensitive data from being stored on unapproved tools. Sensitive information requires encryption, defined retention, and traceability. document security It also covers temporary files, prompts, responses, and integrations. Contracts with vendors need to define purpose, storage, disposal, and incident response.
Pre-production controls
Before release, the team must test anonymized samples, edge cases, incomplete documents, and attempts to induce errors. The system needs to reject out-of-scope requests, indicate uncertainty, and forward exceptions. Model versions, knowledge base, instructions, and human corrections must remain logged for auditing purposes.
Bias, explainability, and logs
According to the NIST AI RMFReliable systems must strive for validity, security, resilience, transparency, explainability, privacy, and management of harmful biases. This requires segmented testing, experts, and continuous monitoring. Logs record inputs, sources, outputs, interventions, and decisions. Digital compliance can incorporate this evidence into audits.
How do we measure AI performance?
The gain in speed only represents success when quality remains within limits. The measurement compares the previous scenario, the pilot, and production, with breakdowns by product, channel, request, and affected group. This allows the insurer to identify whether automation reduces queues in a balanced way or transfers work to later stages.
- TMA: Average time required to complete the service.
- ALS: Percentage of cases resolved within the agreed timeframe.
- Accuracy: proportion of correct classifications or extractions.
- NPS: Customer perception of the experience.
- Rework: Cases corrected after the AI exited.
- Scaling: Services transferred to specialists.
The indicators should integrate the processes managementHowever, an isolated improvement can mask bottlenecks. If the chatbot reduces average handling time (AHT) but increases complaints or rework, the workflow needs to change. Productivity without accuracy increases corrections. Goals, limits, and responsibilities should be defined before launch and reviewed periodically.
Check out these related articles as well:
- Digital document management organizes databases used by automation systems.
- Automating tasks reduces repetitive activities by establishing defined rules.
- Artificial intelligence in companies requires clear objectives and controls.
Safe automation depends on clear boundaries.
Responsible implementation begins with a specific problem, authorized data, and objective boundaries. Smaller projects allow for measuring errors, correcting integrations, and training teams before scaling up. Customer service, triage, documentation, and back-office operations offer concrete opportunities, provided the company preserves human review in high-impact situations and maintains accountability for decisions.
When structuring the AI for insurance With governance, security, testing, and metrics, the organization reduces rework without losing control. Document traceability also depends on the validation of identity, integrity, and authorship. The functioning of ZapSign as a Certification Authority It integrates digital trust into operations that require validation and security.
Frequently Asked Questions (FAQ)
The answers below clarify recurring points regarding the adoption, risks, and monitoring of artificial intelligence systems in the insurance sector.
This means applying analytical models, generative AI, natural language processing, OCR, and automation to support tasks such as customer service, document reading, claims triage, and underwriting. The system can organize information, detect patterns, and suggest actions, but the company remains responsible for the results and must maintain human review when there are financial, legal, or relevant effects for the insured.
The best initial candidates are repetitive, reversible, and easily auditable processes, such as message classification, field extraction, summary generation, and status querying. Decisions regarding pricing, coverage, denial, or compensation should receive more rigorous controls. Priority should be given to impact, data sensitivity, ease of explanation, and the ability to correct errors quickly.
An insurance company can use AI to organize documents, identify pending issues, and offer analytical support, but decisions affecting payment or coverage require careful analysis. The system's autonomy should be limited by rules, human review, and appeal mechanisms. Inconsistency alerts should not be treated as automatic proof of fraud nor as the sole basis for denying a claim.
Reducing bias involves reviewing the origin of the data, testing results by groups, documenting criteria, and comparing outputs with human evaluations. It is also necessary to monitor the model after implementation and investigate differences in error, approval, or referral. When there is uneven impact or lack of explanation, the company should stop using the model, correct the process, and revalidate the system.
Average Handling Time (AHT), SLA compliance, accuracy, Net Promoter Score (NPS), rework, and escalation rate offer a combined view of speed, quality, and experience. Metrics need to be tracked by channel, product, and request type. A reduction in average time does not represent improvement when it increases complaints, subsequent corrections, or the transfer of cases to specialized teams.
Getúlio Santos is the CEO of ZapSign, a lawyer, technology enthusiast, and entrepreneur.
