Trust in the Machine: Building Confidence in AI-Led Insurance Decisions

Insurance has always balanced numbers with judgment. What is changing now is who (or what) makes those judgments. Artificial intelligence is being built into everything from underwriting, claims reviews and customer conversations. It’s quick, it’s consistent and it can spot patterns with ease. Still, that doesn’t mean people automatically trust it. When decisions start coming from a system rather than a person, questions about fairness, control and accountability come up fast.

If that trust isn’t there, progress can stall. Regulators hesitate, customers grow wary and even staff may resist relying on the tools meant to help them.

So, confidence in AI comes down to three simple principles: fairness, explainability and accountability. Each one supports the others. The real challenge is weaving them together so the technology works clearly and responsibly. The next section looks at how that plays out in practice and what it means for insurers using AI day to day.

Key Statistics

Let’s start with some statistics:

AI adoption is now scaling rapidly across global and Indian markets. The AI in Insurance market alone is projected to rise from USD 8.63 billion in 2025 to USD 59.50 billion by 2033, growing at nearly 27% annually.

In India, the InsurTech ecosystem (already valued at USD 0.9 billion in 2024) is expected to surge to USD 43.9 billion by 2033, with a 36% CAGR, making it one of the fastest-growing InsurTech markets globally.

This momentum underscores a clear reality: the future of insurance will depend not only on how well AI performs, but on how much customers and regulators trust it.

Fairness: Ensuring Equitable Outcomes

Fairness means that an AI system treats policyholders without unjust discrimination, whether by gender, race, postal code or other proxy variables. In the insurance aspect, this is especially sensitive because premium and claim decisions affect people’s livelihoods. According to a recent survey, respondents judged an insurance pricing practice as fair only if they saw both logic and influence over the outcome.

From a technical standpoint, fairness involves:

• Identifying protected factors (like gender, ethnicity) and ensuring that the model either omits them or handles them in a way that doesn’t lead to disparate impact.

• Monitoring fairness metrics such as false positive rate parity, false negative rate parity, equal opportunity, demographic parity. For example, a model may be calibrated so that across different groups, the rate of incorrectly rejecting valid claims is the same.

• Mitigating bias in training data which may reflect historical imbalance. Careful pre-processing or in-processing may be required.
  

In practice, fairness is not “once and done”. Models evolve, business conditions change and new data builds up. As such, continuous monitoring, periodic fairness auditing and recalibration are needed.

As insurers expand AI-led underwriting and pricing models, regulators are starting to treat fairness metrics as compliance essentials rather than ethical extras. This shift mirrors the rise of embedded insurance and predictive analytics, where bias-free automation directly impacts customer acceptance and retention.

A 2023 study by the European Journal of Risk Regulation found that people perceive pricing algorithms as fair only when the logic behind them is transparent and when customers feel they retain some influence over the outcome. That insight applies equally to underwriting and claims decisions.

Explainability: Making Machine Decisions Intelligible

One barrier to trust is the perception that AI is a “black box” – a model that takes inputs and spits out a decision without transparency. In an industry like insurance, regulated and customer-facing, this cannot stand. Explainability means providing insights into why the model made a decision and what its logic is.

Techniques for explainability include:

• Feature importance or SHAP (SHapley Additive exPlanations) values: Highlighting which features contributed most to a decision in a given case.

• Counterfactual explanations: Describing “if X had been different, the decision would change to Y”, helping users understand sensitivity.

• Local Interpretable Model-agnostic Explanations (LIME): Giving a simplified surrogate model that mimics how the complex one behaves for a particular decision.

• Model-behaviour dashboards: Showing aggregate statistics, decision‐thresholds, feedback loops and “why” summaries to business users and regulators.

• Explainability also supports the customer experience: If a policyholder is denied a claim, providing a clear, understandable rationale builds credibility and reduces appeal risk.

Explainable AI is also becoming a market differentiator. As AI handles larger portions of claims and underwriting, it is expected to reach 70–80% of routine insurance decisions by 2033. This means that firms that offer transparent reasoning will gain a clear advantage in customer trust and regulatory confidence (Global Growth Insights, 2025).

A paper published in the Annals of Actuarial Science noted that while advanced analytics opens powerful opportunities, it also demands new forms of transparency. Explainability, in practice, becomes both a technical and a cultural responsibility.

Accountability: Putting Humans in the Loop

Even the best AI model should not be left to run autonomously without oversight. Accountability means that human roles, governance structures and audit trails are in place so decisions can be reviewed, corrected and defended.

Insurance firms are increasingly adopting governance frameworks for AI. For example, regulators and industry guidance urge that firms perform impact assessments for AI use-cases, establish roles for oversight, define escalation paths and maintain audit records.

In practice, this means:

• Documented decision-flows: model version, inputs, outputs, rationale logged.

• Human review of decisions flagged as high-impact (for example large claim denials or pricing changes).

• Appeal workflows for customers, giving them a voice and mechanism to challenge decisions.

• Continuous performance tracking: drift, fairness metrics, business KPIs.
  

In short, accountability gives assurance that if something goes wrong (or appears unfair) there is a humanable system of response.

By 2030, as GenAI tools become standard in claims processing and policy servicing, regulators are expected to mandate stronger auditability requirements across Asia-Pacific. India, which already contributes nearly 28% of the region’s AI-in-InsurTech market (Global Growth Insights, 2025), will likely be at the forefront of this shift.

As Clifford Chance highlighted in its 2021 paper on AI in insurance, firms should view governance not as a regulatory formality but as part of good business practice. A transparent audit process protects both the insurer and the insured, ensuring that technology remains accountable to human judgment.

Case Study: AI Explainability in an Insurance Setting

A recent academic study examined how AI and explainability tools are used in the insurance domain. The authors found that while firms deploy explainability methods, people often use them differently than intended. They adapt the tools to fit their daily work, which shows a gap between how the systems were designed and how they’re actually used.

Key take-aways from that case:

• Although explainability tools were integrated, end-users (underwriters, claims staff) used simplified explanations or heuristics rather than full system logic. This points to the need for user-centric design of explainability outputs (not just algorithmic).

• Unexpected user behaviour emerged. For example, users interpreting feature-importance graphs incorrectly, or skipping parts of the explanation workflow. Here, the recommendation is better training, tailored dashboards and governance of how users interact with the model.

• The study emphasises that trust is built not just by the model but by the human-machine interface, clear documentation and alignment of model outputs with human workflows.

  
  

In other words, even if the technology is sound, the “last mile” of how humans engage with the model matters deeply.

Designing for Trust

For InsurTech firms aiming to embed trust into AI-led decisions, here are some practical guidelines:

Start with Impact Assessment	Classify AI use-cases by risk (e.g., underwriting vs marketing) and apply stronger governance where customer impact is high.
Select Appropriate Fairness Metrics Early	For example, equal opportunity (same true positive rate across groups) or calibration (predicted risk matches actual risk). Monitor them over time.
Build Explainability Tools Tuned to Users	Not all business users are data scientists. Provide dashboards, plain-language summaries, interactive visualisations. Train users in interpretation.
Establish High-Impact Decisions	For claims above a threshold, or pricing shifts, include human review, appeal rights, clear audit logs.
Embed Continuous Monitoring & Model-Governance	Set up alerts for model drift, fairness waterfalls, performance degradation. Schedule regular audits and model refreshes.
Communicate With Transparency	Onboarding documents, policy communications, customer portals should explain how AI decisions are made (in plain language), what data is used and what recourse exists.
Ensure Data Governance	Protect sensitive attributes, ensure data quality, document proxies and check for unintentional bias from proxies (e.g., postal code acting as a race proxy).

Summing Up,

As AI takes a deeper role in insurance decision-making, the challenge is no longer simply “Can we use it?” but “Can we trust it?” Fairness, explainability and accountability become the pillars of that trust. InsurTech companies that embed these principles will not just reduce downstream risk: they will engender stronger relationships with customers, regulators and partners. In that way, trust becomes a competitive differentiator, not just a compliance obligation.

Author: Dr. Kavindra Kumar Singh, Chief Technology Officer, SMC Insurance Brokers Pvt Ltd

Disclaimer: The opinions expressed within this article are the personal opinions of the author. The facts and opinions appearing in the article do not reflect the views of IIA and IIA does not assume any responsibility or liability for the same.