By Alex Johnson, Head of Insurance Solutions at Quantexa
Today, the insurance industry cannot utilize all of its available data, often leaving a majority of data untapped due to perceived complexity. This creates a significant traceability gap. As AI increasingly influences coverage, pricing, and liability, organisations must be able to demonstrate exactly how data sources were used and support decisions with clear evidence. Explainability across the entire data estate builds confidence and trust, enabling AI to evolve from a technical capability into a core decision-making function.
Why Explainability Matters
Insurance and financial services operate in highly regulated environments where accountability has traditionally applied to human judgement. As AI assumes a greater role in augmenting decisions, expectations for accountability are expanding to automated and AI-assisted processes, particularly where outcomes impact pricing, financial exposure, coverage, or access to services.
Regulators expect firms to demonstrate alignment between decisions, policy, and risk appetite. Auditors require traceability from source data to final outcomes, while customers expect fairness and transparency in their dealings. Explainability and accountability help organisations meet these expectations while supporting responsible innovation and closer collaboration between technology, risk, compliance, and business teams.
Regulatory Pressure on AI Decisions
Regulatory scrutiny is increasingly focused on the transparency and fairness of decision processes rather than simply model performance. AI-driven decisions must operate across fragmented core systems and rely on trusted, connected data to remain consistent over time.
This requires clear data lineage back to the individuals, organisations, or third parties associated with a decision. Controls over model inputs and outputs must be traceable to the people or businesses affected. Regulators are also placing growing emphasis on review processes both during decision-making and after the fact. Organisations that can clearly explain individual claims, pricing, or risk decisions demonstrate stronger compliance and often improve operational performance.
From Model Metrics to Decision Transparency
Aggregate metrics such as model accuracy provide a high-level view of performance but offer limited insight into individual outcomes. In regulated industries, decisions occur at the individual level, such as determining the price of a specific risk or deciding whether to approve a claim. Each outcome carries direct implications.
Decision-level transparency focuses on understanding what influenced a specific outcome. This includes which data points mattered, how relationships influenced the assessment, and how context shaped the result. It allows teams to understand reasoning rather than relying solely on model scores, supporting stronger documentation, clearer accountability, and greater trust in AI-enabled processes. As adoption matures, this level of transparency helps organisations move from cautious experimentation to confident production deployment in mission-critical areas.
Decision Intelligence in Practice
Decision Intelligence (DI) brings together data, entities, and context within a unified framework. Instead of analysing isolated records, it reflects the real-world relationships and behaviours that shape risk. This creates a dynamic knowledge base that supports AI models and agentic systems, ensuring risk-based decisions are grounded in a real operational context.
Commercial underwriting
DI provides visibility into complex corporate structures, directorships, business relationships, and interconnected exposures. By giving underwriters and AI models access to the same contextual data, organisations can justify risk adjustments more effectively, improve product selection, and accelerate underwriting processes by up to 75%.
Claims assessment
Rather than analysing a claim in isolation, DI evaluates the wider network around it, including claimants, witnesses, and suppliers. This network-level perspective can improve loss ratios and indemnity spend by around 3%, supporting reserve releases and helping reduce pressure on premium increases.
By creating a trusted feedback loop from raw data to final decision, DI surfaces data quality issues, reduces fragmentation across systems, and makes outcomes easier to explain, justify, and review. Over time, this feedback loop also strengthens future decisions by improving data integrity and reducing the risk of AI hallucination.
Building an AI-Ready Data Foundation
Many organisations face challenges due to fragmented systems and siloed processes. Addressing these structural issues strengthens the link between data and decisions, improving explainability and organisational confidence in AI.
An AI-ready foundation rests on four pillars: trust, control, connectedness, and context.
Trust means data provenance, lineage, and quality are clearly understood and continuously monitored. Control ensures access to data is governed, logged, and policy-driven across internal teams and third parties. Connectedness allows data to be unified across people, organisations, locations, and physical or digital assets so that models can learn from real-world relationships. Context enriches internal and external data sources and reduces duplicate views of customers, claimants, and suppliers by up to 60%, ensuring AI models reflect how insurance and financial risk operate in practice.
Enabling Confident AI Adoption
Stronger data foundations and decision transparency allow insurers and financial institutions to deploy AI with greater confidence while engaging constructively with regulators during audit and review processes. These capabilities support clearer governance, better risk evaluation, and faster organisational learning from outcomes.
As the market matures, Decision Intelligence supports the shift from cautious experimentation to evidence-based production systems in mission-critical areas. By embedding transparency and accountability into decision-making processes, organisations can innovate responsibly while meeting rising expectations from regulators, customers, and the market. Over time, this approach allows organisations to transform data uncertainty into an operational and competitive advantage in months rather than years.
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