How Is Insurance Underwriting Evolving in the AI Age?
Insurance underwriting is the process of evaluating risks and deciding how to price an insurance policy. Underwriters review data about the applicant, estimate the likelihood of future losses, and set coverage terms. The goal is to maintain a balance between risk exposure and the insurer’s ability to pay claims.
In the past, underwriting relied on manual review. Underwriters gathered documents, checked external data sources, and applied internal rules. Many steps were slow because data was scattered across systems, and most analysis depended on individual expertise.
Over time, insurers introduced structured databases, rule engines, and workflow tools. These systems reduced some manual work, but data entry and document review still required significant human effort. Risk models improved, yet they were limited by the speed and consistency of data collection.
AI shifts much of this process to automated extraction and pattern recognition. By combining generative AI models with traditional machine learning techniques, modern solutions scan documents, pull key fields, and classify risks with high accuracy. They can also read unstructured text and summarize details for human review. This dramatically reduces effort, improves accuracy, and shortens time for application approval, improving customer satisfaction.
This is part of a series of articles about insurance claims processing
How AI Is Transforming Insurance Underwriting
AI is reshaping underwriting from a manual, resource-heavy process into a streamlined, strategic function. Traditionally, underwriters spent large portions of their time gathering data, checking paperwork, and performing repetitive tasks. Today, AI systems can automate many of these steps with high precision, prefilling forms, extracting data from documents, and even analyzing images or satellite data to assess property conditions.
Automation addresses a major operational burden. An estimated 40% of underwriters’ time is spent on non-core and administrative work. Across the industry, this inefficiency could cost up to $160 billion over five years. AI reduces this drag by enabling automated triage, data enrichment, and risk scoring. Submissions can be quickly assessed for appetite fit and likelihood to bind, allowing insurers to prioritize profitable opportunities and boost productivity.
The impact extends to speed and customer satisfaction. In life insurance, where policy approval can take weeks due to medical data collection, AI can accelerate this timeline significantly by pulling relevant records automatically. For property underwriting, virtual surveys using satellite or drone imagery replace costly and time-consuming on-site inspections. These AI-enabled tools not only reduce turnaround time but also enhance risk assessment accuracy.
Accenture published several case studies that show dramatic improvements. For example, one insurer reported a 73% increase in claims cost efficiency after adopting AI, and 50% of users of its digital system would recommend it to others. Over 65% of underwriting professionals expect significant AI spending in their firms, with many insurers allocating more than $10 million for AI initiatives. This reflects AI’s growing strategic importance in insurance underwriting.
How Does AI Help Insurance Underwriters?
Let’s review the key areas in which AI can reduce manual workload for underwriters.
Data Extraction, Processing and Enrichment
Modern underwriting increasingly depends on assembling large amounts of data, often in disparate formats (PDFs, scanned documents, images, emails, third-party reports, etc.), to build a comprehensive view of an applicant or asset. Generative AI, powered by large language models and document-understanding tools, helps automate this entire front-end pipeline. It can ingest unstructured documents, perform OCR (optical character recognition) on scanned pages, and then use natural language processing to identify and extract relevant fields (such as applicant name, age, health data, property details, prior claims, or third-party records).
Beyond just extraction, AI can enrich the data by linking to external datasets, for example, combining property records, geographic and environmental data (like flood-zone maps, crime statistics, or local weather history), credit data, or telematics / IoT feeds. This enriched profile gives underwriters a more holistic, up-to-date context for each application, often far beyond what static forms or questionnaires provide.
Risk Assessment, Pricing and Decision-Making
Once data is structured and enriched, AI-driven risk assessment models can evaluate it more deeply and consistently than traditional rule-based or manual systems. Machine learning models trained on historical underwriting and claims data can detect subtle patterns and correlations; for example, how certain combinations of property data, environmental exposures, and prior claims history predict future risk.
Generative AI enhances this further: by interpreting unstructured or complex data (e.g., free-text medical notes, environmental reports, narrative loss runs), it can surface hidden risk factors and flag anomalies that may warrant deeper review, such as inconsistent statements, emergent hazards, or unusual risk clusters.
On pricing, AI enables more granular, tailored pricing models. Rather than relying on broad risk categories, insurers can build micro-segments based on a wide array of signals: Location, property condition, behavioral data (e.g., driving patterns, IoT sensor data), environmental risk, claims history, and more. This allows for dynamic, risk-based pricing and personalization: customers are neither overcharged for low-risk profiles nor underpriced for high-risk exposures.
Workflow Automation and Efficiency
With genAI and ML underpinning intake, data extraction, enrichment, risk scoring and pricing, much of the traditional manual workflow becomes automatable. Submission intake, document parsing, data validation, basic risk pre-screening, and even policy generation can be handled by AI-driven pipelines, dramatically reducing the administrative burden on underwriters.
This automation enables large increases in throughput: Insurers can process far more applications at the same time, scale underwriting capacity without proportionally increasing headcount, and cut down turnaround times from days or weeks to hours or even minutes.
Some platforms now support what’s called “straight-through processing” (STP), where straightforward cases are approved automatically without human intervention, and only complex or flagged cases are escalated for manual review.
Underwriting Governance, Explainability and Compliance
As AI gains a central role in underwriting, insurers must maintain transparency, fairness, and regulatory compliance. One major challenge is ensuring that AI-driven decisions can be explained and justified, not simply because of internal audit and governance needs, but also to satisfy regulators and prevent unfair discrimination.
Modern implementations of AI-based underwriting increasingly embed explainability mechanisms: generative AI systems can reference which data elements or clauses triggered certain decisions, and generate “reasoning summaries” that underwriters, auditors, or regulators can inspect.
In addition, AI helps enforce consistent application of underwriting rules and guidelines. By encoding those rules into the AI logic (or incorporating them into retrieval-augmented prompts), insurers reduce subjective variation across underwriters and ensure that each decision aligns with company policy and regulatory standards.
Which Underwriting Processes Can Be Automated by AI?
Risk Profile Analysis
AI systems can automatically aggregate and normalize data from a variety of sources, not just the application form, but property records, environmental data, credit information, IoT/telematics data, prior claims history, and even unstructured text (e.g. narrative descriptions, free-text disclosures).
Once data is consolidated, ML and predictive-analytics models score the risk profile. These models can identify subtle patterns and correlations (for example, how property age, building materials, local climate risk, and past claim frequency combine to influence future loss probability), far beyond what traditional rule-based underwriting can catch.
In practice this means insurers get a comprehensive, data-driven risk assessment in minutes rather than days, enabling more consistent, granular, and accurate underwriting decisions.
Loss Run Analysis
“Loss runs” are documents summarizing a policyholder’s prior claims history (dates, amounts, causes, frequencies).
AI-driven loss-run automation ingests these reports (often in inconsistent formats like PDFs, scans, free text) using OCR + natural language understanding, extracts the relevant structured data (e.g. number of claims, severity, patterns over time), and consolidates it into a standardized format.
That allows underwriting teams to quickly review a policy’s historical claims footprint, detect trends (e.g. increasing frequency or severity), and adjust risk assessment or pricing accordingly, without labor-intensive manual review. Some firms have reported cutting processing time per submission from hours to minutes.
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Fraud & Compliance Monitoring
AI can flag anomalies or suspicious patterns, both at the submission stage and during claims, that may indicate fraud or compliance risk. For example: using predictive models, anomaly detection, and network analytics, AI can assess whether claims histories, relationships between involved parties, or unusual claim patterns match known fraud indicators.
Natural language processing (NLP) lets AI read unstructured data (free-text statements, notes, prior losses) and detect inconsistencies or red flags (e.g. contradictory descriptions, unusual phrasing, suspicious timing).
By automating these checks, insurers can more efficiently channel suspicious cases for deeper human review, reducing fraud risk, improving compliance, and avoiding unnecessary payouts or regulatory exposure.
Operational Insights
Beyond individual applications, AI helps insurers derive macro-level insights across portfolios. By processing large volumes of underwriting and claims data, ML enables underwriting managers to analyze trends, e.g., which property types or geographies are trending toward higher losses, which segments offer the most profitable risk/return balance, or where underwriting guidelines might need updating. This helps with portfolio optimization, re-underwriting decisions, and strategic resource allocation.
Moreover, automated workflows and data enrichment improve operational efficiency: tasks such as document ingestion, data entry, validation, triage, and preliminary risk scoring can be managed by AI pipelines rather than human underwriters, freeing staff to focus on exceptions, complex cases, strategy, and relationship-based work.
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Challenges with AI Adoption in Insurance and How to Overcome Them
Poor Data Quality and Fragmented Legacy Systems
Insurers often rely on data stored across multiple policy, claims, and customer systems that were built at different times and follow incompatible structures. These systems accumulate gaps, inconsistencies, and duplicate records that reduce the reliability of any analytics or AI model that depends on them. When generative models consume unstandardized documents or historical files, quality issues propagate directly into the model’s outputs.
How to overcome:
Establish a unified data model covering policy, claims, billing, and customer records.
Introduce automated data validation and cleansing pipelines.
Modernize or wrap legacy systems with APIs to simplify integration.
Migrate unstructured or document-based information into standardized formats.
Create ongoing data quality monitoring tied to underwriting performance metrics.
Limited Model Explainability and Black-Box Concerns
Advanced models can analyze large volumes of underwriting documents, risk attributes, and historical cases, but many operate in ways that are not transparent to reviewers or regulators. When the reasoning behind an output is unclear, underwriters struggle to defend decisions, verify correctness, or identify when a model has misinterpreted key information in supporting documents. As models grow more complex, organizations face tension between achieving strong performance and maintaining visibility into how each conclusion was generated.
How to overcome:
Use model interpretation tools such as feature attribution, token-level explanations, and scorecards.
Provide human-readable summaries showing which inputs influenced each recommendation.
Calibrate models with domain experts to validate that explanations match underwriting logic.
Maintain documentation describing model behavior, limitations, and review procedures.
Implement governance workflows requiring human sign-off for higher-impact decisions.
Organizational Resistance and Cultural Barriers
AI-driven underwriting introduces new workflows for document handling, risk review, and data extraction, which can disrupt long-standing habits across underwriting teams. Staff may question the reliability of automated assistance or view the technology as a threat to established expertise. These concerns slow adoption and reduce the quality of human–AI collaboration, especially when personnel are not involved in shaping how new systems fit into daily operations.
How to overcome:
Involve underwriting staff early in design, testing, and workflow definition.
Provide training showing how AI accelerates tasks such as document analysis and data extraction.
Set clear boundaries defining which decisions AI supports and which remain with humans.
Build change-management plans that address skill gaps, incentives, and communication.
Track improvements in cycle times and error rates to demonstrate tangible impact.
Best Practices for Deploying AI in Underwriting
Short-Term Moves: Start Small, Build Momentum
In the early stage, insurers should focus on rapid gains that improve underwriting throughput and reduce administrative friction. This phase is about automating the most repetitive parts of the submission and review process while validating that AI can meaningfully reduce cycle time.
Define high-impact underwriting use cases: Identify bottlenecks such as manual submission intake, document review, loss-run analysis, data entry into core systems, or appetite pre-checks. Measure how many hours underwriters and assistants spend on these tasks to prioritize automation opportunities.
Run a focused underwriting pilot: Select one workflow—for example, OCR-based document ingestion, automated data extraction from ACORD forms, or AI-driven preliminary risk scoring—and test an AI tool on real submissions. Benchmark improvements in triage speed, data accuracy, and time-to-quote.
Engage underwriting teams early: Include underwriters, underwriting assistants (UAs), brokers, and operations staff in the pilot. Gather real-world feedback on how well the system handles submission variability, edge cases, and data quality issues.
Medium-Term Moves: Build Infrastructure and Integrate
Once initial pilots deliver clear value, insurers should focus on scaling AI across underwriting, claims, and policy operations. At this stage, the goal is to integrate AI into core workflows so that submissions move through the pipeline faster and more consistently.
Strengthen data infrastructure across the insurance value chain: Unify policy, claims, billing, and customer datasets. Standardize formats for loss runs, inspection reports, and broker submissions. Improve data cleanliness so predictive underwriting models can rely on consistent inputs.
Deploy scalable underwriting technology: Evaluate AI platforms that integrate with policy administration systems (PAS), CRM tools, third-party data sources (e.g., ISO, CoreLogic, LexisNexis), and claims databases. Favor tools that support risk scoring, appetite matching, and automated underwriting guidelines enforcement.
Establish underwriting metrics and governance: Define KPIs such as quote turnaround time, straight-through processing (STP) rates, bind probability, underwriting leakage, data extraction accuracy, and consistency of guideline adherence. Implement human-in-the-loop workflows for higher-severity risks or regulatory-sensitive lines (e.g., life, health, commercial property).
Long-Term Strategy and Organizational Alignment
In the long run, AI becomes embedded in the insurer’s operating model: shaping underwriting strategy, portfolio management, compliance, and customer experience. This phase requires strategic alignment across leadership, technology teams, and underwriting governance.
Align AI with underwriting and portfolio strategy: Ensure leadership understands how AI supports strategic goals such as improving loss ratios, expanding into new lines or geographies, optimizing risk selection, or increasing broker satisfaction through faster responses.
Build cross-functional insurance teams: Create durable collaboration between underwriting, actuarial teams, claims, data science, IT, and compliance. Train underwriters to interpret AI-generated risk scores, appetite classifications, and exposure insights, and to know when manual judgment should override the model.
Monitor underwriting fairness, transparency, and compliance: Regularly review AI recommendations for accuracy, anti-discrimination compliance, regulatory adherence (e.g., state DOI requirements), and proper documentation. Maintain clear audit trails for every underwriting decision, especially in regulated lines like personal auto, homeowners, workers’ comp, and life insurance.
Kolena: Automating Insurance Underwriting with AI
Kolena provides underwriting teams with no-code AI agents designed specifically for document-heavy insurance workflows. These agents can ingest entire submission packets—including financials, loss runs, identification documents, and inspection photos—and convert them into structured, validated insights.
Kolena’s multi-document intelligence mirrors the real behavior underwriters expect: comparing credit data against applications, identifying mismatches between pay stubs and declared income, reconciling claim histories, and highlighting missing or contradictory information. The platform’s ability to produce line-by-line reasoning and citations ensures every extracted data point is transparent, traceable, and compliant—an essential feature in regulated underwriting environments.
For insurers seeking to modernize underwriting without committing to multi-year transformation projects, Kolena’s no-code configuration, instant deployment, and seamless integrations make it possible to automate high-impact underwriting tasks quickly and safely. The result is an underwriting department that moves faster, works more accurately, and scales more effortlessly.