Everybody who has done a single manual policy check knows how slow the process is. Hurrying up increases the likelihood of making a mistake, which can result in significant financial losses.
You would agree that it’s unacceptable to make a mistake while performing a task whose whole point is to correct mistakes and pay attention to details.
Insurance has very strict rules when it comes to policy checking, yet insurance carriers, MGAs, and brokers handle increasing volumes of complex policy data every day. A single unchecked detail can turn into regulatory penalties, lost revenue, or client dissatisfaction.
Policy checking automation is one of the most valuable processes to implement into existing systems. While retaining a high level of accuracy, insurers save hours spent on paperwork and can start focusing on more important tasks.
Although AI began its implementation in insurance a bit later compared to other industries, it is currently at its peak. Automation in insurance streamlines the most time-consuming aspects of the process while ensuring compliance, reducing risk, and expediting decision-making.
In this blog, we’ll explore how AI is redefining policy checking, what benefits it brings, and how your organization can adopt it without disrupting your existing workflows.
TL;DR (Too Long; Didn’t Read)
- Manual policy checks are slow and are being replaced by AI Policy Checking tools
- AI automates field extraction, document comparison, and the detection of discrepancies.
- Policy Checking Automation ensures compliance, reduces E&O risk, and speeds up policy issuance.
- Only critical mismatches are flagged for human review, saving time.
- BoundAI integrates with any system to streamline operations without major tech changes.
What Is Policy Checking?
Policy checking ensures that the final issued insurance policy matches the terms and conditions negotiated during the quote and bind stages. The reasons for possible mistakes are numerous, and although we always strive to avoid them, mistakes can occur.
The insured has multiple locations, back-and-forth communication and negotiation with different people, or different quotes displayed, which is why policy checking is a part of the policy lifecycle.
Traditionally, this process is handled manually by underwriters or underwriting assistants who compare multiple documents, such as quotes, binders, applications, endorsements, and final policies. The purpose of policy checking is to ensure that coverages, limits, and deductibles are consistent across all documents. The same applies to named insureds, policy numbers, premium amounts, addresses, forms, and other details.
At first, it might sound easy; all you have to do is read the documents and compare them to ensure they contain the same data. In reality, underwriters must process dozens of policy checks daily, and they face obstacles such as handwritten documents, unorganized files, or time pressure, among others.
Replacing Manual Policy Checks with AI
While Excel sheets and document checklists offer some structure, they can’t scale. As policies become more customized and documentation more complex, manual tracking becomes unmanageable.
As mentioned above, human error is sometimes inevitable due to the amount of workload and traditional practices. Underwriters commonly face obstacles while conducting policy checks, including unstructured data, disorganized files, and cluttered PDFs. Policy issuance deadlines are often tight, leaving little time for detailed reviews.
Adding more people can be draining, as having more people work simultaneously creates more variability and liability. Insurance companies are replacing manual policy checks with AI-powered tools due to the benefits AI offers and its potential for increased profitability.
What Is Policy Checking Automation?
Policy checking automation uses AI, machine learning, natural language processing (NLP), and intelligent document processing (IDP) to automatically extract, compare, and validate data across policy documents.
Policy checking automation turns policy checking from a manual task into a high-speed, intelligent validation engine that augments human decision-making.
Document Ingestion
The AI system reads documents, such as application forms, quotes, binders, and final policies. The main difference is that document ingestion can read both structured and unstructured data in PDF or scanned formats.
Field Extraction
Thanks to natural language processing (NLP) and optical character recognition (OCR), AI can identify and extract key fields, such as coverage terms, limits, deductibles, premium amounts, and named insureds. AI doesn’t just recognize characters, but it also understands what those characters represent based on their context.
Document Data Comparison
AI compares extracted data across versions and identifies mismatches, missing information, missing endorsements, or inconsistencies. AI creates a side-by-side comparison or a highlighted summary of mismatches, enabling underwriters and compliance teams to verify what changed and why quickly.
Flagging Discrepancies for Human Review
Only discrepancies or edge cases are flagged for human review, which dramatically reduces the workload for your underwriters. Instead of overwhelming underwriters with every detail, these systems are trained to surface only what truly needs human attention.
Reporting and Audits
One of the most critical advantages of AI-powered policy checking is its ability to create a complete digital audit trail. Each comparison is logged, making it easier to demonstrate compliance during audits.
Key Benefits of AI in Policy Checking
Speed and Accuracy
AI processes and compares documents in minutes, not hours. What once took 30–60 minutes per policy now takes seconds. The best part is that accuracy remains at a high level, with more than 90% accuracy, depending on the tool. Using AI for policy checking reduces policy issuance time and catches discrepancies that a human eye might miss.
Risk Reduction
AI systems don’t get tired or skip steps, but they consistently check every clause, every time. This reduces exposure to E&O risk and ensures your policies always reflect what was agreed upon.
Scalability & Operational Cost
AI Policy Checking software is beneficial not only for huge insurance companies but also for fast-growing MGAs and carriers. By eliminating manual data entry and redundant reviews, policy checking automation frees up underwriters and support staff to focus on higher-value tasks, such as client service, risk evaluation, and strategic decision-making.
Real-Time Insights
AI tools generate structured data that can be fed into dashboards or analytics tools to prepare a report. This creates visibility across your book, flagging recurring discrepancies, training gaps, or trends in broker behavior.
How to Get Started
Begin by defining your objectives and determining your company’s primary goal. Having clear goals helps you prioritize use cases and select the most suitable vendors. Analyze the documents you work with to prepare for the next step, which is AI training on your real documents.
The final step is to monitor and track your performance with AI, but first, you need to choose the right tool to integrate into your existing system.
BoundAI: Your Reliable Policy Checking Agent
Professionals with extensive experience in the insurance industry have developed the Bound AI Policy Checking Agent. It was tailored for insurers, and by insurers. BoundAI doesn’t just extract text automatically; BoundAI understands insurance, policy structure, and insurance terminology.
With Bound AI, you get insurance-native NLP models, up to 99% data extraction accuracy, side-by-side policy comparisons, and flagged discrepancies with visual highlights. The result is faster policy issuance, fewer errors, and stronger compliance without needing to overhaul your tech stack.
Click here to learn more about the BoundAI Policy Checking Agent and its benefits.
The Bottom Line
Manual policy checking has enabled insurers to survive for all these years, but it’s time for a change. With other insurance companies switching to AI policy checking, manual policy checking is not sustainable, and you won’t be able to keep up with the market.
Comparing the two methods, AI policy checking wins all comparison points, and you can only benefit from it; there are no downsides. Automation isn’t about replacing underwriters; it’s about supporting them.
If you want to take your operations to the next level and stay competitive in the market, book a call with us to discuss your next steps.
