IDP for Insurance
Document Automation with Advanced AI
6 Insurance Use Cases Where Cortical.io Document Automation Can Help
Improve quoting efficiency
Quoting group benefits is probably one of the most challenging quoting processes and requires the analysis of many documents of variable types and structures. Our IDP solution extracts, interprets and classifies key information found in terms and provisions and is able to detect the best plan matching a competitor’s policy. As a result, underwriting teams can prepare more accurate quotes faster and close more deals.
Mitigate risks
Monitoring the gaps between standard global policies and locally-issued documents is a manual, error-prone process that can be drastically improved with our meaning-based IDP software which performs accurate comparisons both at the policy and clause level, and redlines deviations. It helps reduce both the risks of losses and the personal resources required to manually sift through thousands of policies.
Accelerate the intake process
Large insurers receive hundreds of quote requests per email every day. Our IDP solution can speed up the intake process by filtering out requests where additional information is needed, sending appropriate notifications both to the customer and the underwriting team, and creating records in a quoting or CRM platform.
Go beyond simple claim processing
Our IDP software can be used to extract and compare complex information from documents like First Notice of Loss (FNOL) and witness reports, and to generate new data for claim decisioning and claim validity checking.
Improve customer experience
During the Request for Proposal (RFP) process, insurance brokers spend a lot of time comparing manually various carrier proposals because of the lack of standardization of the documents. Our IDP solution helps automate this process, enabling brokers to improve customer experience with more timely responses.
Reduce coverage gaps
Our meaning-based IDP solution helps you systematically compare policies with the initial quotes even when the documents have different formats, structures, and terminologies. This helps insurance carriers minimize the risks of losses.
How does Cortical.io improve the efficiency of the quoting process?
Watch this 2-min video to understand how Cortical.io IDP software helps you:
Extract accurate information from thousands of policies
Interpret provisions
Deliver more timely, higher quality quotes
Why is SemanticPro For Insurance Different?
Grouping of extractions
Cortical.io SemanticPro extracts complex information from insurance plans and is able to group extractions, for example by employee class or service-type in dental plans.
Interpretation of provisions (inferences)
Inferences are a way to interpret and categorize extracted data. Disability insurance policies, for example, contain beneficiary and funding method provisions that must be matched to the correct option from among multiple possibilities. Using meaning-based interpretation, SemanticPro is able to correctly interpret the phrases “you do not pay for the cost of coverage” as “100% employer-paid” (funding method) and “you, your spouse or children” as “family” (beneficiary), respectively.
Business rules for automated decision making
SemanticPro offers the possibility to define custom business rules and apply them to the extracted information. For example, SemanticPro can identify the best plan matching a competitor’s offer and pre-fill the appropriate quote template with the extracted information. The transparent scoring system enables full explainability of results.
Complex table extractions & mapping tables
Insurance policy documents typically organize policy details about coverage, benefit amounts, exclusions and more into tables with multiple columns and rows that are often visually stylized. Cortical.io utilizes robust OCR to cleanly and accurately parse the text from these tables and render the data for easy annotation and extraction. Our system can also generate mapping tables in order to map the column values between different tables.
Comparison by extractions
With SemanticPro, you can compare documents on the basis of the extractions, instead of the whole document: either via a redline of the extracted provisions, or via a similarity score that tells the similarity of two provisions based on their meaning.
Meaning-based search
SemanticPro uses meaning-based search that distinguishes context and relationships between words and phrases that keyword-based search engines simply can’t achieve. For users, this means faster search times and more accurate results.
No code platform, specially built for business users
Business users define extraction targets and inferences, upload documents and start training the system to automatically perform extractions without the need or cost of an AI expert. The visual output can be customized to match the required input for a given quoting tool, for example.
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