6 MINUTE READ
Reinsurance plays a critical role in the stability and resilience of the insurance industry. By assuming a portion of the risk from primary insurers, reinsurers provide the necessary financial backing to ensure claims are paid, particularly during large-scale loss events.
However, the effectiveness of this essential function is often compromised by a significant challenge: the claims data gap. This gap, created by inconsistent, delayed, and incomplete data transfers from cedents (primary carriers) to reinsurers, impedes the efficiency of the reinsurance process.
In this blog, we explore how this data gap affects reinsurance operations and how emerging claims technologies, particularly AI in insurance, are helping to bridge it.
What Is the Reinsurance Claims Data Gap?
In simple terms, the reinsurance claims data gap is the disconnect between the information a reinsurer needs and what it actually receives from cedents. When a cedent reports a claim under a reinsurance treaty or policy, the details sent to the reinsurer are often late, incomplete, or in incompatible formats. For example, a cedent might send a spreadsheet of claims with missing fields, or a disorganized email notice that someone must interpret manually. The lack of standardization, along with a vast and unorganized volume of documents, creates a gap between raw input and usable data for reinsurers.
A recent report from Aon found that nearly 40% of reinsurers still rely on spreadsheets for managing claims data, leading to inefficiencies and errors in claims handling. This reliance on outdated, manual methods increases the complexity of extracting and analyzing critical claims data.
How This Data Gap Affects Reinsurance Claims Processes
The impact of messy and delayed data on reinsurance claims is significant. Adjusters spend valuable time manually fixing and reconciling information, reformatting spreadsheets, and chasing missing details by email instead of adjusting claims or making decisions.
And because reinsurers typically only gain direct control of claims data after a claim crosses a certain threshold, real-time alerts and centralized data aggregation are essential for responding effectively and avoiding downstream delays.
Delayed or incomplete data can also lead to disputes. If key information (like dates, coverage allocations, or supporting documentation) is missing, reinsurers might contest the claim or delay payment until the information is clarified.
In short, poor data quality creates friction at the worst time – just when insurers and reinsurers are trying to settle claims.
AI Is Changing the Game in Reinsurance Claims Management
Closing the data gap between reinsurers and cedents means converting unstructured, inconsistent inputs into clean, structured data – an area where AI can make a big difference.
Modern artificial intelligence, especially machine learning and natural language processing (NLP), can automatically read and organize claims information much faster and more consistently than any manual process:
Document Extraction
AI tools can automatically extract data from emails, PDFs, and other documents, eliminating the need for manual data entry. This helps reinsurers receive structured data directly from the claim forms or correspondence, without requiring additional manual effort.
Document Classification
Systems that are using AI tools can classify incoming documents based on type, whether they are claim forms, medical reports, or adjuster notes, and then apply tailored rules to each document category.
For example, claim forms may require extracting specific fields like claim number, date of loss, and policy details, while medical reports need to pull out injury details and treatment codes. By using specialized rules for each document type, the AI tool ensures that the relevant information is accurately identified and extracted, reducing the risk of errors and improving processing efficiency.
Data Standardization
AI can interpret varying formats and terminologies used by different cedents and convert them into a consistent, standardized format that aligns with the reinsurance industry’s requirements. For example, one cedent might report “claim amount” while another uses “settlement total.” AI can automatically recognize these as the same data point and standardize them to “claim amount,” ensuring consistency across sources. This process enables seamless integration of data from multiple insurers, improving consistency and accuracy.
These applications of AI tools are already helping reinsurers by reducing administrative overhead, increasing data accuracy, and speeding up the claims settlement process.
Bridging the Gap: Insurers and Reinsurers Improve Collaboration with AI
While it’s clear that collaboration improves when both insurers and reinsurers use AI tools to structure and share data, the true power of these tools lies in their ability to bridge the gap even if only one side adopts them.
By equipping one party with AI tools, the entire ecosystem benefits. AI can structure the messy, unorganized data coming from one side, making it accessible, transparent, and ready for decision-making.
This streamlined process accelerates decision-making, helping identify potential issues early on and preventing disputes before they arise. As a result, policyholders benefit from a more efficient and transparent experience, while both cedents and reinsurers strengthen their ability to manage risk and maintain financial health.
It’s a win-win, where efficiency, accuracy, and collaboration drive long-term success.
Clive™ AI Improves Reinsurance Claims Data Management
Clive is the insurance industry’s first AI Claims Adjuster. Developed by Five Sigma, a leader in AI-native claims management technology, Clive adds AI capabilities to any existing claims management system (CMS) to automate routine and manual tasks, dynamically plan claim handling, and advance the claim automatically according to the insurer’s Standard Operating Procedure (SOP). Clive drives artificial intelligence, efficiency, and accuracy in claims handling. Adjusters are freed to focus on complex decision-making and better customer service.
Here’s how Clive improves reinsurance operations:
- Automated Data Processing and Risk Analysis – Clive automatically processes incoming data from brokers and cedents, resolving formatting discrepancies and unifying claim inputs. Clive transforms raw claims data into structured insights, supporting accurate exposure analysis, reserve optimization, and financial planning – monitoring claim thresholds and triggering alerts when large-loss indicators appear.
- Full Claims Visibility and Centralized Data – Clive consolidates claims data from multiple cedents into a single platform, eliminating data silos, ensuring a structured, real-time view of claims data.
- Treaty and Facultative Claims Management – Clive categorizes and tracks claims by treaty type, whether quota share, excess-of-loss, or facultative reinsurance. He automates validation against reinsurance agreements, ensuring compliance with contract terms and preventing over-reserving, overpayments, or incorrect recoveries.
- Automated Compliance and Recoveries – Clive ensures that all documentation and communication are logged and accessible, supporting automated audits and tracking recoverable balances. This makes it easier for reinsurers to validate payment obligations and audit proof-of-loss documentation.
Clive transforms reinsurance operations from reactive and manual to proactive and automated, improving accuracy, transparency, and decision-making across the board.
