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Take a tourFraud in insurance submissions can distort risk assessments and lead to poor underwriting outcomes. One of the most commonly manipulated documents in the intake process is the loss run.
Loss runs provide a historical record of claims made by a potential insured. When these documents are altered or inaccurate, they can mislead underwriters and impact pricing and coverage decisions.
This article explains how loss runs work, how they can be manipulated, and how automation and AI are used to detect and prevent fraud. It also outlines strategies to validate authenticity and build a secure submission workflow.
A loss run is a report issued by an insurance carrier that shows the claims history of a policyholder over a specific period. It includes details such as the date of each claim, type of loss, amount paid, and current status.
In new business underwriting, loss runs help insurers evaluate the risk associated with a potential customer. They are used to verify past performance and identify patterns in claim behavior.
To put it simply, a loss run for insurance is a formal document that records an account's prior claims activity. Most insurers request loss runs during submission intake to assess whether the account aligns with their risk appetite.
Loss runs influence underwriting decisions by providing evidence of loss frequency and severity. Inaccurate or missing loss data can result in incorrect pricing or acceptance of high-risk accounts.
Loss run manipulation happens when someone changes the claims history to make the risk look better. This is often done to get lower premiums or coverage that might otherwise be denied.
Here are key warning signs to watch for when reviewing loss runs:
When reviewing hard copy loss runs or digital documents, pay attention to visual clues that might indicate tampering:
Chronological gaps in claims history often suggest that certain periods were intentionally left out. For example, if a loss run covers 2018 to 2022 but skips 2020 without explanation, the missing year might contain adverse claims experience not disclosed in the submission.
Gaps paired with sudden improvements in claims frequency or severity can also indicate selective reporting in commercial insurance loss runs.
Sometimes, individual claim amounts or dates are changed to present a cleaner risk profile. These changes may be harder to detect without comparison to historical records.
Modern technology makes it easier to spot potential fraud in loss runs. Here's how automation and AI help:
Pattern recognition: AI systems can analyze loss run data to find statistical outliers. If a business has five years of steady claim activity and suddenly reports no claims, the system flags this unusual pattern. AI can also compare claims to industry benchmarks to spot values that don't make sense.
Document forensics: These tools examine the structure of digital files to find subtle changes in PDFs, like altered fonts, misaligned text, or digital layering that suggests tampering.
Historical comparison: Automated systems can quickly compare current loss runs with previous versions from the same insured. If a claim appears in one version but is missing in another, or if dates and amounts change without explanation, the system catches these differences.
The loss run process benefits greatly from these technological advances, allowing underwriters to focus on truly suspicious cases rather than manually reviewing every document.
There are several practical ways to verify whether a loss run is accurate. These methods combine direct confirmation, document comparison, and expert review.
The most reliable way to confirm a loss run's accuracy is to contact the insurance carrier that issued it. This involves requesting a copy directly from the carrier to compare with the submitted version.
Most carriers respond to verification requests within a few business days. Using secure email or carrier portals for these requests helps protect sensitive information. Including policy numbers, time frames, and insured contact information speeds up the process.
This step is particularly important when there are inconsistencies in the submission or when the loss history differs significantly from previous years.
Another effective approach is comparing the current loss run with previous submissions from the same insured. This helps identify changes in claim amounts, dates, or statuses that don't make sense.
Elements to track include:
Experienced underwriters can spot signs of manipulation that automated systems might miss. They examine formatting, language, claim sequencing, and information consistency across different sections of the document.
Reviewers also consider the context of the insured's industry, size, and operations to assess whether the claim history makes sense. For example, a high-risk business with no reported claims over several years raises questions.
When loss run fraud goes undetected, it creates several problems for insurance companies:
Financial impact: Policies may be underpriced based on false information, leading to higher-than-expected claims and reduced profits.
Compliance concerns: Regulators expect insurers to maintain accurate underwriting records. Using manipulated documents may raise questions during audits.
Portfolio performance: Over time, accepting fraudulent loss runs can skew risk models and create imbalanced books of business that are hard to correct.
Breaking up the review process into smaller steps helps catch fraud before it impacts your business. Creating clear procedures for verifying suspicious documents ensures consistent handling across all submissions.
Creating a secure workflow means organizing how loss run data is received, reviewed, and verified while keeping the process efficient for underwriters.
Loss runs come in many different formats depending on the issuing carrier. Standardizing these formats makes comparison and fraud detection easier.
This involves converting loss run data into a consistent structure, organizing key fields like claim amount, date, policy period, and loss type into uniform formats. Once standardized, the data can be analyzed more effectively.
Technologies like machine learning help extract and normalize data from various templates, scanned documents, or PDFs, making the process of getting loss runs from insurance carriers more efficient.
Real-time checks allow submission teams to spot potential fraud during the intake process. These include both automated tools and manual review steps at key points in the workflow.
Automated checks can scan for:
Manual verification steps can be added for flagged submissions. For example, if a document shows a sudden drop in claims or formatting differences, it can be routed to an underwriter for further review.
A collaborative approach involves multiple team members evaluating the same submission, often with different areas of focus. This brings different perspectives to the same data, helping catch issues a single reviewer might miss.
Collaborative reviews work best when there's a structured workflow where submissions flagged for potential issues are routed to a secondary reviewer. A shared system for logging observations and concerns creates a record of how decisions were made, which helps with audits and knowledge sharing.
Preventing loss run fraud requires a balanced approach combining technology and human expertise. The most effective strategies use automated tools to flag suspicious patterns while relying on experienced underwriters to interpret context and intent.
A systematic approach includes standardizing incoming data, automating anomaly detection, and setting up collaborative reviews. Modern underwriting platforms support these steps by organizing workflows, applying fraud checks consistently, and maintaining a secure environment for data processing.