The role of noise in decision-making for insurance claims professionals

In the insurance industry, claims professionals are responsible for assessing and settling policyholders' claims. An essential aspect of this process is decision-making, which can be affected by a variety of factors. One such factor is noise, which refers to the variability in decisions that cannot be attributed to differences in cases or factual information.

In this article, we will explore the concept of noise in the decision-making of insurance claims professionals and discuss the impact it has on the industry.

Understanding noise in decision-making

Noise is different from bias, which stems from systematic errors or personal prejudices. Instead, noise is the inconsistency that occurs when different claims professionals, or even the same professional at different times, make different decisions for similar cases.

Noise can arise from various sources, such as cognitive limitations, emotional influences, and environmental factors. For insurance claims professionals, noise can manifest as inconsistencies in evaluating claims, determining coverage, or setting reserves, ultimately affecting the quality and fairness of claim settlements.

Implications of noise for insurance claims professionals

  • Financial consequences: Noise can lead to overpayment or underpayment of claims, resulting in financial losses for both insurance companies and policyholders. Overpayments may cause higher premiums and affect the insurer's profitability, while underpayments can lead to dissatisfied policyholders and reputational damage.
  • Inconsistency and fairness: When noise leads to different claim outcomes for similar cases, it raises questions about the fairness and consistency of the decision-making process. This can erode trust in the insurance system and damage the relationship between insurers and policyholders. Distrust is identified as a breeding ground for fraudulent claims, as identified in the Insurance Fraud Taskforce Report (2016). I have explored this a little further below.
  • Claims leakage: Inconsistent decision-making can result in legal disputes (resulting in litigation leakage) and potential regulatory penalties (as a consequence of failing to treat customers fairly). Policyholders may challenge claim denials or settlement amounts, leading to costly litigation and regulatory scrutiny.

Focus on noise in decision-making and insurance claims fraud

Noise (the variability in decisions that cannot be attributed to differences in cases or factual information) can lead to inconsistencies in evaluating claims, determining coverage, or setting reserves creating an environment that facilitates fraudulent behaviour and hampers efforts to detect and prevent fraud several ways:

  1. Inaccurate assessments: Noise can result in inaccurate assessments of claims, leading to overpayment or underpayment. Fraudsters may take advantage of these inconsistencies to exploit the system, capitalising on the confusion created by noise to submit inflated or fabricated claims.
  2. Erosion of trust: As noise leads to inconsistencies and unfair treatment in the claims handling process, it erodes trust between insurers and policyholders. This lack of trust may cause some policyholders to believe that they need to embellish or misrepresent their claims to ensure fair compensation, ultimately encouraging fraudulent behaviour.
  3. Reduced fraud detection: The presence of noise in decision-making may make it more challenging for insurers to detect fraudulent claims effectively. Inconsistencies in decision-making can create a 'background noise' that makes it harder to identify and isolate suspicious patterns, reducing the effectiveness of anti-fraud measures and allowing fraudulent claims to slip through.
  4. Strained resources: Decision-making noise can lead to increased costs and resource allocation for insurers as they attempt to rectify inaccurate assessments, handle disputes, and manage litigation. This strain on resources may reduce the focus on fraud detection and prevention efforts, providing opportunities for fraudsters to take advantage.
  5. Ineffective data sharing: Noise can undermine the effectiveness of data sharing and collaboration between insurers to combat fraud. Inconsistencies in decision-making may create unreliable data, which, when shared between insurers, can hinder efforts to identify and tackle fraudulent activities collectively.

Mitigating noise in insurance claims decision-making

To address the issue of noise, organisations can implement several strategies:

  1. Standardised processes: Establishing clear guidelines and standardised processes for evaluating claims can help reduce variability in decision-making. This includes checklists, structured interviews, and well-defined criteria for claim assessment.
  2. Training and education: Regular training and education can help claims professionals develop a deeper understanding of the complexities involved in claim evaluation, enabling them to make more consistent decisions.
  3. Decision support tools: Leveraging technology, such as artificial intelligence and machine learning, can provide decision support to claims professionals. These tools can help identify patterns and trends, minimise human error, and ensure that similar cases are treated consistently.
  4. Periodic reviews: Regular audits and peer reviews can identify inconsistencies and potential noise in the decision-making process, providing opportunities for improvement and feedback.

Insurers already put in place many of these mitigations. Our work at Kennedys and Kennedys IQ with the insurance industry supports efforts to reduce noise and the impact of it.

We believe that measuring noise is vital. It is always easier to tackle a problem you can see. By undertaking Noise Audits of decision-making behaviours across large groups of professionals we can start to see where noise exists most, and baseline the problem in order to measure the effectiveness of the mitigations.

The development of decision support technologies is a key area of focus for us. Our work combines the expertise of legal and claims professionals with leading machine learning capabilities built around the evidential reasoning methodology. The IQ Platform includes features that support decision making in fraud and quantum assessment with demonstrable and proven success in improving fraud detection efficacy and reducing noise and time taken by handlers to assess medical evidence and forecast the outcome of a claim. Features currently in live testing include decision support for liability decisions and policy interpretation.


Noise is an often overlooked but critical factor in the decision-making process of insurance claims professionals. By understanding where noise is and its implications, insurers can take steps to mitigate its impact and enhance the consistency, fairness, and efficiency of the claims process.

Insurers already do a lot right. Measurement and decision support technologies can create additional value for insurers, claims professionals and customers.

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