Policy Fraud Detection
Many common types of fraud can only be detected by observing policies themselves. Insurers who can recognise unusual claim patterns, suspicious characteristics, and unexpected connections with other entities that indicate fraudulent behaviour by policyholders will be one step ahead in maintaining a healthy portfolio and minimising future risk.
Key Stats.
50%
Savings over 5 years
2.8
x
Conversion-rate increase
200
%
Average value of an investigation
How it works: Policy Fraud Detection
Our policy fraud detection module helps you identify and act upon these indicators by scoring policies. This module provides cutting-edge prediction tools, allowing you to find risky policies through a comprehensive rules engine and powerful predictive models enriched with information from external sources and our Fraud Intelligence and Link Analysis applications.
Making informed decisions
Identify serial claimants and analyse the sequence and timing of their claims. Catch ghost brokers by creating rules and models that compare policy details with those of claim participants. Use the advanced data matching capabilities of our detection suite to reveal duplicate policies. Inspect a policy’s relationships with associated addresses, people, and vehicles to prevent unauthorised business use.
Whether you choose to apply our state-of-the-art machine learning models to your data and leverage our in-house expertise, or prefer to rely on your own rules and referrals, our applications enable you to make fast, informed decisions about your policies.
Our policy fraud detection module is flexible and integrates smoothly with other applications, providing a robust system to tackle evolving insurance fraud.
Key Benefits
Advanced Policy Scoring
Apply comprehensive rules and predictive models to identify risky policies, leveraging enriched data from external sources and our fraud intelligence tools.
Serial Claimant Identification
Track and analyse the sequence and timing of claims to detect serial claimants.
Ghost Broker Detection
Create robust rules and models to compare policy details with claim participants, effectively catching ghost brokers.
Duplicate Policy Identification
Utilise advanced data matching capabilities to reveal duplicate policies, ensuring portfolio integrity.
Our Detect services
We employ an array of targeted techniques in our automated fraud prediction process. These include models trained on your data using the latest machine learning methodologies.
FAQs
How we use behavioural analytics and machine learning to identify policy level risk while supporting fair customer outcomes.
What is policy fraud detection?
Policy fraud detection focuses on suspicious behaviour at quote, application, inception and across the policy lifecycle such as misrepresentation, premium leakage, opportunistic abuse and organised activity.
How is policy fraud different from claims fraud?
Policy fraud often happens earlier (before a claim) and can involve misrepresented risk information, identity concerns or repeated suspicious behaviours across policies. Detecting it early protects pricing accuracy and reduces downstream claims losses.
Can policy fraud detection run in real time?
Yes. You can score applications or policy events in near real time to support referral, step-up verification or rules-based decisioning, depending on your appetite and operating model.
How do you avoid unfair outcomes for genuine customers?
We use explainable signals, cohort-based scoring and human review for significant decisions. Thresholds and rules can be tuned to reduce friction for low-risk customers while still catching high-risk behaviour.
What data is useful for policy fraud detection?
Typical inputs include application and policy fields, change history, prior loss and policy history, customer and address signals, and links across people/devices/locations where available.
How do we measure success for policy fraud detection?
Metrics often include premium leakage prevented, reduction in bad risks bound, referral hit rate, time-to-decision, and downstream impacts such as reduced fraud losses and improved portfolio quality.
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