Claims Fraud Detection
Fraudsters never stand still.
Their methods are constantly evolving. Insurance claims are subject to a variety of fraud risks, including inception fraud, staged events, exaggeration, and non-disclosure.
Key Stats.
97
%+
Claims risk assessed throughout life cycle
30
%+
Increase in retention rate for claims identified as high risk
80
%
Conversion rate for fast tracked claims
How it works: A comprehensive approach
Detecting these types of fraudulent behaviour requires a comprehensive approach before any claim is paid. Our fraud detection modules are designed to continuously enhance your ability to identify fraud risks and minimise wasteful false positives.
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, text mining, fraud profiles, anomaly detection, and geospatial enrichment. Additionally, any risks highlighted by our Fraud Intelligence and Link Analysis applications are seamlessly integrated into the process.
Our rules engine
Our rules engine is flexible, allowing you to incorporate your own model outputs and internal referrals, or build rules based on your knowledge and expertise. Either way, we ensure you have all the information you need at your fingertips with our visualisation and business intelligence modules. Our simulation module also lets you compare current results with candidate rule sets, ensuring you have the optimal setup for your needs.
Like all our applications, our case management modules can be deployed independently or as an integrated part of our broader solution, providing flexibility and scalability to meet your needs.
Key Benefits
Advanced Fraud Detection
Utilise machine learning, text mining, anomaly detection, and more to stay ahead of evolving fraud tactics.
Customisable Rules Engine
Incorporate your own models and referrals, or build custom rules to suit your expertise and specific needs.
Comprehensive Visualisation
Access all necessary information quickly and easily through our advanced visualization and business intelligence modules.
Continuous Improvement
Use our simulation module to test and compare rule sets, ensuring your fraud detection system is always optimised.
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 our detection capabilities work in practice and how they fit into real-world operations.
How does claims fraud detection work in kbs Intelligence?
Claims are segmented into relevant segments (for example by product, channel or claim type) and scored using a combination of rules and predictive models. Referrals are created when scores exceed configurable thresholds or specific rules fire.
Are all claims scored in the same way?
No. Scoring is tailored by segment so that risk is assessed fairly and accurately for different claim types and customer segments, rather than applying a single blanket model to everything.
How does the scoring engine decide which claims are referred?
You set thresholds, rules and materiality criteria. When a claim’s risk score or rule triggers exceed those settings, the claim is automatically flagged and routed to the right team for assessment.
Can claims handlers create manual referrals?
Yes. Assessors and handlers can manually refer a claim when they spot something unusual. The platform tracks those referrals alongside automated alerts so you have one consistent workflow.
Can investigators see why a claim was flagged?
Yes. Each referral can include reason codes and supporting context such as key features, linked entities, and any relevant document or image checks, so analysts can validate quickly.
What’s the typical implementation timeline for claims fraud detection?
Many insurers start with an initial pilot using available data, then expand into production workflows. Timeline depends on data availability, integration needs and the level of automation you want in triage and case creation.
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