Master’s thesis: Fraud detection in animal insurance

Are you interested in using data science, machine learning or statistics to tackle real-world challenges? Join our Claims Automation team for a master’s thesis exploring how to detect suspicious animal insurance claims, identify document anomalies and reduce incorrect reimbursements. We’ll shape the project together around your interests, available data and academic requirements.

More About The Opportunity

Help us reduce claim-cost leakage and improve claims automation. Lower leakage from incorrect reimbursements, duplicate claims, errors, and potential fraud helps us keep insurance premiums competitive for our customers.

You will join the Claims Automation team who work on automation and efficiency in the claims domain. We are looking for a student interested in data science, machine learning or statistics.

Estimate claim-cost leakage (Estimate the cost of undetected fraud and incorrectly reimbursed claims.)Detect suspicious claims (Identify claims that should be prioritized for additional manual review.)Detect document anomalies (Analyze invoices and receipts for duplicates, inconsistencies, or unusual patterns.)

Estimating Claim-cost leakage

Challenge

Known fraud and incorrect payments represent only the cases we have detected. The true cost of claim leakage is likely higher.

Objective

Develop a method for estimating lower and plausible upper bounds of claim-cost leakage, including potentially undetected fraud and incorrectly reimbursed claims.

Statistical estimation and uncertainty analysisPositive–unlabeled learningAudit- or sampling-based estimationAnalysis of historical claim and investigation outcomes

Expected outcome

An estimate of hidden claim-cost leakage, including assumptions, uncertainty, and possible breakdowns by claim type, customer group, or other relevant segments.

Suspicious claim detection

Challenge

Most claims are legitimate, but some contain unusual patterns that may justify additional review.

Objective

Develop and evaluate a model that identifies and prioritizes unusual or potentially suspicious claims.

Possible signals

Unusual claim frequency or claim amountsClaims shortly after policy inceptionRepeated or escalating claim patternsPatterns that differ from comparable customers or animalsClaims close to deductibles, limits, or other thresholds

Possible methods

Anomaly detectionStatistical peer-group comparisonSupervised or semi-supervised machine learningExplainable risk scoring

Expected outcome

A risk score or ranked review queue to support claims handlers in deciding which claims should receive additional manual review.

Anomaly detection of documents

Challenge

Claims are often supported by invoices and receipts that may be unclear, incomplete, duplicated, inconsistent, or unusual. We already use OCR in production to extract information from documents. There is an opportunity to build on this information to strengthen automated controls.

Objective

Investigate how extracted document data and document similarity analysis can identify anomalies and improve automated claims handling.

Possible areas

Detection of exact and near-duplicate invoices and receiptsUse of existing OCR-extracted fields, such as dates, amounts, clinic/supplier, invoice number, and line itemsDetection of document tampering and AI-generated documentsIdentification of documents that are not suitable for automated processing

Expected outcome

A prototype or framework for flagging documents for manual review due to inconsistencies, anomalies or potential fraud.

Pursuing a master’s degree in computer science, mathematics, statistics/quants, physics, or a related field.Has an interest in machine learning, AI or a related field and in exploring how machine learning models are developed and applied. An interest in Computer Vision, NLP or a related area is a plus.Has good knowledge of Python and experience with relevant frameworks and libraries, gained through coursework, projects or other experience.

We’d love to hear more about you! Apply today. We review applications on an ongoing basis and may fill the position before the application deadline.

Agria är ett helägt dotterbolag till Länsförsäkringar och finns idag i Sverige, Norge, Finland, Danmark, Storbritannien, Tyskland, Frankrike och Irland. Vårt svenska kontor ligger på Gärdet i Stockholm, men vårt hjärta och verksamhet sträcker sig långt utanför Sveriges gränser. Med en tydlig tillväxt agenda fortsätter vi att expandera internationellt, driva innovation och utveckla digitala lösningar som gör verklig skillnad för djurägare. Ett exempel är vårt dotterbolag Agria Vet Guide, som erbjuder digital rådgivning och gör det enklare att hjälpa fler djur snabbare.

Vi är marknadsledande i Norden inom djur- och husdjursförsäkringar, med en tydlig vision: att bli Europas mest omtyckta och pålitliga partner för djurägare. Vårt ledarskap handlar inte bara om marknadsandelar; det handlar om att skapa trygghet för både djur och människor, och att göra livet med djur enklare och roligare.