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Motor Insurance Fraud Detection, Assessment and Intelligence

Public Short Course

HRD Training Prog No 10001712701
Date & Time 28 – 29 September 2026, 9.00 a.m. – 5.00 p.m.
Registration Deadline 17 September 2026 
Learning Format Physical

Overview

Fraud risks in motor claims continue to evolve from isolated opportunistic acts to increasingly organised and patterned activity. Building on core insurance and claims knowledge, this programme develops advanced capabilities in fraud risk evaluation, structured analysis, and defensible escalation decision-making for suspicious claims.

This programme is designed to build professional judgment and decision authority in fraud assessment, equipping participants to evaluate fraud risks, construct defensible fraud hypotheses, and determine appropriate escalation actions within the motor insurance environment.

Participants will learn how fraud exploits insurance processes, how suspicious indicators and linked patterns can be critically evaluated, how structured investigation tools support defensible fraud decisions, and how organised fraud activity can be assessed using network and intelligence techniques.

The programme extends traditional motor insurance knowledge into specialist fraud decision-making capability, enabling participants to justify fraud determinations, defend escalation decisions, and apply governance principles in complex and uncertain claim scenarios.

Recommended For

This programme is ideal for Claims Executives, Claims Investigators, Fraud Analysts and Claims Supervisors.

Learning Outcome

A. Evaluate fraud vulnerabilities, typologies and indicators to assess fraud risk

B. Construct defensible fraud hypotheses using structured fraud detection outputs

C. Evaluate and justify fraud positions and claim decisions using evidentiary and legal principles

D. Determine and defend governance-compliant referral and escalation decisions, including network-based risks.

E. Synthesize structured intelligence and professional judgment into defensible assessment and escalation decisions.

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Motor Insurance Fraud Detection, Assessment and Intelligence

For Inquiries

Sales

+603 2712 8882

Programme Structure

Day 1
Time Module Content
9.00 am –

1.00 pm

Module 1:

Insurance and Claims Foundations for Fraud Detection
  • Identify policy, coverage and process weaknesses that may lead to fraud
  • Review unusual claim values and loss patterns
  • Apply indemnity and valuation principles when assessing claims
  • Make claim decisions based on policy terms and evidence

Module 2:

Fraud Foundations and Taxonomy
  • Recognise common signs and behaviours linked to fraud
  • Understand how individuals or groups carry out fraud schemes
  • Group fraud cases into clear and practical categories
  • Use fraud categories to guide detection and further action
1.00 pm –

2.00 pm

 Lunch break
2.00 pm –

5.00 pm

Module 3:

Weak Spots and Fraud Typologies
  • Identify weaknesses that fraudsters may exploit
  • Use the 7 Weak Spots checklist to review fraud risks
  • Understand common fraud schemes, the people involved and their motives
  • Recognise when several warning signs appear together

Module 4:

Structured Fraud Detection and Investigation
  • Use red flags to identify claims requiring further review
  • Apply the CRASH–TEST checklist to examine suspicious claims
  • Find inconsistencies and unusual claim patterns
  • Develop a clear explanation of how fraud may have occurred

 

Day 2
Time Module Content
9.00 am –

1.00 pm

Module 5:

Clause Anchoring and Evidentiary Assessment
  • Match claim evidence to relevant policy terms and possible breaches
  • Assess whether the evidence is strong enough to support further action
  • Strengthen fraud reasoning by comparing evidence, policy terms and fraud indicators
  • Form a clear and supportable fraud conclusion

Module 6:

Referral and Escalation Governance
  • Decide when a suspicious case should be referred or escalated
  • Check whether enough information and evidence have been gathered
  • Prepare a clear and complete fraud referral
  • Explain and support the recommended action
1.00 pm –

2.00 pm

 Lunch break
2.00 pm –

5.00 pm

Module 7:

Fraud Case Assessment Lab
  • Review a complete claim using the CRASH–TEST checklist
  • Identify inconsistencies, warning signs and missing information
  • Decide whether the case requires fraud action
  • Make and explain an evidence-based claim decision

Module 8:

Fraud Linkage and Network Fraud Intelligence
  • Identify links between claims, people, vehicles and service providers
  • Distinguish isolated claims from coordinated fraud activity
  • Recognize unusual relationships and repeated patterns
  • Decide whether linked cases require further investigation or escalation

Dates

28/09/2026

2 Days (Physical)

Fees

Normal Fee: Individual

Aii Member: 
RM 2,100/USD 480

Non-Member:
RM 2,300/USD 525

Normal Fee: Group of 3 pax

Aii Member: 
RM 2,000/USD 455

Non-Member:
RM 2,200/USD 500

Meet The Expert

 

Jefferson Liwas

Fraud Intelligence & Network Analysis Specialist, ISM Insurance Services Malaysia Berhad

Jefferson is a motor insurance professional with more than 18 years of experience in claims operations, fraud detection and fraud analytics. He specialises in identifying organised motor fraud by analysing connections between claims, individuals, vehicles and service providers across multiple insurers and takaful operators.

 

At ISM Insurance Services Malaysia Berhad, he drives the industry-wide application of the Fraud Intelligence System (FIS) through training and collaboration. He develops motor fraud trend analyses on emerging syndicate activity. He also supports the industry Fraud Prevention Committee (FPC) in a secretariat role and led the development of key initiatives, including the Insurance & Takaful Anti-Fraud Conference and Anti-Fraud Excellence Programme in 2024.

 

He holds a Master of Business Administration (MBA) from Anglia Ruskin University. He has completed fraud examination training under the Association of Certified Fraud Examiners (ACFE) and is an HRD Corp Accredited Trainer.