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⚡ Source: ReedRef: 56700226

Intelligence Analyst

Vermelo RPO·Manchester·Posted 1 months ago
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Job description

Original text imported from Reed

Intelligence Analyst

Location: Manchester/Stoke/Peterborough/Tunbridge Wells 

About the Role

We’re looking for a sharp, analytical Intelligence Analyst to join our Counter Fraud Department. In this role, you’ll support investigations into complex and emerging fraud threats, gather intelligence from multiple sources, and turn insights into actionable outcomes that strengthen our fraud prevention framework. You’ll play a key part in identifying fraud patterns, maintaining intel systems, and supporting the wider team in the fight against organised and application fraud.

What You’ll Do

  • Manage and support investigations into complex fraud rings and emerging threats.
  • Identify trends and analyse data using mining and fraud management software.
  • Maintain close working relationships within the Counter Fraud Department to share intelligence and ensure effective processes.
  • Lead proactive intelligence-gathering activity and produce detailed research reports.
  • Prepare and submit IFED referrals and contribute to Intelligence submissions to the Insurance Fraud Bureau (IFB).
  • Coach and mentor team members to maintain high standards and mitigate delivery risks.
  • Represent the team at industry intelligence forums to share insights and maintain up-to-date awareness of fraud trends.
  • Act as a key contact for all intelligence-related training and queries.

What You’ll Bring

  • Must Have:
  • Strong understanding of data security and confidentiality.
  • Solid computer literacy and data analytics capability.
  • Deep understanding of insurance and insurance fraud.
  • Knowledge of intelligence models and investigation techniques.
  • Familiarity with NIM rating guidance and intelligence reporting standards.
  • Preferred:
  • Knowledge of organisations and networks fighting insurance fraud (e.g., IFB, IFED).
  • Experience using fraud prevention systems, tools, and software.
  • A collaborative, proactive team approach and a passion for developing others.

Why Join Us

You’ll be part of a highly skilled team working at the forefront of fraud prevention — identifying patterns, closing gaps, and helping to protect our customers and business. This is a role for someone who thrives on solving problems, spotting the unseen, and making a real impact.

Ready to make a difference? Apply today and help us stay one step ahead of fraud.


SpeedCV AI

Key skills

AI-extracted from the job advert

Must-have skills
Data security and confidentialityData analyticsInsurance fraud knowledgeIntelligence modelsInvestigation techniquesNIM rating guidanceIntelligence reporting standards
Nice-to-have
IFB knowledgeIFED experienceFraud prevention systemsFraud management software
Soft skills
Analytical thinkingProblem solvingMentoringCollaborationCommunicationAttention to detailProactive approach
SpeedCV AI

Application advice

5 AI-generated recommendations to maximise your chances.

1

⭐ Highlight your insurance fraud experience prominently as this role specifically requires deep understanding of insurance fraud patterns

2

📊 Quantify your investigation results: "Identified 15 fraud rings, preventing £2.3M in false claims"

3

🔍 Emphasise any IFED or IFB experience as these are key stakeholders mentioned in the role

4

🛠️ List specific fraud management software and data mining tools you've used

5

🤝 Showcase mentoring experience as the role involves coaching team members and training delivery

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Suggested CV bullets

3 bullets our AI drafted for this specific advert, mirroring its ATS keywords.

How to tailor your CV

Add these 3 bullets under your most recent experience:

  • Identified 12 organised fraud rings through data mining analysis, preventing £1.8M in fraudulent insurance claims over 18 months
  • Prepared 45 IFED referrals and 20 IFB intelligence submissions, achieving 85% successful prosecution rate for complex fraud cases
  • Mentored 6 junior analysts in NIM rating guidance and investigation techniques, reducing case processing time by 25%

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Letter preview — tailored to Vermelo RPO

Dear Hiring Manager,

Vermelo RPO's Counter Fraud Department role combines my passion for intelligence analysis with my expertise in insurance fraud detection and IFED referral processes. My background in data mining and fraud pattern recognition aligns perfectly with your need for someone who can identify emerging threats and strengthen prevention frameworks.

My background in fraud investigation and intelligence gathering has equipped me with the analytical skills and industry knowledge essential for this position. I am particularly drawn to the collaborative aspects of the role and the opportunity to mentor team members whilst working with key industry bodies like the IFB.

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SpeedCV AI

Interview questions

10 questions generated from this advert.

Technical

  • How would you approach identifying patterns in a large dataset of insurance claims?
  • What fraud management software have you used and how did it support your investigations?
  • Explain the NIM rating system and how you would apply it to intelligence reporting
  • How would you structure an IFED referral for a complex fraud case?
  • What data mining techniques would you use to detect emerging fraud threats?

Behavioural

  • Tell me about a time you identified a fraud pattern others had missed
  • Describe a situation where you had to mentor a colleague through a complex investigation
  • How do you handle confidential information when working with multiple stakeholders?
  • Give an example of when you had to present complex fraud intelligence to senior management
  • Tell me about a time you collaborated with external organisations on a fraud case
SpeedCV AINEW

STAR answer examples

Model answers using the Situation-Task-Action-Result framework. Adapt to your own experience.

1Question

Tell me about a time you identified a fraud pattern others had missed

While reviewing 300 motor insurance claims over 3 months, I noticed subtle patterns in repair shop locations and claim timings that others had overlooked. I cross-referenced the data using fraud management software and discovered 8 seemingly unrelated claims were actually part of an organised ring. I documented my findings using NIM rating guidance and prepared a detailed intelligence report. This led to IFED referrals for all cases and prevented an estimated £450,000 in fraudulent payouts. The pattern recognition techniques I developed were subsequently adopted by the wider team.
2Question

Describe a situation where you had to mentor a colleague through a complex investigation

A junior analyst was struggling with a multi-layered application fraud case involving 15 suspicious claims. I worked closely with them over 4 weeks, teaching them how to use data mining tools effectively and apply intelligence models systematically. I showed them how to structure their research reports and prepare IFED referrals to professional standards. By the end of the mentoring period, they had successfully identified 3 additional linked cases and improved their investigation completion rate by 40%. They now regularly mentor new team members using the same structured approach.

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