Fraud Prevention Analyst
| Posting date: | 28 January 2026 |
|---|---|
| Hours: | Full time |
| Closing date: | 27 February 2026 |
| Location: | Southend on Sea, SS0 0EJ |
| Company: | NatWest Group |
| Job type: | Permanent |
| Job reference: | R-00272064-OTHLOC-GBR-5FSOU044 |
Summary
Join us as a Fraud Prevention Analyst
- You’ll identify, assess, mitigate, monitor and report on fraud risk so we can manage any threat of fraud
- Importantly, you’ll also monitor and evaluate the performance of our fraud prevention processes and strategies
- This is a critical role where you’ll be responsible for promoting a culture that helps us manage fraud risk effectively within the business
What you'll do
In your new role, you’ll assess and understand external fraud risks associated with our business activities, while reviewing and developing processes to help mitigate those potential fraud risks.
You’ll also:
- Evaluate new data sources and integrate them into our existing strategies, to optimise the banks fraud prevention capabilities
- Support ongoing enhancement of the Fraud MI and reporting suite to develop and deliver accurate timely and meaningful, in-depth analysis that will identify existing and emerging fraud trends which will influence business decision making
- Maintain strong internal and external relationships with stakeholders and vendors, sharing information and data to enhance our fraud prevention capability
- Demonstrate subject matter expertise which will lend itself to the development of new products, systems and processes across the business
The skills you'll need
We’re looking for someone who has strong technical and numerical skills, with experience in using risk management tools and techniques including credit score systems, data modelling, data mining and behavioural scoring systems.
You’ll also have:
Experience in the application of risk management tools and techniques such as credit scoring systems. Statistical data modelling, data mining and behavioural scoring systems
Experience in applying statistical modelling and analysis techniques to the development of fraud risk prevention strategies
Proven numerical and technical skills, educated to degree level in a numeric discipline like Mathematics, Statistics or Operational Research
Strong database management skills and other programming languages
Proven record in the effective use and interpretation of management information