UK

United Kingdom · Technical roles · Entry Level (0-2 years)

Associate Fraud Detection Engineer

Here is the whole job, in plain words. What it is, a real day, what you decide, how you're judged, how people get here and where they go next. Then the part no course gives you: twelve AI tutors who learn your work.

  • Experience bandEntry Level (0-2 years)
  • Direct reportsNo direct reports
  • Reports toFraud Detection Engineer
  • UK framework levelUsually someone starting out, or keeping a process running

Also advertised as Junior Fraud Engineer · Fraud Analyst (Engineering Focus) · Entry-Level Fraud Detection Specialist

Built on an analysis of 43,079 real UK job descriptions · grounded in qualifications employers recognise

Start with a free Future Fluency check, tuned to Associate Fraud Detection Engineer

Ten quick questions, one per Future Fluency, asked against this role rather than a generic one. About five minutes, and no card.

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1What this role really is

This role is all about getting your hands dirty with data and code to help us stop fraudsters in their tracks. You'll be working closely with more experienced engineers, learning the ropes of how we build and maintain our fraud detection systems. Think of it as your apprenticeship in the high-stakes world of digital defence. You'll be contributing to real projects from day one, helping to protect our customers and our bottom line.

2What you'd actually use

The tools this job runs on, and how well you'd need to know each one.

Python (pandas, scikit-learn)Intermediate

Writing scripts for data cleaning, feature extraction, and running basic model training experiments on pre-processed datasets. You'll be using existing libraries extensively.

SQL (PostgreSQL, MySQL)Intermediate

Writing complex queries to extract data from various production databases and data warehouses (Snowflake, BigQuery) for analysis and feature engineering.

Apache KafkaBasic

Consuming data from existing Kafka topics for analysis. You'll learn how our real-time data streams work and how to access them.

Databricks or KubeflowBasic

Running predefined jobs, training models using existing notebooks, and managing experiments within our ML platform. You'll learn the platform's interface.

Grafana and TableauBasic

Monitoring existing dashboards for fraud trends and system health. You'll learn to navigate, filter, and interpret visual data to spot anomalies.

Rule Engines (e.g., Drools, Sift, Feedzai)Basic

Modifying and testing existing rules under supervision. You'll learn the syntax and logic of our chosen rule engine.

3What you get to decide, and how that grows

Power in a job isn't your title. It's what you're allowed to decide. Here's how it grows as you move up.

The choiceComing inWhere you are nowThe step above
Technical Approach for a New FeaturePropose options, but the final decision rests with your mentor/senior engineer. You'll be asked to explain your reasoning.Decide on the approach for routine features, consulting with a senior engineer for complex or novel problems.Design and decide on the technical approach for entire workstreams, seeking input from architects on broader system implications.
Modifying a Production Fraud RuleImplement changes only under direct supervision and after thorough testing, with final approval from a senior engineer or manager.Design, implement, and test routine rule modifications, with final approval from a senior engineer or manager.Design, implement, and deploy complex rule sets independently, with peer review and manager notification.
Data Sharing with External PartiesAbsolutely no independent sharing. All requests must be escalated to your manager, who will follow established protocols.Escalate all requests to manager; may assist manager in preparing data under strict guidelines.Advise on appropriate data sharing protocols and prepare anonymised/aggregated data for approved external requests, with manager approval.
Budget Allocation for Tools/SoftwareNo authority. You can suggest tools to your manager for consideration, but you won't be approving purchases.Suggest tools up to £1K for manager approval.Recommend and justify tool purchases up to £5K for manager approval, within existing budget.

4How you'll be judged

The scoreboard, honestly: the hard targets, how often each one is actually looked at, and the quiet human signals that never make it onto a dashboard.

Task Completion Rate
The percentage of assigned small tasks (e.g., data pulls, script modifications, bug fixes) completed within the agreed timeframe.
Target · 90%+

If you're assigned 10 small data cleaning tasks in a week, you should aim to finish at least 9 of them on time. We're not expecting miracles, just steady progress.

Code Review Feedback Incorporation
The average number of iterations required to get your code approved after initial submission, reflecting how well you're learning from feedback.
Target · 2 iterations or fewer

You submit a new feature. Your mentor gives you 3 points of feedback. You address them, resubmit, and it's approved. That's one iteration. We're looking for that kind of quick learning.

Documentation Contribution
The number of new or updated internal documentation pages, runbooks, or code comments you contribute.
Target · 2-3 contributions per month

After fixing a bug, you update the relevant runbook with the solution. Or you add clear comments to a complex script you've been working on. It's about making things easier for the next person.

Alert Investigation Support
The number of fraud alerts you assist the Risk Operations team with, by providing requested data or initial analysis.
Target · 5-10 alerts per week

Risk Ops flags a suspicious transaction. You're asked to pull all associated user activity from the database. You get that data to them quickly and accurately.

Proactive Learning & Curiosity
How actively you seek to understand our systems, ask thoughtful questions, and explore new tools or techniques.
  • You'll ask 'why' a lot, not just 'how'. You'll show up to team meetings with questions about recent fraud trends. You might even tinker with a new library in your spare time and share what you've found.
Collaboration & Teamwork
Your willingness to help out team members, participate in discussions, and respond constructively to feedback.
  • You're responsive to messages from colleagues. You offer to help a peer who's stuck (if you can). You take code review feedback on the chin and use it to improve, rather than getting defensive.
Attention to Detail in Code & Data
The care you take to ensure your code is clean, well-tested, and that your data analysis is accurate.
  • Your code passes basic linting checks. You spot minor inconsistencies in data before your mentor does. You double-check your SQL queries before running them on production data (always a good habit!).
Problem-Solving Approach
Your ability to break down a problem, identify potential solutions, and know when to ask for help.
  • When faced with a bug, you don't immediately ask for the answer. You'll describe what you've tried, what you think the issue is, and then ask for guidance. You're not afraid to admit you're stuck, which is crucial.

5Would you like it

The honest version. What people enjoy, and what grinds them down.

What people enjoy
Solving Complex Puzzles

You love digging into messy data, trying to find the hidden patterns that fraudsters are using. Every new fraud attempt is a puzzle you're eager to help solve.

Spending an afternoon sifting through log files to understand why a specific type of transaction is suddenly failing, and then figuring out the underlying fraud signature.

Making a Tangible Impact

You want to see your work directly protect the business and its customers. You get a buzz from knowing your code is actively preventing losses.

Seeing a graph showing a drop in chargebacks after a rule you helped implement went live, knowing you contributed to that saving.

Continuous Learning & Growth

You're always keen to pick up new programming languages, learn about new fraud techniques, or understand how a different part of the system works. You thrive on expanding your knowledge.

Voluntarily taking an online course in graph databases because you're curious how they could help us detect fraud rings, even if it's not directly assigned.

What frustrates people
  • Spending days cleaning data that should have been clean in the first place.
  • The constant cat-and-mouse game – building a defence only for fraudsters to find a new loophole.
  • Having to explain basic technical concepts repeatedly to non-technical teams.
  • Working on a solution that gets deprioritised or scrapped due to shifting business priorities.
  • The occasional 2 AM alert when a major fraud attack is underway – it's part of the job, but it's tough.
What this role does not give you
  • A predictable, 9-to-5 routine with no urgent interruptions.
  • A role where you're always building brand-new, glamorous features.
  • Complete control over the data quality you receive from upstream systems.
  • A static environment where you can master one thing and then relax.

6Who you work with

This role directly contributes to the financial security of the organisation by supporting the development of systems that prevent monetary losses from fraud. Your work helps maintain customer trust and ensures compliance with financial regulations, which is pretty critical, honestly.

Inside the business
  • Fraud Detection Engineers (your mentors)
  • Data Scientists (who build the models)
  • Risk Operations Team (who action the alerts)
  • Backend Engineering Teams (who provide the data)
Outside the business
  • Payment Processors (e.g., Visa, Mastercard)
  • Cybersecurity Vendors (where we integrate tools)

7What you need before you start

Not a wish list. The things you would be expected to already have.

  • A foundational understanding of computer science principles (data structures, algorithms).
  • Experience with at least one programming language, preferably Python, for data manipulation and scripting.
  • Basic SQL skills for querying databases.
  • A genuine curiosity about fraud and a desire to learn how to fight it.
  • Strong problem-solving skills and the ability to break down complex issues.
  • Excellent written and verbal communication skills; you'll need to explain your work and ask clear questions.

8What to practise next

Where the job is going, and what to do about it starting this week.

Advanced SQL & Data Warehousing

As our data grows and our fraud detection becomes more sophisticated, you'll need to extract more complex insights from our data warehouses. This means mastering advanced SQL and understanding data modelling principles.

Window Functions · Common Table Expressions (CTEs) · Data Modelling Principles · Query Optimisation

  • This month: Focus on mastering all types of JOINs and aggregate functions in SQL.
  • Month 2: Start experimenting with window functions on our historical data.
  • Month 3: Take an online course on data warehousing fundamentals and star/snowflake schemas.
  • Month 4: Propose a more optimised version of an existing SQL query used by the team.

Quick win: Challenge yourself to rewrite an existing complex SQL query into a more readable or performant version using CTEs. Ask a senior engineer for feedback.

Real-Time Data Processing Fundamentals

Fraud happens in real-time. Our defences need to be just as fast. You'll need to understand how our streaming data pipelines work to contribute effectively to real-time fraud detection.

Kafka Producers & Consumers · Stream Processing Concepts (Basic) · Latency & Throughput · Idempotency

  • This month: Read the official Kafka documentation on producers and consumers.
  • Month 2: Set up a local Kafka instance and experiment with sending and receiving messages.
  • Month 3: Work with a senior engineer to understand one of our existing Spark Streaming or Flink jobs.
  • Month 4: Propose a small feature that could use real-time data from Kafka.

Quick win: Spend an hour exploring our internal Kafka topics using a tool like Kafka Tool or a simple Python consumer. Just observe the data flowing through.

9Staying current once you are in

What people here do to keep up
  • Participate in online coding challenges (e.g., LeetCode, HackerRank) to sharpen your problem-solving and algorithm skills.
  • Contribute to open-source projects, especially those related to data science, security, or payments.
  • Attend industry webinars or virtual conferences on fraud detection, cybersecurity, or data engineering.
  • Read relevant blogs and research papers to stay current on new fraud techniques and detection methods.
  • Build personal projects that involve data analysis, machine learning, or API integrations to demonstrate your practical skills.

10How the AI economy is changing work like this

Before we ask anything of you, here's what we can already say about AI and work of this kind:

The new skill this role is being asked for: Prompt Engineering & LLM Integration

Large Language Models (LLMs) are already changing how engineers work, from generating code to summarising complex data. Fraudsters are also using them. You'll need to know how to use them effectively for defence and understand their limitations.

We'll only ever tell you what we can actually back up. No hype, no scare tactics.

Your PlanIllustration

Built for Associate Fraud Detection Engineer

4 units that map to this job, from the qualifications that cover it.

  1. Machine Learning Methods and Models in Data ScienceQualifi Ltd · covers 1 of 1 standardsLevel 3
  2. Machine Learning AlgorithmsOCN London · covers 1 of 1 standardsLevel 5
  3. Machine LearningQualifi Ltd · covers 1 of 1 standardsLevel 7
  4. Data Analytics and Machine LearningATHE Ltd · covers 1 of 1 standardsLevel 5
These are the real units behind this job, in the order they rank for it. Nothing here is marked done, because this plan has not been started by anyone yet. Yours would fill in as you go.

The rising capability

Zavmo analysis

What's rising in its place

This is where the work is heading, and the higher pay with it. Get fluent here and the shift stops being a threat and starts being your edge.

Prompt Engineering & LLM Integration

Large Language Models (LLMs) are already changing how engineers work, from generating code to summarising complex data. Fraudsters are also using them. You'll need to know how to use them effectively for defence and understand their limitations.

  • Context Windows & Token Limits
  • Temperature Settings
  • RAG Architectures (Basic)
  • Output Validation & Hallucination Detection

What you’ll use

Skills this role draws on

Technical

  • Adversarial Feature Engineering (Basic Understanding)
  • Anomaly Detection Concepts
  • Graph-Based Network Analysis (Conceptual)
  • Model Explainability (Interpretation)
  • Real-Time Model Deployment (Observational)
  • Identity & Trust Signals (Foundational)

The pathway

How you actually get there, here

How you become one varies far more by country than what one does. This is the UK route. Most people take one of these ways in; the right one depends on where you're starting from.

  1. 1

    Graduate / Junior Software Engineer

    0-1 year post-graduation

    Skills to master

    • Solid Python/SQL, understanding of software development lifecycle, basic data manipulation, version control (Git).

    You're ready to move on when

    • Can independently write clean, functional code for small tasks.
    • Understands basic data structures and algorithms.
    • Comfortable using Git for collaboration.
    • Shows a strong desire to specialise in data and security.
  2. 2

    Data Analyst (with coding skills)

    1-2 years experience

    Skills to master

    • Advanced SQL, Python for data analysis (pandas), data visualisation, understanding of business metrics, curiosity about underlying data systems.

    You're ready to move on when

    • Can extract and analyse complex datasets independently.
    • Identifies data quality issues and proposes solutions.
    • Has a strong interest in moving from descriptive analysis to predictive modelling and system building.
    • Has started experimenting with basic machine learning concepts.
  3. 3

    Cybersecurity Analyst (with programming interest)

    1-2 years experience

    Skills to master

    • Understanding of security principles, threat intelligence, incident response, network fundamentals, scripting for automation (Python).

    You're ready to move on when

    • Has experience investigating security incidents or anomalies.
    • Understands common attack vectors and mitigation strategies.
    • Can write scripts to automate security tasks.
    • Wants to apply engineering principles to proactive threat detection.

11Where this role leads

The long view:Your journey starts here, but where it goes is largely up to you. We're committed to providing the opportunities and support for you to build a truly impactful and rewarding career in technical roles.

Pay & demand

Pay and demand for this role will appear here, each figure traced to a named authoritative source (e.g. the ONS Annual Survey of Hours and Earnings, under the Open Government Licence). We don’t show numbers we can’t attribute.

The ten Future Fluencies

Zavmo analysis

The credential is what you can do today. These are what keep you valuable.

A qualification proves you can do the job as it's defined today. These ten are what decide whether you're still the obvious person for it in five years. They're the capabilities employers are now writing into senior roles faster than people are learning them. Zavmo weaves them through whatever you study, so you come out with both: the credential and the fluency.

The highlighted ones are the Fluencies your role leans on hardest, from how Associate Fraud Detection Engineer is actually changing. In about two minutes, the free confidence check asks where you stand on each of the ten. That's the whole check, and it's what makes the plan yours rather than generic.

Where it leads

Roles this leads to

12The team that's yours

No two people are taught the same way. This is one-to-one, not one-to-many.

Zavmo is a hyper-personalised AI learning platform. Twelve virtual tutors, each with a different way of teaching, and one orchestration agent that picks the right one for the moment. So every single lesson is shaped around you, your role, and the way you learn. Not a course everyone sits through. A conversation built for you, and no one else.

…and nine more, matched to you after your first chat. Meet all twelve

Start free and meet yours

13What it feels like

A conversation, not a course

Because your tutor knows your role, your projects and your last session, learning sounds like this. And it's different for every single person:

Machine Learning Methods and Models in Data ScienceLevel 3

Applied to your work in Associate Fraud Detection Engineer

The objective of this unit is to provide learners with a foundational understanding of machine learning methods and models used in data science. Learners will gain knowledge of supervised, unsupervised, and reinforcement learning, including their applications and key characteristics.

How the thinking builds
  1. Remember
  2. Understand
  3. Apply
  4. Analyse
  5. Evaluate
  6. Create
An illustration of a Zavmo lesson, built from this role’s own route. The unit, its objective and every criterion above are the awarding body’s own words, not an example.

One to one, not one to many

No two people run this the same way

A course is written once and handed to everyone. This is assembled around you, and keeps changing as it learns you. Five things it reads, and what each one changes.

  1. Your actual work Every lesson is taught against a live piece of your own work, not a worked example from a textbook.
  2. What you already know The first conversation finds your starting point, so you skip what you can already do and spend the time on what you cannot.
  3. The conditions you learn under Not a learning-styles quiz. The evidence does not support those. The dimensions the research does back, read once and used to shape the plan.
  4. How far you got last time It picks up mid-thought. The tutor knows what you said, what you struggled with, and what it asked you to try.
  5. Which tutor suits the moment Twelve of them, each for a different kind of thinking. The one who walks you through a first idea is not the one who stress-tests it.

See how you learn, free. Eight questions, no sign-up. A directional taster; the diagnostic inside Zavmo goes deeper and keeps adapting.

DemonstrateIllustration

Evidenced on your work in Associate Fraud Detection Engineer

You do not finish by watching something. You finish by showing it on the work you already do, against the measures this job is judged on.

  • Task Completion RateThe percentage of assigned small tasks (e.g., data pulls, script modifications, bug fixes) completed within the agreed timeframe.If you're assigned 10 small data cleaning tasks in a week, you should aim to finish at least 9 of them on time. We're not expecting miracles, just steady progress.90%+
  • Code Review Feedback IncorporationThe average number of iterations required to get your code approved after initial submission, reflecting how well you're learning from feedback.You submit a new feature. Your mentor gives you 3 points of feedback. You address them, resubmit, and it's approved. That's one iteration. We're looking for that kind of quick learning.2 iterations or fewer
  • Documentation ContributionThe number of new or updated internal documentation pages, runbooks, or code comments you contribute.After fixing a bug, you update the relevant runbook with the solution. Or you add clear comments to a complex script you've been working on. It's about making things easier for the next person.2-3 contributions per month
  • Alert Investigation SupportThe number of fraud alerts you assist the Risk Operations team with, by providing requested data or initial analysis.Risk Ops flags a suspicious transaction. You're asked to pull all associated user activity from the database. You get that data to them quickly and accurately.5-10 alerts per week
These are this job's own measures, with its own targets. Nothing is marked evidenced, because nobody has started this yet. Yours would fill in from the work you bring.

Your passport

This isn't a certificate you file away. It's a passport to the life you're designing.

Every credit you earn and every fluency you build adds up: evidence where it counts, carried with you. Zavmo keeps the map: where you are, where you're heading, and the next step, at your pace, around your life. From Associate Fraud Detection Engineer to Fraud Detection Engineer (L2), and whatever you decide comes after.

Level 2 · in progressAI Fluency→ Fraud Detection Engineer (L2)→ your design
Where this takes you

Your journey starts here, but where it goes is largely up to you. We're committed to providing the opportunities and support for you to build a truly impactful and rewarding career in technical roles.

See Your Progress GrowIllustration
Associate Fraud Detection Engineer
  • Adversarial Feature Engineering (Basic Understanding)
  • Anomaly Detection Concepts
  • Graph-Based Network Analysis (Conceptual)
  • Model Explainability (Interpretation)
  • Real-Time Model Deployment (Observational)
  • Identity & Trust Signals (Foundational)
This is your Mind Palace on learn.zavmo.ai. Every skill above comes from this role's own record, not an example borrowed from another job. A node lights up when you evidence it, and what you build stays yours between jobs. That is the part a course cannot do.

14The detail, folded away

Everything else the record holds

The career branches in full, how AI is already showing up in the day-to-day, and the questions people ask about this job. Here when you want them, out of the way while you decide.

Where it leads next, rung by rung

Where it leads

The career path, and where it branches

Associate Fraud Detection Engineer is a start, not a ceiling. Each step below asks for new skills and hands back more autonomy.

  1. Fraud Detection Engineer (L2)

    2-3 years in Associate role

    From executing tasks to owning features and small projects.

    • Designing and building new real-time data processing pipelines (Kafka Streams, Spark Streaming).
    • Independently designing and deploying fraud detection features or simple models.
    • Optimising SQL queries and data extraction processes for performance.
    • Contributing new features to the central feature store (Feast/Tecton).
Working with AI on the job

Working with AI

Where AI is starting to help

We're not just talking about the future; we're using AI *today* to make our fraud detection engineers more productive, more effective, and frankly, less bogged down by tedious tasks. For an Associate, this means you'll be learning with the best tools from day one.

Imagine having a super-smart assistant that helps you write code, spot hidden patterns, and even explain complex model decisions. That's the reality here. We're integrating AI into our daily workflows to amplify your impact, allowing you to focus on the truly interesting, high-value problems of stopping fraudsters.

Code Automation & Debugging

Use AI coding assistants (like GitHub Copilot) to suggest Python and SQL code snippets, automatically generate test cases, and even help you debug tricky issues. This means less time wrestling with syntax and more time understanding the logic behind the fraud.

Anomaly Pattern Suggestion

Our internal AI tools continuously scan vast amounts of transactional data. They'll proactively highlight unusual clusters or subtle correlations that could point to new fraud patterns, giving you a head start on investigations without hours of manual digging.

Smart Documentation & Summaries

Generate clear summaries of complex technical documents, automatically create user-friendly explanations for model decisions (using XAI outputs), and even draft initial versions of internal reports. This cuts down on the 'boring but necessary' admin work.

Data Exploration & Feature Ideas

An AI assistant can help you explore new datasets by suggesting potential features or transformations that might be predictive of fraud. It's like having a brainstorming partner that never runs out of ideas, helping you learn faster.

Common questions

Common questions

How do you become an Associate Fraud Detection Engineer?

Common routes in include Graduate / Junior Software Engineer (0-1 year post-graduation), Data Analyst (with coding skills) (1-2 years experience) and Cybersecurity Analyst (with programming interest) (1-2 years experience). Times vary with prior experience.

Where can an Associate Fraud Detection Engineer progress to?

This role can lead on to Fraud Detection Engineer (L2) (2-3 years in Associate role), depending on the skills you build.

What level is an Associate Fraud Detection Engineer in the UK?

This role aligns to RQF Level 2 on the UK framework, a guide to the depth of qualification it maps to, not a hard entry bar.

What new skills matter most for an Associate Fraud Detection Engineer?

Increasingly, Prompt Engineering & LLM Integration. These are the areas where the higher-paid, future-proof work is heading.

The honest bit

You’ve started things before

Most of them were built for a room full of people who aren’t you. A cohort moves on whether or not your week allowed it, and by the third week the thing you’re behind on becomes the reason you stop opening it.

There’s no cohort here, and no timetable to fall behind. Before anything starts, Zavmo asks when you’re sharpest and how long you can realistically sit down for, then builds the sessions around those answers. A bad fortnight changes your pace. It doesn’t put you behind.

And you only pay once you start learning. Searching and planning are free, and you can cancel any time — so the cost of finding out is an afternoon, not a year.

What it costs

Less than one coaching session. Every month.

A single career-coaching hour costs more than a month of this, and it ends when the hour does. Zavmo doesn't. It's £70 a month, about £2.30 a day, for a companion that knows an Associate Fraud Detection Engineer, works on the job you actually do, and keeps going at your pace rather than a timetable's.

  • Searching and planning stay free. You only pay when you start learning.
  • Your credits are yours. Regulated, and they don't vanish when a subscription ends.
  • Cancel any time and billing stops. No notice period, no minimum term.

Your path, personalised

You have the map. Walking it is the part we do together.

This route runs to 1 national skill standard. That is a real journey.

Zavmo shapes a learning experience as unique as you are. It fits how you learn, your pace and the work you already do. Every step stays benchmarked to recognised national standards. That’s the plan for becoming an Associate Fraud Detection Engineer: personal to you, and it still counts. The first steps are free.

Independent research finds well-designed intelligent tutoring performs nearly as well as one-to-one human tutoring: VanLehn (2011), Educational Psychologist.

A private tutor in the UK averages £35–40 an hour . Zavmo is £70/month.

A real plan on learn.zavmo.ai: Ofqual-regulated units, credits, and a three-month run at your own pace.
Start free No commitment. See your first steps free.

15Where to go from here

Other roles at Level 2

Same depth of qualification, different job. Useful if the work appeals but this particular role does not.

Other roles in Technical roles

Stay in the field you know and move sideways rather than up.

If you leave this industry

The skills you'll gain here – advanced data engineering, real-time systems, machine learning for security, and adversarial thinking – are highly transferable. You could move into broader cybersecurity engineering, data science roles in other industries, or even specialise in areas like risk engineering or payment platforms.

Not sure this is the right direction?

Work out what you actually want from work first, then come back and see which roles fit it. Takes about ten minutes.

This role profile is © 2026Growth Engineering Technologies Ltd. Built from UK occupational standards and regulated qualification data, and written for Zavmo.

You're not behind. You're right on time. The shift is only just beginning. Your role won't look the same in two years. Be the one who leads the change, not the one it happens to. Build my plan, free Here's the first ten minutes: a 2-minute confidence check → your personalised roadmap → meet the tutors matched to you. No card, cancel any time. No card. Build your plan, see your roadmap and meet the twelve tutors matched to you. All free. When you're ready to start learning, it's £70 a month, billed monthly. Cancel any time and billing stops.