Python for Counter-Fraud
Python is one of the most popular programming languages today, and mastering it provides significant investigative and automation opportunities for fraud investigators. From analysing transaction patterns to automating repetitive checks, Python can transform how efficiently you work with data and detect fraud.
This hands-on introduction to Python is designed specifically for those working in counter-fraud. The course teaches practical coding skills through real fraud scenarios such as analysing transactions, flagging suspicious patterns, and automating routine OSINT checks.
This hands-on online course combines a hands-on understanding of Python with practical fraud investigation exercises.
You will write real Python code, work with actual fraud datasets, and progressively build your programming skills from basic syntax through to automated fraud detection scripts. By the end of the course, you will have created a working fraud toolkit that you can adapt and expand for your own investigation needs.
Module details
What you will learn:
Python Basics for Investigators
Build foundational Python knowledge tailored to fraud investigation needs:
• Understand why Python is useful in counter-fraud work and what it enables you to do.
• Learn how to install and run Python on your system.
• Gain confidence in coding fundamentals and Python syntax.
• Write your first Python scripts for fraud investigation tasks.
Working with Data
Master essential data structures and file handling for fraud analysis:
• Understand how to use lists, dictionaries, and simple data structures using fraud-relevant examples.
• Work with CSV and Excel files—the most commonly used formats in fraud investigations.
• Prepare datasets for analysis by removing inconsistencies and standardising formats.
Filtering, Rules, and Pattern Detection
Build fraud detection logic using conditional statements and loops:
• Use conditions (if statements) and loops to implement fraud rules and red-flag checks.
• Build simple reusable checks for duplicate records indicating identity fraud or application fraud.
• Detect unusual transaction amounts suggesting payment fraud.
• Flag repeated identifiers such as emails, phone numbers, or IP addresses across multiple accounts.
Automating Repetitive Investigation Tasks
Transform time-consuming manual tasks into automated scripts:
• Write scripts to standardise datasets, ensuring consistent formatting across investigation files.
• Automate the renaming and organisation of evidence files for case management.
• Batch-check records against fraud criteria without manual review.
• Log script outputs so results can be reviewed and included as part of case files for audit trails.
Working with Open-Source Data and Third-Party Tools
Expand your capabilities using external data sources and Python libraries:
• Learn to make web requests to incorporate external data into your fraud investigations and reports.
• Parse basic structured formats like JSON for integrating API data.
• Understand third-party Python libraries and safety concerns when using external code.
• Explore transitioning to R—a dedicated programming language for statistical analysis often used alongside Python.
Investigation Project
Apply everything you have learnt to build a practical fraud detection tool:
This course culminates in a guided project where you will build your own "fraud toolkit"—a Python script that loads data, applies fraud checks, flags suspicious records, and exports a findings file. This mirrors real-world counter-fraud workflows and provides you with a reusable tool for your daily investigation work.
Real-world applications
The Python skills taught in this fraud investigation training apply across numerous counter-fraud scenarios:
• Automating transaction monitoring to flag high-risk payments.
• Detecting duplicate applications indicating identity fraud or synthetic identities.
• Batch-checking customer data against fraud indicators.
• Standardising evidence files and investigation documentation.
• Analysing large datasets for fraud patterns invisible to manual review.
• Integrating open-source intelligence data into fraud investigations.
• Building custom fraud detection rules specific to your organisation's risk profile.
• Creating automated reports for compliance teams and senior stakeholders.
Who is this course aimed at?
This Python course is designed for fraud prevention and investigation professionals seeking to develop coding skills for data analysis and automation:
• Fraud analysts wanting to automate repetitive investigation tasks.
• Counter-fraud investigators working with large transaction datasets.
• Compliance officers conducting AML monitoring and KYC checks.
• Financial crime investigators examining payment fraud patterns.
• Data analysts in fraud prevention teams building detection tools.
• Anyone in counter-fraud roles looking to develop technical programming skills.