Description

The course Unmasking the Hidden: AI Detection in Financial Crime & Compliance is designed to equip you with the latest AI-powered tools and techniques essential in the fight against modern fraud.

You will explore how AI is transforming fraud detection, prevention, and investigation, as well as how European regulations are shaping the future of compliance.

Who should attend

The course is intended for professionals who work with or interact with data, risks, and compliance processes, including:

specialists in antifraud, compliance, audit, and internal control;

analysts from banking, insurance, energy, telecom, and the public sector;

investigators and experts in financial crime prevention and anti–money laundering;

project coordinators and managers who want to understand the practical application of AI in risk and compliance;

individuals interested in learning modern data analysis tools without requiring complex software investments.

Course Content

1. Practical Introduction

The program begins with an interactive demonstration showcasing the potential of AI in generating both visual and analytical content.
From this starting point, participants discover how AI can be used to analyze and understand behavioral patterns that precede fraud or compliance breaches.
The focus is on developing analytical skills—not on using commercial applications.

2. Anomaly Detection & Pattern Recognition in Python

This section provides a hands-on perspective on how unusual behaviors can be identified in operational or transactional data.

Participants will work with intuitive Python libraries, with step-by-step explanations:

pandas – filtering, cleaning, and transforming data (Excel-like tables)

numpy – basic numerical operations

matplotlib / plotly – visualizing results

scikit-learn – applying anomaly detection models (Isolation Forest, One-Class SVM, LOF)

regex / fuzzywuzzy – identifying matches and similarities in texts, names, and addresses

Practical exercises simulate real fraud scenarios:

detecting suspicious transactions based on statistical deviations;

recognizing atypical patterns in payment series or reports;

building simple functions for risk score calculation.

Everything is designed so that participants can repeat the exercises later without IT assistance.

3. From Analysis to Decision: Power BI Dashboards

Participants will learn how to turn analytical results into intuitive, interactive Power BI dashboards.
They will understand how to visually structure indicators, track trends, and interpret risk scores in real time.

Activities include:

importing and modeling data in Power BI;

data cleaning and normalization using Power Query;

creating indicators and scores with DAX;

building heatmaps, tables, and dynamic charts;

segmenting entities by risk level (low, medium, high).

The outcome is a complete antifraud dashboard that can be reused or extended in their own analysis processes.

4. Prevention, Compliance & Regulatory Frameworks

The final part connects technical analysis with European regulatory and compliance best practices. Topics include:

principles of ethics, transparency, and accountability in AI use;

current challenges in applying AI to audit and control processes;

ISO/IEC 42001 – its structure, requirements, and applicability in antifraud;

how organizations can align with AI governance and compliance principles through methodology and documentation.

A special guest will provide practical insights on integrating the standard into day-to-day risk and compliance activities.

Skills Gained

- Understanding modern AI-based fraud detection methods

- Ability to use Python for data cleaning and analysis

- Creating Power BI dashboards for reporting and monitoring

- Visual interpretation of risk scores and behavioral patterns

- Knowledge of legal frameworks and ISO/IEC 42001 requirements for AI governance

- Developing analytical thinking based on evidence and data interpretation