Описание
Business Training Audience
Business Owners
Board of Directors
Senior Management
Head of Security
Head of IT
Heads of Internal Control
Head of Internal Audit
Head of Risk Management
HR Director
Department Specialists
This business training on the use of artificial intelligence (AI) in building a comprehensive anti-fraud system in a company will help solve practical problems:
Explore artificial intelligence (AI) and machine learning (ML) technologies and their impact on corporate anti-fraud systems, proactive risk identification
Learn how to use AI to build a risk-based anti-fraud system integrated into internal control, compliance, and audit processes
Understand how AI strengthens anti-fraud systems
Master the principles of building AI models for fraud detection
Learn how to use AI to analyze transactions, counterparties, and employee behavior
Learn AI implementation strategy for a company’s anti-fraud system
Obtain practical tools and case studies from leading companies
Main business training topics:
Digital transformation and evolution of corporate anti-fraud systems
Goal: Understand how AI is changing the approach to combating corporate fraud.
Topics:
Typologies of corporate fraud and current trends
Limitations of traditional internal control systems
The role of data and digital technologies in combating fraud
Key AI technologies: machine learning, NLP, anomaly detection
Strategy for transitioning to an intelligent anti-fraud system
Workshop: Self-assessment of the maturity of a company’s anti-fraud system
Artificial intelligence in fraud detection and analysis
Goal: Master the use of AI models to identify anomalies and suspicious activity.
Topics:
Building AI models for anomaly detection
Supervised and unsupervised learning in fraud analysis
Using clustering and pattern analysis in transactions
AI algorithms for assessing the likelihood of fraud
Working with big data and streaming analytics
Workshop: Developing a simple AI model for detecting anomalies in transactions
AI in risk management and transaction monitoring
Objective: Use predictive analytics to assess and prevent risks.
Topics:
Predictive analytics and fraud probability assessment
Monitoring deviations and employee behavioral patterns
Building a «risk map» based on data
AI for continuous control monitoring
Detection of complex fraudulent schemes (collusion detection)
Workshop: Developing an AI dashboard of risk indicators and behavioral signals
Verifying counterparties and transactions using AI
Goal: Improve the quality of compliance checks and due diligence processes.
Topics:
Using AI to automate counterparty verification
Analysis of digital footprints, media, and open sources (OSINT)
Detecting affiliations and hidden connections
NLP for document and contract analysis
Using AI in sanctions and reputation screening
Workshop: Analyzing a counterparty database using AI tools
AI and internal employee behavior analytics
Goal: Learn to identify risks of internal fraud and violations.
Topics:
Behavioral Analytics
Using AI to Analyze Communications and Actions
Building Risk Profiles by Department and Role
Segregation of Duties (SoD) and Access Control with AI
Monitoring Digital Footprints and Identifying Conflicts of Interest
Workshop: Building an Employee Behavioral Risk Model
Implementing AI in a Comprehensive Anti-Fraud System
Goal: Develop a strategy for integrating AI into an existing internal control system.
Topics:
Architecture of an AI-focused Anti-Fraud System
Integration with ERP, BI, and Monitoring Systems
Implementation Stages and Change Management
Performance Metrics and ROI for AI Initiatives
Human + AI in Anti-Fraud Combating
Workshop: Developing an AI Anti-Fraud Framework Implementation Roadmap
Ethical and Legal Aspects of Using AI in Anti-Fraud Systems
Goal: To develop a secure and sustainable approach to AI.
Topics:
Ethical Risks and Personal Data Protection
Using AI in Compliance with GDPR and Local Laws
Transparency and Interpretability of AI Models
Managing Bias and Algorithmic Errors
Best Practices: Compliance by Design in AI Systems
Workshop: Analyzing Case Studies and Ethical Risks in Using AI
Duration: 2 days
