An Interactive 5-Day Training Course
Predictive Modeling for Financial Fraud
Leveraging Statistical and Machine Learning Models to
Anticipate and Prevent Financial Fraud
Course Overview
Financial fraud continues to pose a major threat to organisations—draining resources, undermining trust, and triggering regulatory scrutiny. Traditional reactive methods, while still relevant, no longer suffice in today’s complex financial landscape. As fraud tactics become more sophisticated, so must the strategies to detect and prevent them.
The Predictive Modeling for Financial Fraud training course empowers participants to transition from reactive fraud detection to proactive risk mitigation using statistical and machine learning techniques. This training course provides a comprehensive introduction to predictive modeling principles tailored to fraud analytics, enabling professionals to anticipate fraudulent behaviour based on historical data, transactional anomalies, and behavioural trends.
Designed for both technical and non-technical professionals, this course ensures that participants not only understand model design and interpretation but can also contribute meaningfully to fraud prevention strategies within their organisations.
This Predictive Modeling for Financial Fraud training course will highlight:
- The full lifecycle of predictive modeling for financial fraud detection
- Machine learning algorithms (e.g., decision trees, logistic regression, random forests) used in predictive fraud models
- Key performance indicators and metrics for evaluating model accuracy
- How to train, test, and validate models using historical fraud data
- Strategies to embed predictive analytics into broader fraud risk frameworks
- Ethical considerations and data governance in predictive model deployment
Training Objectives
By the end of this Predictive Modeling for Financial Fraud training course, participants will be able to:
- Understand the role and value of predictive modeling in fraud prevention
- Build and evaluate basic predictive models using relevant fraud datasets
- Identify appropriate data features and variables for fraud prediction
- Interpret model outputs and apply them to real-time risk-based decisions
- Collaborate with data teams to implement fraud models aligned with business priorities
- Integrate predictive modeling into a strategic fraud risk management approach
Who should Attend?
This Saudi GLOMACS Predictive Modeling for Financial Fraud training course is ideal for professionals responsible for managing fraud risk, financial data analysis, or predictive analytics, including:
- Fraud and Financial Crime Analysts
- Risk and Compliance Officers
- Internal Auditors and Forensic Accountants
- Data Scientists and Business Analysts
- Finance and Operations Managers seeking proactive fraud solutions
About Saudi Glomacs
At Saudi GLOMACS, we specialize in delivering world-class training courses in Saudi Arabia and across various international locations. Our training courses are tailored to meet the unique demands of Saudi Vision 2030 and the Human Capability Development Program, focusing on empowering Saudi citizens and enhancing workforce skills. We offer diverse courses spanning leadership, management, engineering, and technical disciplines to cultivate expertise and drive professional growth. Our flexible learning options—whether in-person, online, or in-house—ensure accessibility and convenience for individuals and organizations alike.
With over 30+ years of experience through the GLOMACS global network, we are committed to delivering innovative, results-driven training solutions. Our expert instructors combine industry knowledge with dynamic teaching methods, fostering practical skill development and long-term career success. By choosing Saudi GLOMACS, you're investing in personal excellence and contributing to the Kingdom’s sustainable economic growth and vision-driven transformation.
Training Outline
Introduction to Predictive Modeling and Financial Fraud
- Understanding financial fraud typologies and trends
- Limitations of traditional detection and need for prediction
- What is predictive modeling? Key concepts and benefits
- Data collection and cleansing for fraud modeling
- Feature engineering: selecting and creating predictive variables
- Dealing with imbalanced data and rare event modeling
- Overview of classification algorithms: logistic regression, decision trees, and more
- Introduction to machine learning models for fraud detection
- Evaluating model performance: confusion matrix, ROC, AUC
- Interpreting model outputs and scores
- Implementing risk thresholds and decision strategies
- Integrating models into operational systems
- Aligning predictive modeling with business and regulatory objectives
- Developing a fraud risk framework with predictive analytics
- Collaborating with technical teams and data scientists
Certificates
- On successful completion of this training course, GLOMACS Certificate will be awarded to the delegates
- Continuing Professional Education credits (CPE) : In accordance with the standards of the National Registry of CPE Sponsor, one CPE credit is granted per 50 minutes of attendance
Accreditation
GLOMACS is registered with NASBA as a sponsor of Continuing Professional Education (CPE) on the National Registry of CPE Sponsors. NASBA have final authority on the acceptance of individual courses for CPE credit. Complaints regarding registered sponsors may be submitted to the National Registry of CPE Sponsors through its website: www.learningmarket.org.
All Training Seminars delivered by GLOMACS by default are eligible for CPE Credit.
Sessions in international locations
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