About the Course
Modern organizations face an unprecedented scale of operational and financial threats that demand a shift toward evidence-based risk management. This course addresses the core challenge of identifying hidden patterns within vast datasets that signal potential fraud or systemic risk. You will develop the capability to demonstrate five critical domain competencies: performing automated data validation, executing statistical anomaly detection, building predictive fraud models, visualizing risk concentrations, and reporting findings to executive leadership. By integrating the principles of the COSO ERM framework with modern data engineering, you will learn how to build a structured system for continuous monitoring that replaces intermittent, manual checks with persistent, data-driven oversight.
Throughout this intensive 10-day program, you will transition from foundational data concepts to intermediate predictive modeling. You will learn how to apply Benford’s Law to detect accounting irregularities, use SQL for complex data joining across disparate systems, and leverage machine learning algorithms like Random Forest for fraud classification. This course provides hands-on practice with real-world datasets where you will practice building risk scoring engines, while being introduced to advanced concepts like neural networks for pattern recognition. We acknowledge the real-world constraints of data silos, poor data quality, and evolving regulatory burdens, and we provide the specific templates and scripts needed to deliver results under these professional pressures.
Target Audience
This program is essential for professionals who must safeguard organizational integrity through data-driven oversight and technical analysis.
This course is designed for:
- Internal Audit Managers overseeing digital transformation in audit workflows
- Fraud Investigation Specialists responsible for detecting financial statement irregularities
- Risk Analytics Officers building automated early-warning systems
- Compliance Managers handling Anti-Money Laundering (AML) data sets
- Financial Controllers implementing continuous monitoring and internal controls
- Credit Risk Analysts developing predictive models for default detection
- Cybersecurity Analysts monitoring transactional data for digital fraud patterns
- Data Analysts transitioning into specialized risk and forensic domains
- Operational Risk Managers reporting on enterprise-wide threat landscapes
- External Auditors seeking to enhance substantive testing with analytics
Course Objectives
This course equips you to design, execute, and report risk analytics initiatives that improve detection rates, ensure compliance, and drive strategic resilience.
By the end of this course, you'll be able to:
- Assess organizational risk maturity using the ISO 31000 standard as a benchmark
- Apply Benford’s Law and Z-Score analysis to identify statistical outliers in financial data
- Construct complex SQL queries to extract and join risk-relevant data from multiple sources
- Develop a fraud detection dashboard using Tableau or Power BI for real-time monitoring
- Execute machine learning classification models to predict high-risk transactional behavior
- Navigate regulatory reporting requirements for AML and KYC using automated data workflows
- Measure the effectiveness of internal controls through continuous data auditing techniques
- Synthesize analytical findings into executive-level risk reports and actionable mitigation plans
Requirements & Prerequisites
Participants should have a basic understanding of risk management concepts or internal auditing. Familiarity with Microsoft Excel is required. No prior experience with SQL or Python is necessary, as foundational technical skills will be covered during the course.
Local Application and Business Return in Poland
How participants can apply the training in local operating conditions, and the return their organisation can plan for.
How participants apply this
Expected ROI
Training Methodology
This is a practical, outcome-driven course designed to turn risk management aspirations into measurable action and credible reporting.
Methodology includes:
- Hands-on calculation of Z-Scores and Mahalanobis Distance using accounting datasets
- Scenario simulation requiring fraud detection decisions during a simulated procurement audit
- Audit diagnostic exercise using the COSO ERM framework to identify control gaps
- Stakeholder mapping exercise for reporting fraud findings to the Audit Committee
- Case study analysis of financial fraud in the banking, retail, and public sectors
- Group workshop producing a functional fraud detection dashboard in a digital environment
- Reflection exercise benchmarking current organizational risk practices against ISO 31000 standards
Upcoming Sessions
Next available dates worldwide
Certification
Recognized credentials that advance your career
Participants who complete the Data Analytics for Risk Management and Fraud Detection Training Program earn a Trainingcred Certificate of Achievement, demonstrating professional competence and alignment with global standards in learning and development.
NITA Accredited
Accredited by the National Industrial Training Authority, ensuring programs meet nationally recognized standards of quality and relevance.
CPD Certified
Recognized by the CPD Certification Service, ensuring every program meets internationally benchmarked standards of professional excellence.
Why this course earns its place on your CV
Accredited training, practitioner trainers, and peers on the same career track — the three things real expertise is built on.
Career Advancement
- Equip yourself with top-tier risk management skills demanded by leading firms.
- Enhance your resume with advanced fraud detection techniques, boosting career prospects.
- Gain certifications that make you a prime candidate for senior analytical roles.
Expert-Led Training
- Learn from industry experts with first-hand experience in risk and fraud analytics.
- Directly apply real-world case studies taught by seasoned professionals.
- Access exclusive insights that set you apart in the complex field of data analytics.
Practical Skills Application
- Master the use of cutting-edge tools for immediate implementation in your workplace.
- Transform data into actionable fraud prevention strategies through hands-on learning.
- Develop confidence in mitigating risks with data-driven decision-making skills.
Tools and platforms relevant to this field
Examples Poland teams may encounter, and that may be featured in training where they support the confirmed course scope.
These are field-relevant examples, not a promise that every tool will be covered. Exact coverage depends on the confirmed course scope, participant needs, and delivery format.
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Power BI MicrosoftCommonly used to build fraud dashboards, exception reports, and interactive risk visualisations for audit and compliance teams.
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SQL Server MicrosoftUsed to query transactional data, reconcile records, and automate repeatable fraud tests across large datasets.
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Python Python Software FoundationUsed for anomaly detection, automated scoring, text parsing, and network analysis in fraud and risk workflows.
