Course Code: omaiafp
Duration: 9 hours
Prerequisites:
  • Familiarity with financial risk and fraud prevention processes
  • Basic understanding of digital transformation in banking
  • Experience in managing technology-driven initiatives

Audience

  • Banking executives and decision-makers
  • Operational risk and compliance leaders
  • Digital transformation and innovation managers
Overview:

AI for anti-fraud initiatives is a strategic approach to using artificial intelligence to strengthen fraud detection and prevention in financial institutions.

This instructor-led, live training (online or onsite) is aimed at executive-level professionals who wish to lead, structure, and manage AI projects focused on fraud prevention within the banking sector.

By the end of this training, participants will be able to:

  • Define and assess operational AI opportunities in fraud prevention.
  • Structure effective business cases for AI implementation.
  • Establish scalable project frameworks and assign key roles.
  • Monitor, maintain, and optimize AI models for sustained impact.

Format of the Course

  • Interactive lecture and discussion.
  • Lots of exercises and practice.
  • Hands-on implementation in a live-lab environment.

Course Customization Options

  • To request a customized training for this course, please contact us to arrange.
Course Outline:

Session 1: Preparing the Ground for AI in Risk

1. Strategic Diagnosis for AI Adoption

  • Identification of knowledge and capability gaps in AI within the organization.
  • Mapping of current fraud prevention processes: Where AI can optimize and transform.
  • Common challenges in AI adoption in banking and how to overcome them.
  • The executive vision of AI: Defining realistic expectations and impact metrics.

2. Operational Foundations of AI in Banking

  • Types of AI applied to fraud detection: Supervised and unsupervised machine learning, Natural Language Processing (NLP).
  • The importance of data quality and volume: Data collection, cleaning, and preparation for AI models.
  • Data architectures for AI: Infrastructure required for processing large volumes of information in real time.
  • Risk and Mitigation: Data Governance, Security, and Privacy in the Age of AI

3. Creating the Operational Business Case

  • Definition of key operational metrics for AI (e.g., reduction of false positives, response time).
  • Calculation of the operational and financial return on investment (ROI) of AI in crime prevention.
  • Presentation of the business case to key stakeholders: Strategies for obtaining internal buy-in.
  • AI as an enabler of operational efficiency and resilience.

Session 2: Leadership and Execution of AI Projects

1. Structure and Roles in an AI Project

  • Identification of key profiles: Data scientists, ML engineers, business experts, risk specialists.
  • AI team models: Internal teams vs. hybrid teams with external partners.
  • Management of expectations and effective communication between technical and business teams.
  • Design of a scalable and adaptable implementation roadmap.

2. Tools and Methodologies for AI Projects

  • AI and ML Platforms (MLOps): Key concepts for managers (automation, monitoring, deployment).
  • Use of visualization and analysis tools for data-driven decision-making.
  • Agile methodologies (Scrum, Kanban) applied to the development and deployment of AI models.
  • Considerations for integrating AI with existing legacy systems.

3. Continuous Monitoring and Tuning of AI Models

  • The lifecycle of an AI model: From development to production and maintenance.
  • Automated model monitoring: Detection of performance degradation and data drift.
  • Retraining and redeployment strategies to maintain AI effectiveness in the face of new threats.
  • The importance of a robust AI Governance framework.

Session 3: Optimization and Long-Term Vision of AI in Banking

1. Results Evaluation and Impact Measurement

  • AI performance metrics: Accuracy, recall, loss reduction, false positive rate.
  • Executive Dashboards: How to interpret results without being a technical expert.
  • Model Audit and Validation: Ensuring the robustness and reliability of AI decisions.
  • Reporting to senior management and regulators: Transparency and justification of AI performance.

2. Advanced Challenges and the Future of AI in Crime Prevention

  • Generative AI and Deepfakes: New threats and how AI can combat them.
  • Interbank collaboration and fraud intelligence sharing.
  • AI in the context of anti-money laundering (AML) and organized crime.
  • Building a pro-AI and data-driven organizational culture.

3. AI Capability Acquisition Strategies: Optimizing the Path

  • Internal Development vs. Strategic Alliances: A key decision for speed and efficiency.
  • Challenges of building AI capabilities from scratch: time, cost, scarce talent.
  • Benefits of partnering with specialized platform providers: Instant access to cutting-edge technology, pre-trained models, extensive experience in banking fraud, lower risk and implementation time, and a focus on tangible results that free up internal resources for core initiatives.
  • Agility and Adaptability: How external platforms enable rapid response to emerging threats and regulatory developments.
  • Long-Term Strategy: Maximize the value of AI for comprehensive and continuous protection of your institution and your customers.
Sites Published:

México - Operational Management of AI-Based Anti-Fraud Projects