The increasing use of vast datasets for financial risk modeling presents a critical challenge: how to extract valuable insights without compromising individual privacy. As regulators and the public demand stronger data protection, financial institutions must adopt robust privacy-enhancing technologies. This "Privacy-Preserving Risk Models" training course introduces you to the concept of Differential Privacy, a rigorous mathematical framework that allows you to analyze data and build accurate risk models while guaranteeing the privacy of every individual's data point.

This 10-day training course goes beyond theory to provide practical skills in implementing differential privacy in real-world risk management scenarios. You will learn to use differential privacy to build credit scoring models, analyze market risk, and conduct fraud detection with a high degree of privacy guarantee. By mastering these techniques, you will not only comply with evolving privacy regulations but also build trust with your clients, establishing a new standard for responsible data usage in the financial industry.

Duration: 10 days

Target Audience:

• Risk Managers and Analysts
• Data Scientists and Machine Learning Engineers
• Quantitative Analysts
• Compliance and Privacy Officers
• Financial Engineers
• IT Professionals in the financial sector
• Researchers in privacy-preserving AI
• Students in finance and computer science
• Chief Risk Officers
• Auditors

Objectives:

• Understand the core concepts of differential privacy
• Learn to apply differential privacy to financial data
• Implement privacy-preserving risk models
• Master the trade-offs between privacy and utility
• Gain insights into privacy budgets and composition
• Analyze the impact of differential privacy on model accuracy
• Identify suitable risk modeling problems for private solutions
• Learn to use popular privacy libraries
• Understand the regulatory landscape for data privacy
• Prepare for the future of responsible data analysis

Course Modules:

Module 1: Foundations of Differential Privacy

• The definition of differential privacy
• The role of noise in privacy
• The concept of a privacy budget (ϵ)
• Global vs. local differential privacy
• Common privacy attacks on financial data

Module 2: Privacy-Preserving Data Release

• The Laplacian mechanism for releasing numeric data
• The Exponential mechanism for releasing categorical data
• The importance of query sensitivity
• An introduction to the no-free-lunch theorem
• Implementing a private data aggregator

Module 3: Differential Privacy for Machine Learning

• The principles of privacy-preserving machine learning
• Private stochastic gradient descent (Private-SGD)
• The role of clipping and noise injection
• The impact on model accuracy and convergence
• Implementing a differentially private neural network

Module 4: Private Credit Scoring Models

• The limitations of traditional credit scoring
• Applying differential privacy to credit applications
• The role of private data collection
• Building a privacy-preserving logistic regression model
• Evaluating model performance under privacy constraints

Module 5: Differential Privacy for Market Risk

• The challenges of sharing market data
• Using differential privacy for volatility modeling
• Private-preserving principal component analysis (PCA)
• Analyzing market risk with privacy guarantees
• The impact on scenario analysis and stress testing

Module 6: Privacy in Fraud Detection

• The importance of private data in fraud detection
• Using differential privacy to train fraud classifiers
• The trade-off between false positives and privacy
• Implementing a private anomaly detection system
• The role of secure multi-party computation

Module 7: Privacy Budgets and Composition

• The concept of sequential composition
• The role of advanced composition theorems
• Managing a global privacy budget
• The implications of releasing multiple queries
• Strategies for budget allocation

Module 8: The Legal and Regulatory Landscape

• An overview of GDPR and CCPA
• The role of differential privacy in compliance
• The importance of accountability and transparency
• The legal implications of re-identification attacks
• The future of privacy regulations

Module 9: Advanced Differential Privacy Topics

• An introduction to zero-knowledge proofs
• The concept of federated learning
• The role of synthetic data generation
• The use of homomorphic encryption
• The integration of privacy-preserving technologies

Module 10: Practical Implementation with Libraries

• An overview of popular privacy libraries like Opacus and Google's DP library
• Hands-on implementation of private algorithms
• The importance of parameter tuning
• Debugging and troubleshooting privacy implementations
• Integrating a privacy library into an existing workflow

Module 11: Case Studies in Financial Privacy

• Analyzing case studies of privacy-preserving systems
• The challenges and successes of real-world implementations
• The role of ethical considerations
• The impact on business strategy
• Lessons learned from the field

Module 12: The Privacy-Utility Trade-off

• The mathematical relationship between privacy and accuracy
• Quantifying the loss of utility
• Strategies for maximizing utility while preserving privacy
• The role of hyperparameter optimization
• The importance of a practical mindset

Module 13: Communicating Differential Privacy

• Explaining complex concepts to non-technical stakeholders
• The importance of transparency with customers
• Building a trust-based relationship
• The role of privacy audits
• Marketing your privacy-first approach

Module 14: Career and Professional Development

• The growing demand for privacy professionals
• The role of certifications in the field
• The importance of continuous learning
• The future of financial professionals
• Networking with other privacy experts

Training Approach

This course will be delivered by our skilled trainers who have vast knowledge and experience as expert professionals in the fields. The course is taught in English and through a mix of theory, practical activities, group discussion and case studies. Course manuals and additional training materials will be provided to the participants upon completion of the training.

Tailor-Made Course

This course can also be tailor-made to meet organization requirement. For further inquiries, please contact us on: Email: info@skillsforafrica.org, training@skillsforafrica.org  Tel: +254 702 249 449

Training Venue

The training will be held at our Skills for Africa Training Institute Training Centre. We also offer training for a group at requested location all over the world. The course fee covers the course tuition, training materials, two break refreshments, and buffet lunch.

Visa application, travel expenses, airport transfers, dinners, accommodation, insurance, and other personal expenses are catered by the participant

Certification

Participants will be issued with Skills for Africa Training Institute certificate upon completion of this course.

Airport Pickup and Accommodation

Airport pickup and accommodation is arranged upon request. For booking contact our Training Coordinator through Email: info@skillsforafrica.org, training@skillsforafrica.org  Tel: +254 702 249 449

Terms of Payment: Unless otherwise agreed between the two parties’ payment of the course fee should be done 10 working days before commencement of the training.