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Artificial intelligence for finance
AI applications in finance must connect model relevance with business requirements. Explore possible applications, their implementation requirements and limitations. Strengthen your ability to engage with technical teams and critically examine results.
- Duration
- 2 days 14 hours
- Code
- FN002FR Code
Presentation
Today, artificial intelligence is radically transforming the financial sector, offering unprecedented competitive advantages. Financial institutions can use this technology to automate complex tasks, optimise risk management and detect fraud more effectively, while personalising the customer experience.
In this intensive course, you will discover practical applications of AI in finance. You will cover the fundamentals of artificial intelligence, machine learning, deep learning and learning types (supervised, unsupervised and reinforcement). The programme also covers AI for credit scoring, fraud detection and financial report automation with generative AI. It also addresses key aspects of regulation (GDPR) and AI model transparency.
By the end of these digital finance classes, you will be able to analyse customer risk, identify suspicious transactions and generate automated financial summaries. Through targeted practical exercises, you will master tools and AI methods for solving real financial problems, strengthening your expertise and ability to innovate in this constantly evolving sector.
Objectives
By the end of this AI for finance course, you will be able to:
- gain an in-depth understanding of AI fundamentals and its different approaches, including symbolic AI, machine learning and deep learning;
- distinguish the 3 main learning types (supervised, unsupervised and reinforcement) and their specific applications in finance;
- master the practical application of AI to credit scoring, including supervised methods such as logistic regression and decision trees, and relevant financial performance metrics;
- develop fraud detection skills using unsupervised AI techniques such as Isolation Forest and Local Outlier Factor (LOF), while understanding different fraud types and their impact.
- use generative AI to automate financial report creation and produce summaries from raw data.
Program
Module 1: understand AI fundamentals in finance
- The definition, objectives and practical applications of artificial intelligence in finance.
- Key differences between symbolic AI, machine learning and deep learning.
- The 3 main learning types: supervised, unsupervised and reinforcement, with their characteristics and specific uses.
Module 2: apply AI to credit scoring
- Examples of AI applications, including automated credit scoring and customer risk analysis.
- Supervised methods for credit scoring (comparing logistic regression and decision trees).
- Financial performance measures (precision, recall, F1 score and specific metrics such as default rate and false positives/negatives), essential for meaningful evaluation.
Module 3: detect fraud with AI
- Overview of different types of financial fraud (payment card, transfer and identity fraud) and their impact on financial businesses.
- AI-based fraud detection techniques, particularly unsupervised methods for anomaly identification.
- Using Isolation Forest and Local Outlier Factor (LOF) as practical anomaly detection tools.
- Managing class imbalance issues in fraud detection to optimise model performance.
Module 4: automate financial reports with generative AI
- Automating financial reports with generative AI.
- Generative AI applications, including financial summaries generated from raw data (e.g. ChatGPT, GPT-4).
Module 5: manage AI model regulation and transparency
- AI model regulation, including the General Data Protection Regulation (GDPR), for ethical and lawful use.
- The need for AI model transparency and explainability to ensure trust and compliance.
Audience
This course is intended for:
- Finance professionals who want to understand and apply artificial intelligence in their field.
- Financial analysts who want to integrate AI to improve customer risk analysis, cash flow forecasting and report automation.
- Compliance managers who want to master AI applications, particularly fraud detection and regulatory implications.
- Anyone interested in applying AI to finance and seeking practical skills in this evolving field.
Prerequisites
This course requires the following prerequisites:
- Basic finance knowledge: familiarity with fundamental financial concepts and mechanisms.
- Fundamentals of statistics and programming: understanding of statistical basics and some familiarity with programming concepts.
Teaching and assessment methods
- Initial skills assessment
- Training materials provided to participants
- Continuous assessment throughout the course
- End-of-course feedback questionnaire
- Combination of theory and practical application
- Attendance records
- Post-course follow-up evaluation
- Practical exercises
Course highlights
- Instructor expertise: benefit from recognised instructors with in-depth knowledge of AI principles, relevant learning methods and their practical applications in finance.
- Practical application: master AI concepts through concrete examples of financial applications , including specific methods for credit scoring and fraud detection. The course also covers topics such as cash flow forecasting and automated financial report generation.
- Develop operational skills: build expertise in AI fundamentals, relevant learning methods (supervised, unsupervised, reinforcement) and their practical application in finance to solve real-world problems.
Dates and sessions
Choose the date and delivery format that suit you.
No upcoming sessions are currently available.