Global Learning for Operational Leaders
Course AF-002
Artificial Intelligence in Banking and Financial Services
This course provides a comprehensive foundation in the application of Artificial Intelligence (AI), machine learning, an... The outline covers Foundations of Artificial Intelligence (AI) and Machine Learning (ML) in Fina...
- 5 daysClassroom or online
- 14Upcoming dates
- 7Cities
- €1,625Fees from
Your week at a glance
- Day 1 Foundations of Artificial Intelligence (AI) and Machine Learning (ML) in Finance and more
- Day 2 AI in Credit Risk Assessment
- Day 3 AI in Risk Management and Operations & AI in Portfolio and Wealth Management
- Day 4 Enhancing the Customer Experience Through AI and more
- Day 5 AI for Personalized Financial Services & Ethics and Responsible AI and more
Classroom
14 sessions
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Online / Live
14 sessions
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Introduction
Course overview
This course provides a comprehensive foundation in the application of Artificial Intelligence (AI), machine learning, and cognitive modelling within the financial services sector. Designed specifically for professionals in banking, asset management, financial services and governance, it explores how these technologies are reshaping client engagement, risk profiling, predictive analytics, and operational efficiency.
Course participants will gain a solid understanding of the fundamental logic behind AI and machine learning within the industry, and how these tools can be applied to better understand client behaviour, assess risk appetite, and tailor services to individual objectives. The course also highlights the transformative potential of AI in enhancing the customer experience across various touchpoints in financial services.
In addition to practical applications, such as time-series analysis and predictive modelling, the course also explores the integration of AI with legacy systems, identifying both opportunities and operational challenges.
What you will achieve
Learning objectives
- Understand how AI and machine learning can be used to assess client profiles, risk appetite, and financial objectives
- Identify how AI technologies enhance customer experience across various areas of financial services
- Apply AI concepts to time-series analysis and broader predictive analytics
- Evaluate practical challenges and integration issues when adopting AI alongside legacy systems
- Assess gaps between AI expectations and real-world outcomes, including the risk of overpromised benefits
Who should attend
Target audience
- This course is ideal for anyone in the banking and financial services industry, who are exploring the use of AI to support their role or organization.
Methodology
Learning approach
- The courses focuses on an interactive approach to knowledge transfer as well as practical studies and engagement of participants in an interactive Q&A format.
Complete course outline
Five focused days of learning and application
The timings provided are estimates and may vary depending on the number of participants and the discussions taking place during the sessions. They are indicative only and do not constitute a binding commitment.
Day 1
Foundations of Artificial Intelligence (AI) and Machine Learning (ML) in Finance and more
- Foundations of Artificial Intelligence (AI) and Machine Learning (ML) in Finance
- Introduction to AI and ML
- Evolution of search algorithms (Google, Microsoft, etc.)
- Overview of cognitive modelling and neural networks
- Supervised vs. Unsupervised learning
- Training vs. Inference
- Teaching models to learn from data
- Making predictions from trained models
- Tools and Technologies
- Software: Python, C++, parallel programming
- Hardware: Nvidia GPUs, high-capacity data storage
- Natural Language Processing (NLP)
- Coffee break
- Sentiment analysis from social media and textual data
- Applications of AI in Finance
- Customer-Facing Applications
- Chatbots and virtual assistants
- Personalized client profiling
- Risk Management
- Credit scoring and loan prediction
- Market risk prediction
- Fraud detection (anomaly detection and real-time monitoring)
- Trading and Execution
- Algorithmic trading strategies
- Sentiment and data pattern analysis
- Regulatory Compliance
- AI in AML/KYC processes
- Streamlining regulatory reporting
Day 2
AI in Credit Risk Assessment
- Enhancing Credit Evaluation
- Traditional vs. AI-driven credit scoring
- Key predictive variables
- Financial history
- Behavioral data
- Macro indicators
- AI Techniques and Models
- Logistic regression
- Neural networks
- Decision trees
- Alternative Data Sources
- Coffee break
- Alternative Data Sources (continued)
- Social media and transaction data
- Ethical concerns in alternative data usage
- Early Warning Systems
- Proactive identification of potential defaults
- AI in Asset Allocation and Investment Strategy
- Data-Driven Decision Making
- Analyzing financial statements, news, and social media
- Market sentiment extraction using NLP
- Portfolio Optimization
- Detecting non-obvious correlations
- Asset rebalancing and risk-adjusted allocations
- Fraud and Anomaly Detection
- Monitoring investment transactions for irregularities
Day 3
AI in Risk Management and Operations & AI in Portfolio and Wealth Management
- AI in Risk Management and Operations
- Predictive Analytics
- Forecasting market volatility via time series analysis
- Operational Efficiency
- Process mining and workflow optimization
- Predictive maintenance for systems
- Fraud Detection
- Classification and clustering techniques
- Real-time fraud analytics
- Coffee break
- Real-time fraud analytics (continued)
- Behavioral biometrics for identity verification
- AI in Portfolio and Wealth Management
- Portfolio Execution
- AI-assisted trade execution and price optimization
- Minimizing slippage and transaction costs
- Robo-Advisors and Automation
- Automated client profiling and rebalancing
- Risk monitoring and strategy adjustment
- Client-Centric Advisory
- Personalized reporting and strategy communication
Day 4
Enhancing the Customer Experience Through AI and more
- Enhancing the Customer Experience Through AI
- Client Engagement
- AI-powered support systems and recommendations
- Intelligent onboarding and client reporting
- Operational Automation
- Data entry, reconciliation, compliance, and reporting
- Personalized Services
- Tailored investment strategies and communications
- Data Integrity, Security, and Model Governance
- Data Quality and Bias
- Importance of training data quality
- Bias and fairness in AI decision-making
- Model Interpretability
- Explainability and audit trails
- Addressing black-box challenges
- Security and Compliance
- Data privacy, encryption, and cybersecurity risks
- Coffee break
- Data privacy, encryption, and cybersecurity risks (continued)
- Regulatory obligations and risk mitigation
- Human Capital and Organizational Challenges
- AI in Human Resources and Operations
- Bias and accountability in automated decisions
- Skills gap and AI talent requirements
- Strategic Adoption
- Building trust and executive buy-in
- Cost-benefit analysis of AI investment
- Workforce Impact
- Job displacement vs. upskilling
- Collaborating across business and tech divisions
- Governance, Ethics, and Risk Culture
- Organizational Governance
- Bridging the gap between tech and senior leadership
- FinTech adoption among younger demographics
- Ethics and ESG Considerations
- Data sharing, customer consent, and privacy
- Blockchain and environmental impacts
- Change Management
- Navigating structural and regulatory changes
Day 5
AI for Personalized Financial Services & Ethics and Responsible AI and more
- AI for Personalized Financial Services
- Tailored Advice and Budgeting
- AI-driven insights on spending and savings
- Smart financial planning tools
- Robo-Advisory Services
- Algorithmic wealth management
- Tax-loss harvesting and rebalancing
- Hybrid Models
- Human-AI collaboration in advisory roles
- Regulatory oversight and client transparency
- Ethics and Responsible AI
- Ethical AI Frameworks
- Fairness, accountability, and transparency
- Bias mitigation and auditability
- Privacy and Data Security
- Coffee break
- Data anonymization and federated learning
- Managing cybersecurity threats
- Principles of Responsible AI
- Oversight, robustness, and explainability
- The Regulatory Landscape
- Regulatory Frameworks in AI and Finance
- Current and emerging regulations for AI in finance (e.g., GDPR, explainability requirements)
- Overview of global and regional AI regulations impacting financial services
- Data Governance and Compliance
- Regulatory focus on data privacy, usage, and model governance
- Compliance challenges related to AI systems in finance
- Regulatory Technology (RegTech) Applications
- How AI can support compliance: monitoring, risk assessment, and reporting automation
- Future Trends in AI for Financial Services
- Emerging AI Technologies
- Quantum Machine Learning: addressing complex optimization challenges
- Federated Learning: privacy-preserving collaborative training
- Generative AI: synthetic data, content generation, and reporting applications
- Strategic Industry Impact
- How AI will reshape financial services, including roles and responsibilities
- Disruption of traditional models and competitive landscapes
Course FAQs
Questions about this course
What participants and training managers usually ask before booking Artificial Intelligence in Banking and Financial Services. Can't find your answer?
What does Artificial Intelligence in Banking and Financial Services (AF-002) cover?
This course covers Accounting and Finance through a structured five-day outline focused on practical application, discussion, and implementation planning.
When is the next available session?
The next scheduled session starts on 9 - 13 November 2026. See the course schedules section for the available dates and delivery formats.
Who should attend this course?
This course is ideal for anyone in the banking and financial services industry, who are exploring the use of AI to support their role or organization.
Is bilingual course delivery available?
Yes. All course materials and presentations are provided in English, while bilingual explanation and discussion support may be available depending on the selected location and trainer availability. For example, when attending a course in Istanbul, you may request a Turkish-speaking trainer to explain the English course content in Turkish. Arabic explanation support may also be requested at this location. Other languages are available in selected cities. You can choose your preferred language support as an optional preference during registration.
How can I register for a session?
Use any Register button next to the available course dates to open the participant registration page and submit your booking request for the selected session.
Is this course available online as well as classroom-based?
Check the course schedules section for currently published classroom and Online / Live sessions.
Where are classroom sessions delivered?
Current classroom venues include Barcelona, Paris, London, Amsterdam, Istanbul, Vienna, Cape Town.
How long is the training programme?
The programme runs for five days from 09:00 to 14:00, which gives 25 contact hours, including a daily 30-minute break (22.5 hours of instruction).
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