This comprehensive course explores the application of data analytics and artificial intelligence in financial services, covering the technologies, methodologies, and use cases that are transforming the industry. Participants will learn about data management, machine learning algorithms, natural language processing, and their applications in areas such as risk management, customer insights, fraud detection, and investment analysis. The curriculum addresses both technical implementation and business strategy considerations, including data governance, model risk management, and ethical AI practices. Through technical demonstrations and case studies, learners will develop the knowledge to leverage data and AI for competitive advantage in financial services.
Data Analytics and AI in Financial Services
Banking, Insurance and Financial Services
October 25, 2025
Introduction
Objectives
Upon completion of this course, participants will be able to:
- Understand data analytics and AI technologies
- Develop data-driven business strategies
- Implement machine learning applications
- Manage data governance frameworks
- Navigate AI regulatory requirements
- Assess AI implementation risks
- Develop AI use cases for financial services
- Manage data analytics projects
- Evaluate AI vendor solutions
Target Audience
- Data Scientists and Analysts
- Technology Managers
- Business Intelligence Professionals
- Risk Management Officers
- Product Development Teams
- Compliance Professionals
- Financial Analysts
- Strategy Consultants
Methodology
- Data analysis exercises
- AI use case development
- Model validation workshops
- Implementation planning sessions
- Ethical scenario analysis
- Vendor evaluation exercises
Personal Impact
- Enhanced data analytics knowledge
- Improved AI understanding
- Stronger technical assessment skills
- Better project management capabilities
- Enhanced ethical decision-making
Organizational Impact
- Improved decision-making quality
- Enhanced operational efficiency
- Better risk management
- Increased innovation capability
- Competitive advantage in analytics
Course Outline
Unit 1: Data Analytics Foundation
Data Management- Data architecture principles
- Data quality management
- Data governance frameworks
- Data privacy and security
- Descriptive analytics applications
- Predictive modeling approaches
- Prescriptive analytics methods
- Visualization and reporting
Unit 2: Artificial Intelligence Technologies
Machine Learning- Supervised learning algorithms
- Unsupervised learning approaches
- Reinforcement learning applications
- Deep learning neural networks
- Natural language processing
- Computer vision applications
- Robotic process automation
- Intelligent automation
Unit 3: Banking Applications
Customer Insights- Customer segmentation advanced
- Churn prediction models
- Next best action recommendations
- Personalized marketing
- Credit risk modeling enhancement
- Fraud detection systems
- AML transaction monitoring
- Operational risk analytics
Unit 4: Investment Applications
Investment Analysis- Alternative data applications
- Sentiment analysis for markets
- Portfolio optimization advanced
- Algorithmic trading strategies
- Robo-advisor technology
- Personalized portfolio construction
- Behavioral finance applications
- Client engagement enhancement
Unit 5: Insurance Applications
Underwriting Enhancement- Predictive underwriting models
- Risk assessment automation
- Claims prediction analytics
- Pricing optimization
- Automated claims assessment
- Fraud detection enhancement
- Image recognition for damage
- Customer service automation
Unit 6: Implementation Framework
Project Management- AI project lifecycle management
- Data science team structure
- Vendor selection criteria
- Implementation best practices
- Model validation requirements
- Bias detection and mitigation
- Performance monitoring
- Model documentation standards
Unit 7: Governance and Ethics
Regulatory Compliance- AI regulations overview
- Explainability requirements
- Data protection compliance
- Regulatory examination preparation
- Algorithmic bias prevention
- Transparency and fairness
- Accountability frameworks
- Social responsibility considerations
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