AI/ML IN FINANCIAL SERVICES COURSE

• Instructor Introduction
• Course Objectives & Flow
• Q & A

Buckle up and explore the world of AI and ML: Essential skills for aspiring data scientists and engineers. Uncover the power of AI and ML in finance, decode key terminology, and dive into the Machine Learning pipeline and workflow.

• What is AI & ML?
• Skills needed for a data scientist or data engineer
• AI & ML solutions for financial services
• Key terminology and software related to financial services
• Machine Learning pipeline & workflow

Assignment #01 - AI/ML & Financial Services Typeform [Optional]

Discover AI/ML algorithms to pinpoint financial issues with actionable AI/ML solutions. Delve into classifications, compare regression models, and witness a live demonstration that walks you through the intricacies of identifying challenges and the right approaches.

• AI & ML algorithms
• Classification vs. regression models
• Financial problems meet AI/ML solutions
• Demo: Analyze financial problems & formulate solutions

Assignment #2 - AI/ML Solutions for Financial Services

Unlock the power of data in finance. Learn to harness diverse data sources, explore essential data processing tools, and master the art of data preparation for precise analysis and modeling in finance.

• Data sources in finance
• Data processing software
• Importance of preprocessing data & data cleansing in finance
• Lab: Python for preprocessing & cleansing data

Assignment #03 - Python for Preprocessing & Cleansing Data [Optional]

Elevate your finance skills with practice. Build an automated credit risk assessment system, evaluate model performance with diverse metrics, and create a robust credit risk model.

• Credit scoring & evaluation models
• Automating credit risk assessments
• Model evaluation & performance metrics
• Demo: Building a credit risk model

Assignment #4: Credit Risk Model

Uncover the nuances of fraud detection. Harness AI's role in real-time fraud prevention, master data-driven fraud detection, and implement powerful anomaly detection techniques.

• Types of fraud
• AI & fraud detection
• Real-time detection
• Guest Speaker Demo: Data-driven fraud detection & anomaly detection techniques

Assignment #5: Fraud Detection

Use history to predict the future. Leverage time series forecasting methods, pinpoint practical applications for predictive analytics, especially in ATM demand volume prediction, and assess model effectiveness using suitable metrics.

• Forecasting & analytics tools
• Time series forecasting techniques
• Use cases for predictive analytics
• Model evaluation and performance metrics
• Lab: Trend forecasting

Assignment #6: Trend Forecasting

Master AI techniques for portfolio optimization and risk management in finance. Enhance portfolio distribution and implement algorithmic trading strategies.

• Portfolio distribution
• Asset management using Neural Networks
• Algorithmic trading strategies
• AI-driven portfolio optimization and risk management
• Guest Speaker Demo: Implementing & assessing AI-driven portfolio optimization and trading strategies

Assignment #07 - Algorithmic Trading Case Study [Optional]

Delve into RPA's applications, integrate AI/ML, and design automation solutions for operational excellence in finance. The result? Maximized efficiency, accuracy, and innovation in financial processes.

• RPA financial services use cases
• RPA opportunities & benefits
• Integrating AI/ML with RPA
• Guest Speaker: RPA in financial services

In this class, you’ll master customer segmentation using ML and Deep Learning for actionable insights from diverse data. Then you’ll create distinct customer profiles based on behaviors, preferences, and characteristics.

• Customer segments
• Customer profiling & segmentation techniques using ML/DL
• Personalized marketing & product recommendations using ML/DL
• Lab: Customer segmentation

Assignment #8: Customer Segmentation

Learn to work smarter and not harder. Employ NLP to design virtual assistants, chatbots, and gain insights from customer feedback using sentiment analysis.

• Introduction to Natural Language Processing (NLP)
• Applications of NLP in finance
• Automation: virtual assistants & chatbots
• Sentiment analysis for customer feedback
• Lab: Building virtual assistants & chatbots

Assignment #9: Virtual Assistants & Chatbots

Cultivate a culture of proficiency by educating and training teams. Learn how to integrate AI/ML into existing systems and gain insights from metrics that measure success and ROI.

• Building an AI/ML team and infrastructure
• Integrating AI/ML into existing systems
• Educating & training teams on AI/ML solutions
• Measuring success and ROI of AI/ML projects

Assignment #10: Educating Teams on AI/ML Solutions

Handle the future of finance responsibly. Identify ethical and regulatory dimensions, potential biases, and data privacy concerns in AI implementation within finance.

• Ethical considerations of AI in finance
• Data privacy & security concerns
• Regulations and compliance
• Guest Speaker: Ensuring ethical implementation of AI & ML in finance

Kickstart your career and journey into AI-powered financial paths. Get insights into the job landscape, review interview tips, and learn how to tailor your resume to highlight your skills.

• Career outlook
• AI & finance future
• Resume & interview guidance
• Course wrap-up

Assignment #11: Resume & Cover Letter Review [Optional]