Financial Data Analytics Using AI and Machine Learning

Financial Data Analytics Using AI and Machine Learning
Course Objectives:
By the end of this course, participants will be able to:
• Understand the fundamentals of AI and machine learning in the context of financial data analysis.
• Utilize AI and machine learning techniques to analyze and interpret financial data for better decision-making.
• Implement AI-based solutions to predict trends, detect fraud, and optimize financial operations.
• Analyze and manage large financial datasets effectively using AI-driven tools and algorithms.
• Gain hands-on experience with tools and software used in financial data analytics.
• Apply machine learning models to solve real-world financial problems such as risk management, fraud detection, and customer segmentation.

Course Content:
Introduction to Financial Data Analytics, AI, and Machine Learning
• Session 1: Introduction to Financial Data Analytics
o Overview of financial data analysis and its importance.
o Types of financial data: structured vs. unstructured data.
o Key challenges in financial data analytics.
Introduction to Artificial Intelligence (AI) in Finance
o What is AI and how it impacts financial services.
o Key AI techniques: Natural Language Processing (NLP), Computer Vision, and Decision Trees.
o AI tools in finance (Python, TensorFlow, Keras, etc.).
Introduction to Machine Learning in Finance
o What is machine learning (ML)?
o Types of machine learning: supervised, unsupervised, and reinforcement learning.
o Role of machine learning in financial analytics.
o Overview of popular ML algorithms (linear regression, classification, clustering, etc.).
Techniques and Applications of AI & ML in Financial Analytics
Data Preprocessing for Financial Data Analytics
o Data collection, cleaning, and normalization.
o Handling missing values and outliers in financial datasets.
o Feature selection and dimensionality reduction.
Predictive Analytics in Finance using Machine Learning
o Introduction to predictive modeling.
o Building regression models for financial forecasting (e.g., stock prices, market trends).
o Evaluating model performance (accuracy, precision, recall).
o Case studies: Predicting financial outcomes using historical data.
Fraud Detection and Risk Management using AI & ML
o Machine learning models for fraud detection and risk analysis.
o Anomaly detection in financial transactions.
o Credit scoring and loan default prediction using ML.
o Implementing AI-driven risk management solutions.
Advanced Applications, Tools, and Hands-On Implementation
Advanced Machine Learning Models in Finance
o Neural networks and deep learning for financial prediction.
o Time series analysis and forecasting (ARIMA, LSTM models).
o Reinforcement learning for optimizing financial decisions.

DATE:
1ST BATCH: 2nd – 4th Apr, 2025
2ND BATCH: 14th – 17th Oct, 2025