Executive Program in Financial Analytics leveraging Data Analytics, ML & AI

Duration: 100 hours
Format: Online/Hybrid, Instructor-led sessions, Hands-on projects, Industry case studies
Tools Covered: Excel, SQL, Python, SAS JMP, Power BI, Scikit-Learn, TensorFlow/Keras, NLP Libraries (NLTK, SpaCy, Transformers)

Course Description

The Executive Program in Financial Analytics leveraging Data Analytics, ML & AI equips finance leaders and professionals with advanced analytical and AI-driven capabilities to make smarter, faster, and data-driven financial decisions.

Participants will learn to extract insights from financial data, design predictive and prescriptive models, and implement AI solutions for risk management, investment analytics, forecasting, fraud detection, and regulatory compliance.

The program combines finance domain expertise with hands-on training in analytics, machine learning, and AI tools, culminating in a Capstone Project on real-world financial datasets.

Program Learning Outcomes

By the end of the program, participants will be able to:

• Leverage data analytics & visualization tools to monitor and optimize financial performance.
• Apply statistical & ML models for forecasting, valuation, credit scoring, and portfolio optimization.
• Implement AI-driven fraud detection, trading, and risk management solutions.
• Use dashboards and storytelling with data to communicate insights effectively to stakeholders.
• Drive data-led transformation in finance and corporate strategy.

100-Hour Course Curriculum

Module 1: Financial Analytics Foundations (8 Hours)

• Role of Analytics in Financial Decision-Making
• Financial Data Ecosystem (Markets, Banking, Corporate Finance)
• Key Metrics: Liquidity, Profitability, Risk Ratios
• Case Study: Traditional vs. AI-Driven Finance

Tools: Excel, SQL, Python (Pandas)

Module 2: Data Analytics for Finance (15 Hours)

• Data Cleaning & Preparation for Financial Datasets
• Exploratory Data Analysis (EDA) & KPI Insights
• Data Visualization for Finance (Cash Flow, Valuation, Risk)
• Building Financial Dashboards

Tools: Excel (Power Query), SQL, Python (Pandas, Matplotlib, Seaborn), Power BI

Hands-on: Financial Performance Dashboard

Module 3: Statistics & Predictive Modeling (12 Hours)

• Probability Distributions in Finance (Normal, Lognormal, Fat-Tailed)
• Hypothesis Testing for Returns & Risk
• Regression Models for Forecasting Prices & Valuations
• Time-Series Models: ARIMA, ARCH/GARCH

Tools: Python (Statsmodels, Scikit-Learn), SAS JMP, Excel Solver

Case Study: Loan Default Forecasting

Module 4: Machine Learning in Financial Analytics (20 Hours)

• ML for Finance: Supervised vs. Unsupervised Applications
• Classification Models: Logistic Regression, Random Forest, XGBoost
• Clustering for Customer Segmentation & Credit Risk Profiling
• NLP for Sentiment Analysis (Earnings Calls, News, Social Media)

Tools: Python (Scikit-Learn, XGBoost, SpaCy, Transformers), Jupyter Notebook

Hands-on: Credit Risk Scoring using ML

Module 5: AI Applications in Finance (20 Hours)

• Deep Learning for Stock Price & Portfolio Forecasting
• Neural Networks for Asset Allocation
• Fraud Detection using Anomaly Detection & Autoencoders
• AI in Corporate Finance – Predictive Liquidity & Cash Flow Forecasting

Tools: Python (TensorFlow, Keras, PyTorch, AutoML), NLP (Hugging Face)

Hands-on: AI-Driven Fraud Detection Model

Module 6: Risk Management & Regulatory Analytics (10 Hours)

• Market, Credit & Operational Risk Analytics
• Stress Testing & Scenario Simulations
• AML/KYC & Regulatory Compliance (Basel, IFRS 9)
• ESG & Sustainable Finance Analytics

Tools: Python (Monte Carlo, Scikit-Learn), R (RiskMetrics), Power BI

Case Study: Stress Test of a Bank Balance Sheet

Module 7: Capstone Project (15 Hours)

End-to-End Financial Analytics Solution using real-world datasets.
Sample Project Tracks:

1. Credit Risk Scoring (ML-based)
2. AI-Powered Fraud Detection
3. Stock/ETF Portfolio Optimization
4. Predictive Cash Flow Forecasting
5. ESG & Sustainable Finance Dashboard

Deliverables:

• Technical Notebook (Python/R/Excel)
• Interactive Dashboard (Power BI/Tableau)
• Executive Summary & Presentation

Time Allocation (100 Hours)

• Module 1: 8 Hrs
• Module 2: 15 Hrs
• Module 3: 12 Hrs
• Module 4: 20 Hrs
• Module 5: 20 Hrs
• Module 6: 10 Hrs
• Module 7 (Capstone): 15 Hrs

✅ Total = 100 Hours

Certification & Assessment

• Module Quizzes & Assignments: 20%
• Case Studies: 30%
• Capstone Project: 50%
• Certification: Executive Program in Financial Analytics leveraging Data Analytics, ML & AI