Certificate in Artificial Intelligence for Quantitative Finance

Certificate in Artificial Intelligence & Quant Finance

Program Details

Ideal for working professionals as well as students
Seven months (320 hours)
INR 1,79,500 (All inclusive) for participants in India
USD 2,450 for those registering from outside India
Super Early-bird discounts, Early-bird discounts, group discounts and special prices for students are available. Please contact us for more information
Fee payable in 3 installments
Classes are held on weekends, i.e., Saturdays and Sundays in the afternoon 3–8 pm IST (9:30 am–2:30 pm GMT).
Lectures will be live i.e. led by the instructor in real-time
Recording of each class will be available to all students

Eligibility

Who should attend?

If you aspire to join the elite ranks of tech-driven finance professionals and quantitative experts, this program is for you.
If you wish to be part of the story that is unfolding in AI and Machine Learning and want to integrate this emerging technology with quant finance to be at the forefront in the financial industry.
No prior background in AI or Quantitative Finance is required—the program is designed to build expertise from the ground up.
Suitable for software professionals seeking to pivot into the fintech or quant finance space.
Open to students and graduates from engineering, physics, mathematics, statistics, economics, operations research, and MBA programs.

Curriculum: A bird’s eye view

1. Foundation in Maths

(Asynchronous) Mathematics for Quant Finance

Matrices, Ordinary and partial differential equations, Sequences and series, Taylor series.

Probability and Statistics

Basics of probability, Distributions, Expectation, Functions of a random variable, Moment generating function, Central Limit Theorem.

2. Explore the World of Finance

Foundations of Finance and Bond Mathematics

Forward rates, Yield-to-maturity, Duration, Convexity, Hedging with bonds, Floating rate notes, Interest Rate Swaps.

Options

Arbitrage Relationships for Option Prices, Put-Call parity, Trading strategies involving options, Two-step and multi-step Binomial Tree for option pricing.

3. Essential Tools

Python Programming for Finance

This course builds Python programming skills with emphasis on data types, functions, modules, OOP, and file handling. It also introduces NumPy, Pandas, Matplotlib, and Seaborn for data analysis and visualization.

Time Series Modelling and Forecasting

Covers AR(p) and MA(q) models (stationarity, invertibility, GE log returns examples), ARIMA framework (differencing, model selection via AIC/BIC), and forecasting with ARCH/GARCH for volatility clustering in financial data.

4. Heart & Soul of Quant Finance

Stochastic Processes for Quant Finance

Definition and classification of stochastic processes, discrete-time and continuous-time processes, martingales, Brownian motion.

Stochastic Calculus for Derivative Pricing

A rigorous introduction to stochastic calculus and its applications in finance. Arbitrage-free valuation of contingent claims derived by martingale methods. Black-Scholes option pricing theory. Applications to complex contingent claims.

5. Numerical Methods

Monte Carlo Simulation for Financial Engineering

Generation of random numbers, Inverse Transform, Acceptance-Rejection Method, Pricing Path-Dependent Options, Variation Reduction Techniques, Control Variates, Antithetic Variates, Envelop Rejection method, Box-Muller method, Quasi Monte-Carlo Simulation

6. Credit Derivatives

Modelling and Valuation of Credit Derivatives

Structural Models, Reduced Form Models, The Hazard Rate Model, Valuation of a CDS, Calibrating the CDS Survival Curve, CDS Risk Management.

7. Get your hands dirty with AI!!

Machine Learning: Algorithms, Models, and Performance Analysis

This course introduces core machine learning concepts, including supervised, unsupervised, and reinforcement learning. It covers regression, classification, decision trees, probabilistic models, and evaluation metrics for practical applications.

Advanced Concepts in Machine Learning and Deep Learning

This course focuses on ensemble methods, clustering algorithms, reinforcement learning basics, and neural network foundations. Students gain insights into backpropagation, activation functions, and introductory convolutional neural networks.

Advanced Deep Learning and Reinforcement Learning

This course explores advanced deep learning architectures (CNNs, GANs, LSTMs) and reinforcement learning methods. Learners practice with NLP, model optimization, and tools like TensorFlow, Keras, and TensorBoard.

8. AI Applications in Quant Finance

Transforming Finance with AI: Trading, Sentiment Analysis, and Risk Modelling

Explores AI and ML application in Finance, including algo trading, feature engineering, time series application and regulatory standards.

Advanced AI in Finance: Volatility Prediction, Stress Testing, and Risk Modeling

Covers AI-driven financial analysis, including sentiment analysis, fraud detection, volatility modeling & model interpretability.

What Distinguishes Certificate in AI & Quant Finance of SQF?

World-class faculty
Curriculum at par with the world’s best
In-depth coverage advanced AI and its applications to finance
Unmatched rigor in theoretical underpinnings
Hands-on implementation of the cutting-edge models
Specialized support for interview preparation to secure quant roles at leading financial firms.

Career Opportunities

Graduates of the program will be well-positioned for a wide range of high-demand roles at the intersection of AI, machine learning, and quantitative finance, including:

AI/ML Domain Expert
Machine Learning Modeler – Financial Forecasting & Analytics
Fraud Analytics Specialist using ML Techniques
Data Engineer – Risk, Finance & Compliance
Quantitative Modeler – Machine Learning for Financial Markets
Algorithmic Trader – ML-Driven Strategies
Model Validation Expert – ML Models in Finance
AI/ML Research Scientist – Financial Applications
AI/ML Model Validator
AI/ML Algorithm Designer
Big Data Strategist for Financial Services
BFSI Data Scientist (Banking, Financial Services, Insurance)
Model Validation Quant – Independently implements and verifies pricing models to ensure accuracy of front-office models
Quant Developer – Implements financial models in major programming languages for testing and product development (ideal for tech professionals)

Still have questions?

Email: info@sqf.org.in

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