Master AI in Finance — From Python to Production Models
Build machine learning and deep learning models for algorithmic trading, fraud detection, credit scoring, and robo-advisory. 4-module programme with hands-on Python labs, real finance datasets, and an industry capstone.
Get batch timings, fee structure & syllabus breakdown.
Preparing the next generation of finance technologists
Hands-on Lab Access with Real-World Datasets
Project-First Curriculum Built by Practising AI Engineers
Clear Bridge from Theory to Industry-Ready Tools
Placement Support into High-Growth AI-Finance Roles
One technology, multiple financial futures
Artificial Intelligence in Finance refers to the application of machine learning, deep learning, natural language processing, and data science to financial services — automating risk models, powering algorithmic trading, detecting fraud, personalising wealth advice, and transforming traditional banking workflows. This practical programme is designed to take students from foundational statistics and programming to advanced deployment of AI systems that real fintechs, banks, and hedge funds use daily.
Built for the finance-tech generation
Commerce, Finance, Economics, and Computer Science graduates/postgraduates planning a career in AI-driven finance
Finance and analytics professionals looking to upskill in machine learning, Python, and model deployment
Programmers and data analysts transitioning into quantitative finance roles
MBA/postgraduates with a background in finance, economics, or engineering
4 modules. 16 weeks. From Python to Production.
- Python for finance (NumPy, Pandas, Matplotlib)
- Statistical inference & hypothesis testing
- Financial time-series analysis & forecasting
- Intro to ML libraries (scikit-learn, XGBoost)
- Data cleaning & feature engineering for financial datasets
- Supervised learning — credit scoring & loan default prediction
- Unsupervised learning — customer segmentation & anomaly detection
- Reinforcement learning — algorithmic trading & portfolio optimisation
- NLP for financial sentiment & earnings call analysis
- Time-series forecasting — ARIMA, LSTM, Prophet
- Neural networks & deep learning fundamentals (TensorFlow/Keras)
- Deep learning for fraud detection & AML
- Transformer models for financial text & news sentiment
- Computer vision in banking — document processing & KYC
- Model explainability & interpretability (SHAP, LIME)
- Model deployment & MLOps (Flask, Docker, FastAPI)
- Risk management & model governance
- Regulatory compliance & ethical AI in finance
- Capstone: end-to-end AI solution for a real finance problem
- Portfolio & presentation to industry mentors
Tools & Libraries You Will Master Hands-On
From exploratory finance data science in Python to production MLOps deployment.
Batch Schedules Built for College Students & Working Execs
Learn without leaving your current college lecture schedule or full-time corporate role.
Saturday & Sunday Masterclasses
4-hour intensive weekend practical labs designed specifically for working financial analysts, accountants, and outstation learners.
Post-College Hybrid Track
Monday to Thursday 6:00 PM – 8:00 PM sessions allowing B.Com, BBA, and economics undergraduates to upskill after university hours.
100% HD Recordings & Code Reviews
Every session is recorded with starter repos on GitHub, automated grading tests, and 1-on-1 weekly mentor doubt resolution.
Practical learning, real outcomes
100+ hours of guided coding - not just theory. Build real models from day one.
Focused cohorts so every student gets personalised feedback on complex algorithms.
Work on live datasets from fintechs, banks, and hedge funds - real problems, real impact.
Guidance from practising AI engineers and quant researchers for placement and portfolio building.
One AI programme, three ways in
Job-ready, capstone-first, or school-level — pick the variant that matches where you are today. Each pathway has its own detailed brochure.
Practical 8–12 week track into machine learning for trading, fraud detection, credit scoring and robo-advisory — for graduates and working professionals.
Download Full Programme BrochureA project-first track — pick a real finance problem, build and deploy an end-to-end model, and present to industry mentors for certification.
Download Capstone BrochureEarly computational thinking and applied AI for Class 11 & 12 commerce students and early B.Com learners.
Download School BrochureWho is this programme for?
- Graduates/postgraduates in Commerce, Finance, Computer Science, Economics, Mathematics, or Engineering
- Finance professionals seeking to add AI/ML skills to their toolkit
- Programmers and data analysts transitioning into quantitative finance
- MBA or postgraduates with a background in finance, economics, or engineering
Roles our AI graduates step into
What you'll be able to do after this programme
- Build and deploy machine learning models for credit scoring, fraud detection, and algorithmic trading using real financial datasets
- Apply deep learning techniques (LSTM, transformers) for time-series forecasting and financial text analysis
- Implement MLOps pipelines with proper model validation, explainability, and regulatory compliance
- Deliver an industry-grade capstone project solving a real finance problem, reviewed by practising AI engineers
Download the full AI in Finance brochure with module-wise breakdown, project details, faculty profiles, and batch calendar.
Download AI in Finance Brochure (PDF)Master AI in Financial Services
Get a syllabus breakdown, batch timings, and a fee structure customised to your background.