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Certificate Program in AI for Finance (CPAIF)
Build your career in AI for Finance Applications
Live Online Instructor-led Weekend Program
- English
-
Indian Institute of Quantitative Finance
- Book a guidance call
Quick Facts
- Program Duration
- Program Schedule
- Program Timing
- Program Start Date
CPAIF Program | AI for Finance Course Highlights
IIQF integrated flagship program covering Data Science (DS) , Machine Learning (ML) & Artificial Intelligence (AI) for Financial Applications
- Dedicated learning journey tracks for exhaustive coverage of Data Science (DS), Machine Learning (ML) & Artificial Intelligence (AI) methodology, techniques & toolsets
- Deep-dive coverage of Big Data Analytics & Decision Science, Supervised Learning, Semi-Supervised Learning, Deep Learning, Reinforcement Learning, Natural Language Processing, Model Evaluation, Model Optimization, Model Validation, Model Benchmarking & Model Explainability
- Focused on building skills & core competencies from scratch-up - Statistics, Probability, Mathematics, Programming, Analytics, Algorithmic Design & Modelling
- Designed to deliver know-how on BFSI financial use-cases & applications across specialized areas of Risk, Trading, Pricing, Quants & Generative AI
- Rigorous live classroom lectures from our expert faculty panel constituting BFSI industry subject matter experts & academic researchers
- Practical hands-on learning through Python prototyping & implementation workshops on front-to-back model building & algorithmic training exercises
- Renders technical know-how on BFSI industry adoption of AI & ML technology stack, business intelligence (BI) toolkit & deployment infrastructure
- Coverage of evolving DS, AI & ML areas like Large Language Models, Generative AI, Fraud Risk, Climate & Sustainability Risk
- BFSI industry mentor-led DS, AI & ML capstone projects and implementation white paper writing
About The Certificate Program in AI for Finance
AI BFSI Career Roles
- BIG Data Scientist
- BIG DATA Strategist
- BIG Data Analytics Expert
- Synthetic Data Expert
- BIG Data Engineer
- Big Data Platform Expert
- Big Data Owner
- AI & ML Researcher
- AI & ML Domain Expert
- AI & ML Model Validator
- Explainable AI & ML Expert
- AI & ML Project Manager
- AI & ML Product Designer
- AI & ML Product Owner
AI BFSI Core Competencies
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Mathematical, statistical & probability skills
– Differential & integral calculus
- Linear algebra & matrix operations
- Vectorized calculations
– Descriptive & inferential statistics
- Random events, experiments & expectations -
Exploratory data analytics skills
- Big Data merging, manipulating & mining
- New Age unstructured data Mechanics
- Data augmentation & visualization
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Model development & validation skills
- AI & ML methodology & Techniques
- AI & ML model performance evaluation, validation & explainability
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Programming Skills
- Model building & deployment in Python
- Ai & Ml Infrastructure, Architecture & Tech Stack
CPAIF - Key Learning Outcomes
- Big Data Science
- Structured & Unstructured Datasets
- Data Diagnostics, Distribution & De-noising
- Data Mining, Exploration, MANIPULATIONS & Transformations
- Data Augmentation & Synthesizing
- Data Visualization & Storytelling
- Supervised learning techniques
- Unsupervised learning techniques
- Deep learning techniques
- Reinforcement learning techniques
- Large natural language learning
- Model optimization techniques
- Model performance evaluation
- AI & ML expandability
- Model validation testing
Also Available as
Individual Certificate Programs
Individual Certificate Programs
CPAIF Integrated Learning Tracks
9 Months
-
CPDSF
3 Months
-
CPMLF
4 Months
-
+ Elective*
2 Months
and Capstone Project Assignments (*You can take one
or
more electives)
CPAIF Track I (CPDSF)
Module 1 - Applied Programming in Python
3 Weeks
- Python Toolsets & Libraries –
- Pandas, Numpy, Scipy
- Matplotlib, Seaborn, Bokeh, Plotly - Business Intelligence Tools & Software
- Tableau, Power BI, Qlik
Module 2 - Applied Statistics & Probability
3 Weeks
- Multivariate Statistics
- Distribution Families – Discrete & Continuous
- Sampling Estimation & Central Limit Theorems
- Decision Theory & Science
- Random Experimental & Probabilistic Framework
- Bayesian Theory, Hypothesis Testing, Inference and Confidence Intervals
Module 3 - Big Data Mining & Manipulation
2 Weeks
- 7 V’s of Big Data - Volume, Velocity, Variability, Variety, Veracity, Value, Visualization
- Structured & Unstructured Datasets – Data Massaging & Manipulations
- High Dimensional Data Handling
- Financial Datasets - Risk, Treasury, Front Office Pricing & Valuation, Trading, Climate & Sustainability
Module 4 - Exploratory Data Analytics (EDA)
5 Weeks
- Data Extraction, Exploration & Error Handling
- Data Cleansing, Transformation & Aggregation
- Data De-noising, Distribution Fitting & Descriptive Statistics
- Data Augmentation, Generation & Synthetization
- Data Statistical Inference & Insights
- Data Visualization & Storytelling
CPAIF TRACK II (CPMLF)
Module 1 - Applied Mathematics for ML
2 Weeks
- Statistical & Probability Theory & Applications
- Linear Algebra & Matrix Vectorized Operations
- Differential Calculus
- Integral Calculus
- Functional Estimation & Global Minima/Maxima Based Optimization
- Mathematical Modelling & Formulation
Module 2 – ML Supervised Learning Methods
8 Weeks
- Statistical & ML Driven Regression - OLS, MLE, LASSO, RIDGE, Elastic-Net
- Statistical & ML Driven Classification - Linear Classifiers (Logistic Logit, Probit Regression), Bagging (CART Decision Tree, Random forest), Boosting (Ada-Boost, XG-Boost), Support Vector Machines (SVM)
- ML Model Hyper-Parameter Tuning & K-Fold Cross Validation
- Machine Learning Model Optimization, Performance Evaluation & Model Explainability
- ML Quantitative Validation Tests
Module 3 - ML Un-Supervised Learning Methods
2 Weeks
- Unsupervised & Semi-Supervised Learning
- Statistical & ML Driven Clustering & Association - Hierarchal Clustering & Discriminant Analysis, K-Means Clustering, K-Nearest Neighbors (KNN)
Module 4 - ML Deep Learning Methods
5 Weeks
- Deep Learning - Neural Network (DNN) Intro
- Multi Layer Perceptron (MLP)
- Artificial Neural Network (ANN)
- Convolutional Neural Networks (CNNs)
- Recurrent Neural Network (RNN)
- RNN-Long Short Term Memory (LSTM)
- RNN - Gated Recurrent Unit (GRU)
- Deep Reinforcement Learning (DRL)
Module 5 - ML Natural Language Models (NLP)
3 Weeks
- Unstructured Data Sets & Transformations
- ML Driven Textual & Speech Processing
- ML Driven Document Classification
- ML Driven Image Classification
- ML Driven Chat-bots
Module 6 - Capstone Project
1 Month
- BFSI application project supervised by a BFSI SME expert industry mentor
CPAIF TRACK III (Electives)
As part of CPAIF integrated program, IIQF offers any one out of multiple elective specialisation courses however candidates can register for any additional elective by enrolling separately as per the course pricing terms & conditions. At the time of enrolment, one has to choose any one elective from the given options. You can select any one elective from the list provided below:
AI for Risk Management
10 Weeks
- Financial Risk Prediction & Estimation
- PD, LGD, EAD, IFRS9 ECL Provisions, Fraud Detection & Forensic Audit - Financial Time Series Forecasting
- Loss (Value-at-Risk/Expected Shortfall), Pre-Provision Net Revenue (PPNR) - Financial (Un-) Constrained Optimization
- Portfolio (CAPM) Optimization, RWA Optimization, Optimal & Effective Hedging - Financial Stress Loss Analytics
- Synthetic Stress Scenarios & Shock-Sizing, Stress Testing & Reverse Stress Testing - Financial Unstructured Data Mining & Analytics
- Image Processing & Classification, Risk Sentiment Indicators
AI for Derivative Valuation
10 Weeks
- Financial Risk Prediction & Estimation
- Financial Instrument Pricing (Equity, Fixed Income, Commodity, IR, FX, Alternative Asset Classes), Derivative Pricing & Linear Factor Models, Derivative Valuation Adjustments (XVAs – CVA, DVA, MVA, FVA), P&L Attribution - Financial Time Series Forecasting
- Volatility, Correlations & Covariance, Dynamic Hedging Strategy - Financial Unstructured Data Mining & Analytics
- Pricing Sensitivity & Sentiment Analysis
AI for Trading
10 Weeks
- Automated Algorithmic Trading
- Trade Execution Algorithms, Strategy Implementation Algorithms, Stealth/Gaming Algorithms, Arbitrage Exploitation Algorithms - High-frequency Trading
Generative AI for Finance
10 Weeks
- Financial Text Generation
- Synthetic Data Generation
- Large Language Models (LLM) based FinGPT
Capstone Project
1 Month
- BFSI industry application project supervised by a BFSI SME expert industry mentor
| Batch | Start Date | Fee | Mode | Time |
|---|
Why choose AI for Finance Course?
AI- ML Data Science Applications in Finance Podcast Series
AI- ML Data Science in Finance
Faculty for AI for Finance
SANJAY BHATIA
Background
- Director in UBS - Risk Modelling & Analytics, Model Risk Management & Control, Chief Risk Office (CRO) Function
- MBA-Finance & MSc in Machine Learning & Artificial Intelligence from Liverpool John Moores University (LJMU)
- Post-Graduate Diploma in Machine Learning & Artificial Intelligence from IIIT-Bangalore
- Domain SME on Credit Risk , Derivatives Counterparty Credit Risk, Derivative Pricing, Stochastic Modelling, Stress Testing
ML Expertise (Teaching ML for Quantitative Finance & Risk Management)
- Financial Prediction (Regression & Classification ) - Lasso/Ridge Regression, CART Decision Trees, Ensemble Learning (Bagging & Boosting) & Support Vector Machines (SVM)
- Financial Time Series Forecasting - (Recurrent) Neural Networks, RNN-LSTM, RNN-GRU, Hybrid-RNN-LSTM-GRU
- Financial Instrument Pricing - Non-Linear & High Dimensional Derivative Pricing using Neural Networks
- ML Model Optimization – Hyperparameters Tuning K-Fold Cross-Validation, Stochastic Gradient Descent, Convergence etc.
- Regulatory & Industry ML Adoption, Challenges & Use Cases – Model Explainibility, Performance Evaluation & Testing
Prof Rituparna Sen
Background
- - Associate Professor at the Applied Statistics Division, Indian Statistical Institute, Bangalore.
- - She worked as Assistant Professor at the University of California at Davis from 2004–2011
- - She has also taught courses in Chennai Mathematical Institute and Madras School of Economics.
- - An elected member of the International Statistical Institute
- - A council member of the International Society for Business and Industrial Statistics.
- - She has been awarded the Young Statistical Scientist Award by the International Indian Statistical Association, the Best - Student Paper Award by the American Statistical Association and the Women in Mathematical Sciences award by Technical University of Munich, Germany.
ML Expertise (Teaching ML Statistics for Computational Finance)
- - Authored A Book on Computational Finance with R
- - Authored 30+ Research Papers & Articles
- - Editor: Journal of Applied Stochastic Models in Business and Industry
- - Member of CAIML (Center for Artificial Intelligence and Machine Learning) at ISI
- - Associate Editor of several other journals
- - Guided several masters students on theses in the ML area
Dr. Arindam Chaudhuri
Background
- Worked as post doctoral fellow with Department of Computer Science, University of Copenhagen and Department of Computer Science, Technical University of Berlin.
- Worked as researcher with Siemens Research Labs Amsterdam and Samsung Research Labs at New Delhi & Bangalore.
- His current research interests include business analytics, artificial intelligence, machine learning, deep learning.
- He has published 4 research monographs and 60 articles in international journals and conference proceedings.
- He has served as reviewer for several international journals and conferences.
ML Expertise (Teaching ML Statistics for Computational Finance)
- Implemented Machine Learning Methods - Support Vector Machines, Artificial Neural Networks, Deep Learning Networks, Clustering, Genetic Algorithms and Evolutionary Computing with basic mathematical foundations of probability theory, fuzzy sets, rough sets, possibility theory and a variation of these for solution of various business problems.
- Integrated various artificial intelligence methods to form different soft computing frameworks such as neuro-fuzzy, fuzzy-genetic, neuro-genetic and rough-neuro-fuzzy-genetic.
- Successfully applied these methods for different categories of industrial problems such as decision theory, time series forecasting and prediction, image compression, sentiment analysis, recommendation systems, social networks analytics in order to achieve better results
Ritesh Chandra
Background
- - B. Tech. (IIT-Kanpur), PGDM (IIM-Calcutta), CFA
- - 15 years banking experience in risk management with domestic and MNC banks
- - Member, Board of Studies in the area of Finance at IMT-CDL, Ghaziabad
ML Expertise
- - Passionate about teaching. Has been conducting workshops / training programmes for the last 8 yrs in areas of Quantitative Finance, Financial Management, Risk Management and Machine Learning
- - Has contributed to a book on Applications of Blockchains in Financial Services industry
- - Has worked as visiting faculty with several institutions.
Rupal
Background
- B.Tech from IIT, Kanpur and Executive MBA from IIM Kozhikode.
- Currently working as Vice President, Fixed Income at one of the largest International Bank for their Corporate Investment Banking Division.
- Prior to this he was working as Assistant Vice President at Credit Suisse, Investment Banking Division.
- Regularly worked as an internal trainer in the organizations that he has worked in.
ML Expertise (Teaching ML Application for Financial Systems)
- Passionate about teaching, he has been conducting workshops and training programs on Machine Learning & Data Science.
- His areas of interest are fixed income pricing, financial analytics and statistical learning
Dr. Hari
Background
- M.Sc. in Mathematics from B.H.U. Varanasi.
- Awarded four gold medals because of his outstanding performance in B.H.U.
- He has completed his Ph.D. in Mathematics from Indian Institute of Science, Bangalore.
- He has also worked with Deep Value (Algorithm trading firm).
- Currently involved with one of the world's leading analytics product company.
ML Expertise (Teaching ML Application for Algorithmic Trading)
- Passionate about teaching, he has been conducting workshops and training programs on Machine Learning for Trading.
- ML applications for Algorithmic Trading, High Frequency Trading & Quantitative Analytics
Admission Process in Artificial Intelligence for Finance Course
-
Send Your Application
-
Get on a call with a counsellor
-
Wait for Application Acceptance
-
Pay the fee & join the upcoming batch
Finance your Study
Educational Loans
We are very happy to help you progress to greater heights in your career in every way possible. Education loans available at 0% interest for full time Indian residents. Easy EMI plans available.
Student Aid
Encourages the full time students to enter this domain, benefits, if you are still pursuing formal education.
Get Answers
-
Is this course suitable for beginners in AI and machine learning?
CPAIF is designed to build skills and core competencies from scratch-up by covering the ground on below building blocks:• Statistics • Probability
• Mathematics
• Programming
• Analytics
• Algorithmic Design
• Model Development, Evaluation, Validation, Benchmarking & Explainability -
What kind of software tools and platforms are used in the course for AI applications?
Data Science, Machine Leaning & Artificial Intelligence heavily draws upon the below technology & toolset stack:- • Big Data Platforms & Infrastructure - Apache Hadoop, Apache Spark, Apache Hive, Apache HBase, Google Cloud BigQuery, Amazon EMR, Microsoft Azure HDInsight , Cloudera, IBM InfoSphere BigInsights, Databricks etc.
- • Business Intelligence & Visualization Tools – Plotly, Tableau, Microsoft Power BI, Qlik Sense, MicroStrategy etc.
-
What programming languages are essential for studying AI in finance?
Data Science, Machine Leaning & Artificial Intelligence applications are Programming in Analytics Language like Python, R, Julia with the most prominent being Python which has rich extensive libraries with potent functions to support AI & ML methods. IIQF offers Machine Learning for Finance in Python language. -
Is prior experience in finance required for enrolling in this course?
This course is designed to cover the essentials on statistics, probability, mathematics & programming however having some background on these areas always helps in expediting the learning journey. -
What are the prerequisites for this course in terms of mathematics and statistics?
This AI course in finance is designed to cover the essentials on statistics and mathematics however some prep work on central limit theorem, statistical distributions, probability theory, linear algebra, vectors, matrices, differential calculus and integral calculus would always put somebody on a stronger footing. -
How are AI techniques applied specifically to financial analysis and decision-making?
AI & ML has several applications & use-cases in the financial sub-domains of Risk (Credit/Counterparty Credit/Market/ Operations/ Enterprise), Trading Algorithms & Strategies, Front-Office Valuation & Pricing, Generative-AI. AI & ML techniques provides a data-driven way, instead of theory-driven way, of performing financial analytics & modelling for specific domain-centric use cases. -
How does the course stay updated with the latest developments in AI and finance?
This course exhaustively covers AI & ML techniques, methodology & applications for finance. Its coverage also extensively includes emerging topics like AIML Benchmarking, AIML Validation, Explainable AI etc. -
Can students customise their learning paths within the AI for Finance program?
Please explore different certification programs CPDSF, CPMLF and CPAIF specialization in Risk, Trading, FO Pricing & Valuation and Generative AI which are designed to enable focussed learning paths & tracks to move towards developing the capability in AI for Finance. -
Does the course cover specific AI techniques relevant to finance, such as predictive modelling or algorithmic trading?
Yes, The course covers specific use-cases on predictive modelling like Probability of Default (PD) rating or scorecard development, Loss Given Default (LGD) estimation, Exposure At Default (EAD) estimation, market index forecasting etcetera and on algorithmic trading it trade execution & strategy implementation algorithms. -
Does the course offer hands-on experience with AI tools and platforms commonly used in the finance industry?
Yes, the course specifically covers exploratory data analytics and data visualization in Python and industry-prevalent BI tools. -
Are there any additional resources or learning materials provided apart from the regular curriculum?
Yes, besides the comprehensive and curated topic-specific learning material, the course also provides additional resources like relevant industry or academic research papers, implementation white papers, case studies, reference books/articles/journal papers etcetera. -
Are there opportunities for practical AI implementation in finance projects?
Yes, the course covers specific milestones for model prototyping in python for financial use-cases as well as provides a mentor-led support for the completion of the domain-specific capstone project. -
What is the tuition fee structure for the AI for Finance course?
Please refer to the specific webpages for the certification program for fee structure, details and promotional offers. Also you can directly get in touch with the IIQF counsellors for fee related details. -
How does the course prepare students for real-world AI challenges in finance?
The course provides deep-dive into statistical, mathematical and methodological know-how on AI & ML techniques along with real-world use-cases, application areas & implementation nuances for end-to-end AI & ML capability enhancement. -
What are the unique advantages of choosing your AI for Finance program over others?
CPAIF is a uniquely tailored program which comprehensively covers the methodological details of Data Science, Machine Learning & Artificial Intelligence with a lens focus on application areas in various sub-domains of Finance. CPAIF aims to build domain-specific subject matter expertise by going far beyond the generic, selective, simplistic and domain-neutral design of other certifications offered in the market which are only as good as a beginner sneak peek into the AI & ML subject. -
Are you someone with a knack for mathematics but little to no background in finance?
IIQF being a premier institute for quantitative finance and risk management, upholds the time-tested credentials for building financial quants aptitude for aspirants with a knack for mathematics. -
What is the scope of AI in finance at this point?
The scope of AI has penetrated all financial sub-domains like Risk Quantification & Management, Trading Platforms & Strategies, Front-Office Pricing & Valuations, Large Language Models, Generative AI, as well as emerging topics like Climate & Sustainability Risk. -
What is the difference between AI for finance and algorithmic trading?
AI for finance covers AI & ML applications to the specialized sub-domain of financial market trading to design dynamic algorithms for AI driven trade execution, trading strategy design, high-frequency trading etc. On the other hand, Algorithmic trading is a specialized certification providing the theoretical background around trading/ market making activities, algorithmic trading platforms, statistical/mathematical models, Algo-driven strategies and trade execution applications. -
What is the difference between AI for finance and financial engineering?
AI for finance covers AI & ML applications to the specialized sub-domain of financial engineering to design data-driven and dynamic algorithms for pricing, valuation, volatility, P&L forecasting problems etc. On the other hand, financial engineering is a specialized certification providing the theoretical background and stochastic modelling concepts around quant finance, financial security pricing, econometric concepts, plain-vanilla and exotic derivative pricing etcetera. -
What are the BFSI career roles in the area of AI for finance?
AI for finance has a wider span of promising career roles for professionals from IT/ITES, quants, statistics, risk/trading/front-office/strategy domains across main street banks, investment funds, trading firms, IT service & product companies, consulting firms. Below indicate the predominant career role families for AI experts in finance:- • AIML Algo Designer
- • AIML Developer
- • AIML Product Owner
- • AIML Methodology Researcher
- • AIML Model Optimization Expert
- • AIML Model Validator
- • Explainable AIML Expert
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