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Certificate Program in AI for Derivative Valuations(CPAIDV)
Applied AI for Finance
Live Online Instructor-led Weekend Program
- English
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Indian Institute of Quantitative Finance
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Quick Facts
- Program Duration
- Program Schedule
- Program Timing
- Program Start Date
CPAIDV Program | AI for Derivative Valuations Course Highlights
The IIQF's specialized AI program focuses on AI and ML-driven modeling use cases for derivative valuations and pricing.
- Focused learning journey to cover the essentials on derivative products, derivative pricing framework & fair valuation principles.
- Insightful coverage of quantitative front-office and back-office valuation & pricing models for derivatives using AI & ML techniques & algorithms.
- Extensive coverage of the AI & ML adoption considerations, challenges & cautions - mispricing risk, pricing anomalies & disputes, pricing verifications etc.
- Practical deep-dive into AI & ML driven models, methodology, & mechanics across supervised & deep learning regimes. Coverage of best quants modelling practices and research topics in derivative valuations & pricing domain.
- Designed to deliver know-how on derivative risk analytics use cases encompassing high dimensional, simulation driven & non-linear problem sets.
- Rigorous live classroom lectures from our expert faculty panel constituting BFSI industry subject matter experts & academic researchers.
- Practical hand-on learning through Python prototyping & implementation workshops on front-to-back model building & algorithmic training exercises.
- Renders technical know-how on BFSI & Fintech industry derivative solutions & application ecosystem - architectural design & technology stack.
- BFSI industry mentor-led AI for derivative valuation & pricing capstone projects and implementation white paper writing.
CPAIDV Course Outline
Derivative Pricing & Valuation Domain Deep Dive
3 Weeks
Module 1
Imparts domain-specific know-how on relevant application use cases
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Derivative Products
- Standardized or Plain-Vanilla
- Exotic & Structured
- Linear versus Non-Linear Payoffs
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Derivative Pricing Framework
- Stochastic Random Processes
- Risk Neural Pricing
- Real World or Physical Pricing
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Derivative Portfolio Risk
- Potential Future M-t-M Exposure
- Hedging & Its Effectiveness
- Collateral Margining Requirements
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Counterparty Credit Risk Regulation
- Basel III Pillar-I RWA Ask
Numerical & AIML Models Application Use Cases
6 Weeks
Module 2
Imparts practical application of Numerical & AIML modelling methods or techniques for the specific application use-cases at hand.
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Exploratory Data Analytics
- High Dimensional Problems & Datasets
- Big Data Mining & Manipulation
- Data Diagnostics & Inferential Analytics
- Data Augmentation
- Data Visualization & Storytelling
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Model Development & Validation
- Numerical Application Use Cases
- Simulation Driven Applications
- AI & ML Application Use Cases
- Model Evaluation Metrics
- Numerical versus AI & ML Performance Comparative Analysis & Evaluation
AI & ML Adoption criteria, Considerations & Challenges
3 Weeks
Module 3
Imparts operational, strategic & technical aspects of building AI & ML in-house capability
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AI & ML Adoption Strategy
- AI & ML Driven Front-Office Pricing Models
- AI & ML Driven Valuation Framework
- AI & ML Driven Risk Management Models
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AI & ML Acceptance Strategy
- Explainable AI: AI & ML Explainability & Interpretability
- Ethical & Responsible AI - Data Privacy & Security and Model Fairness
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AI & ML Regulatory Strategy
- Regulatory Ask & Expectations
- Regulatory Acceptance Criteria
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AI & ML Technology Strategy
- AI & ML Infrastructure & Architecture
- AI & ML Front-To-Back Tech Stack
- AI & ML Automated Model Pipeline
CPAIDV Prerequisites – Need prior AI & ML technique know-how
and intermediate-level programming proficiency in Python.
| Batch | Start Date | Fee | Mode | Time |
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Faculty
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
Appliation of AIML in BFSI Industry
AI- ML Data Science Applications in Finance Podcast Series
AI- ML Data Science in Finance
Admission Process for Certificate Program in AI for Derivative Valuations
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Send Your Application
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Get on a call with a counsellor
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Wait for Application Acceptance
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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
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What are broader application use cases of Artificial Intelligence (AI) & Machine Learning (ML) in derivative valuation & pricing?
AI & ML techniques are extensively employed in derivative valuation & pricing framework for risk neural pricing & fair valuation of derivative products as well as for dynamic risk management & optimization of derivative portfolio. -
What are the desired skill sets & core competencies to be a AI & ML expert in derivative valuation & pricing domain?
AI & ML derivative valuation applications requires skill building in key learning areas like derivative products & valuation risks, high dimensional problems, non-linear estimation, AI & ML techniques, Explainable AI & Responsible AI, programming skills, AI & ML tech stack & toolset knowhow etc. -
How AI & ML models are more cutting edge than the conventional statistical models for risk management problem sets?
AI & ML models are far more capable of handling noisy data, modelling alternative datasets, building dynamic data-driven models, estimating non-linear & complex relationships, solving high-dimensional problems & many more. -
What kind of domain expertise is catered by CPAIDV certification?
CPAIDV is a specialized certification covering AI & ML algorithms and their applications in derivative valuation and pricing. This specialized application-oriented course is designed to cover the modelling essentials and AI & ML use cases for derivative products, derivative valuation & pricing and derivative portfolio risk. -
What are the key topics & learning outcomes covered by the CPAIDV certification?
CPAIDV is designed to impart technical, domain and practical use-case specific know how in below crucial areas:
- Essentials Derivative Products, Derivative Valuation & Pricing Framework
- AI & ML Application Deep Learning Neural Networks based models for Derivative Valuation & Pricing
- Explainable AI Evaluate & explain the results of the black-box Neural Networks
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Are there any prerequisites required for CPAIDV certification?
CPAIDV requires prior AI & ML technique know-how and intermediate-level programming proficiency in Python.
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