Certificate Program in Generative AI for Finance (CPGAIF)
Applied AI for Finance
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Indian Institute of Quantitative Finance
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CPGAIF Program | Certificate Program in Generative AI for Finance Course Highlights
IIQF super specialization AI program covering the Generative AI algorithmic design & application use case for financial problems.
- Focused learning journey to cover the emergence of Generative AI based product solutions & design framework for BFSI & Fintech sub-domains.
- Insightful coverage of Generative AI origination, evolution, future trends and potential research areas for BFSI sub-domains.
- Extensive coverage of the Generative AI adoption considerations, challenges & cautions - regulatory, policy, legal, compliance and ethical.
- Provide practical deep-dive into Generative AI models, methodology, and mechanics - like Large Language Models & Transformer Architectures.
- Designed to deliver know-how on BFSI financial use-cases & applications across risk, compliance, fraud analytics, portfolio management & others.
- Winder span coverage of financial use cases encompassing financial text generation, forecasting, anomaly detection, sentiment analysis & others.
- 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 & Fintech industry Generative AI application ecosystem – application architectural design & technology stack.
- BFSI industry mentor-led Generative AI capstone projects and implementation white paper writing.
About The Certificate Program in Generative AI for Finance
Generative AI - BFSI Career Roles
- Generative AI researcher
- Generative AI Data Scientist
- Generative AI engineer
- Generative AI Developer
- Generative AI project manager
- Generative AI product designer
- Generative AI Product Owner
Generative AI - BFSI Core Competencies
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Mathematical, Statistical & Probability Skills
- Descriptive statistics
- Inferential statistics
- Bayesian probabilistic framework
- Frequentist probabilistic framework -
Exploratory data analytics Skills
- New Age unstructured data Mechanics
- Alternative data sets
- Big data merging, manipulating Big Data merging, Manipulating & Mining
- Data analytics & diagnostics
- Data augmentation & Visualization -
Model development & validation skills
- AI & ML methodology & Techniques
- AI & ML model performance evaluation , validation & Explainability -
Programming Skills
- Model building & deployment in Python
- Ai & ml Infrastructure, Architecture & Tech Stack
CPGIAF - Key Learning Outcomes
- Data Augmentation -> Alternative & Synthetic Data Mining & Exploration
- Natural language processing (NLP) & Large Language Models (LLMs) ->
Pairing of NLP models with Gen AI for text summarization, translation, localization, sentiment analysis, and chatbots. - Prompt engineering & Financial ChatGPT
- Transformer architectures
- Generative Adversarial Networks (GANs)
- Variational Autoencoders (VAEs)
- Gen-AI -> Interpretability and Explainability
- Gen-ai financial applications & BFSI/fintech use cases -> Risk, Compliance, Fraud Analytics, Portfolio Management, Trading & others
- Responsible AI -> privacy & security
Certificate Program in Generative AI for Finance Course Outline
Generative AI for Finance
- Financial Text Generation
- Synthetic Data Generation
- Large Language Models (LLM) based FinGPT
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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
Admission Process in Generative AI for Finance Course
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Get Answers
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What is Generative Artificial Intelligence (AI) & how it links to the broader AI domain?
Generative AI is a specific area of artificial intelligence (AI) that deals with training the models not just to identify & learn key patterns/relationships in the existing datasets (as typically is the case in AI) but goes much beyond to leverage on that training to produce new and refined content such as ChatGPT.
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What are the desired skill sets & core competencies to be a Generative AI expert?
Generative AI requires skill building in key learning areas like alternative dataset mining , data augmentation, content analytics, Natural language processing (NLP), Generative Neural Networks (NN) , Explainable AI & Responsible AI etc.
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What the Generative AI models are capable of?
Generative AI are capable of content generation (texts, voice, images, videos, musical notes, scenarios etc.), voice recognition, creating virtual assistants, personalization and recommendations systems & many more.
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What are broader application use cases of Generative Artificial Intelligence (AI) in the BFSI & Fintech financial domain?
Generative AI has several established & emerging use cases for banks, funds, financial institutions, Fintect firms:
• Customized Wealth & Investment Management Advice
• Report Generation - Financial, Regulatory, Compliance, Audit & Others
• Automation in Accounting
• Fraud & Anti-Money Laundering Detection
• Data Privacy & Governance
• Portfolio Risk Management
• Credit Risk Management
• Customer Support Chatbots
• Market Sentiment & Customer Opinion Analysis
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What is the future outlook of Generative AI in the BFSI & Fintech financial domain?
According to MarketResearch.biz, the global market size for generative AI in financial services is projected to reach approx. USD 9.5 billion by 2032, marking a significant growth from USD 0.9 billion in 2022 while growing at an CAGR of 28.1% during the forecast period spanning from 2023 to 2032.
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What kind of domain expertise is catered by CPGAIF certification?
CPGAIF is a specialized certification for covering Gen-AI algorithms and their applications in financial sub-domains like Risk, Compliance, Fraud Analytics, Portfolio Management, Trading & others
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What are the key topics & learning outcomes covered by the CPGAIF certification?
CPGAIF is designed to impart technical and practical use-case specific know how in below crucial areas of Generative AI:
• Alternative Dataset Mining & Exploration -> Large modellable datasets with alternative unstructured data like textual, image, video, signal, social networking & other data.
• Data Augmentation -> Generating synthetic data for training AI & ML models to overcome data scarcity issues and improves the overall performance and generalization of AI models.
• Natural language processing (NLP) -> Learning the Large Language Models (LLMs)
• Generative Neural Networks (NN) & NN Design Architectures -> Transformer architectures, Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs)
• Explainable AI & Responsible AI -> Model interpretability, Data privacy & security
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Are there any prerequisites required for CPGAIF certification?
CPGAIF requires prior AI & ML technique know-how and intermediate-level programming proficiency in Python.
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Can you exemplify any NLP driven use case of Gen AI?
Below highlights a BFSI use case of NLP driven Generative AI:
• FinGPT is part of the FinNLP project, which aims to democratize Internet-scale financial data and provide accessible tools for language modeling in finance.
• FinGPT leverages the strengths of existing open-source large language models (LLMs) and is fine-tuned using financial data for language modeling tasks in the financial domain.
• FinGPT will generate responses for sentiment analysis prompts and predict sentiment categories based on those responses. This can be leveraged to analyze the sentiment of multiple financial news articles or other financial data and obtain the output as negative, neutral, or positive.
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What kind of Gen AI specific career opportunities & avenues available in the broader financial sector?
The BFSI, Fintech & Financial Product/services/consulting firms offer a variety of career avenues & roles like Generative AI researcher, Generative AI Data Scientist, Generative AI Engineer, Generative AI Developer, Generative AI Project Manager, Generative AI Product Designer, Generative AI Product Owner
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