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CPAIRM Program | AI for Risk Management Course Highlights
The IIQF’s Super Specialization AI Program focuses on AI and ML-driven risk management and modeling use cases across various types of risks.
- Focused learning journey to cover the AI & ML based models & analytics
products for risk management sub-domains. - Insightful coverage of quantitative risk modelling use of AI & ML techniques
& algorithms within BFSI & Fintech space. - Extensive coverage of the AI & ML adoption considerations, challenges &
cautions – regulatory, policy, legal, compliance and ethical. - Practical deep-dive into AI & ML driven risk models, methodology, &
mechanics across supervised, semi-supervised & unsupervised learning
regimes. Coverage of best quants modelling practices and research topics in
risk management domain. - Designed to deliver know-how on BFSI risk management use-cases &
applications across portfolio/credit/market/operational risk analytics. - Wider span coverage of financial use cases encompassing risk prediction,
forecasting, anomaly detection, sentiment analysis & others. - Rigorous live online 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 risk management
application ecosystem – application architectural design & technology stack. - BFSI industry mentor-led AI for Risk Management capstone projects and
implementation white paper writing.
CPAIRM Course Outline
Module 1
Imparts domain-specific know-how on relevant application use cases
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Portfolio Risk
- Portfolio Segmentation & Diversification
- Portfolio Optimization
- Portfolio Risk Decomposition & Attribution
- Portfolio Stress Testing & Scenario Analysis
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Market Risk
- Asset Class Price Forecasting
- Asset Class Volatility Forecasting
- Loss or Value at Risk (VaR) Forecasting
- Market Sentiment Indicators
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Credit Risk
- Default Risk Prediction – Probability of Default
- Recovery Risk Prediction – Loss Given Default
- Exposure Risk Prediction – Exposure At Default
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Fraud Risk
- Anomaly Detection & Outlier Diagnostics
- Fraud Analytics
- Forensic Data Science
Module 2
Imparts practical application of Statistical & 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
- Regression, Classification & Clustering
- Statistical Application Use Cases
- AI & ML Application Use Cases
- Model Evaluation Metrics
- Statistical versus AI & ML Performance Comparative Analysis & Evaluation
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 Credit Risk Prediction
- AI & ML Driven Market Risk Forecasting
- AI & ML Driven Fraud Risk Anomaly Detection
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AI & ML Regulatory Strategy
- Regulatory Ask & Expectations
- Regulatory Acceptance Criteria
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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 Technology Strategy
- AI & ML Infrastructure & Architecture
- AI & ML Front-To-Back Tech Stack
- AI & ML Automated Model Pipeline
CPAIRM Prerequisites – Need prior AI & ML technique know-how
and intermediate-level programming proficiency in Python.
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Why Choose CPAIRM Program?
Faculty
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
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
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
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.
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
Admission Process in AI Risk Management Course
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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 financial risk management?
AI & ML techniques are extensively employed in portfolio risk, credit risk,
market risk & operational risk for prediction, forecasting, classification,
segmentation, attribution, optimization problems in the broader sphere of risk
modelling & analytics. -
What are the desired skill sets & core competencies to be a AI & ML expert in
risk management domain?
AI & ML risk applications requires skill building in key
learning areas like risk data mining & augmentation, risk data analytics &
visualization, risk model building, 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 relationships, solving high-dimensional problems & many more. -
What are specific application use cases of AI & ML models for risk management
domain in the BFSI & Fintech financial sector?
AI & ML has several established & emerging use cases for risk
management function in banks, financial institutions, funds, trading outfits,
Fintect firms:- Risk Analytics -> Visualization & Reporting
- Portfolio Risk -> Portfolio Segmentation, Allocation, Risk Decomposition
& Attribution, Scenario Analysis & Stress Testing - Credit Risk -> Prediction of Probability of Default (PD), Loss Given
Default (LGD), Exposure At Default EAD, IFRS9 ECL Provisions - Market Risk -> Forecasting of Stock Price or Index, Forex Rates
,Volatility Loss or Value at Risk (VaR) and Market Sentiment Analytics - Fraud Risk -> Anomaly Detection, Outlier Diagnostics, Fraud Analytics &
Forensic Data Science
-
What is the future outlook of AI adoption for risk management in the BFSI & Fintech
financial domain?
According to Allied Market Research, the market valuation for AI in
banking stands at $160 billion in 2024 and is anticipated to reach $300 billion by 2030. -
What kind of domain expertise is catered by CPAIRM certification?
CPAIRM is a specialized certification covering AI & ML algorithms and
their applications in risk management sub-domains like Portfolio Risk, Credit Risk,
Market Risk, Fraud Risk, & others. This specialized application oriented course is
designed to cover the essentials on risk modelling and AI & ML use cases for various
risk types and tribes. -
Can you exemplify any AI & ML driven application use case for risk management?
Below briefly highlights an AI & ML use case for stock market
prediction:- • Neural Network (NN) based models can be employed for more accurate market
risk
prediction tasks due to their basic property – nonlinearity. - • Neural Network (NN) universal approximation property allows to examine a
non-linear association between the conditional quantiles of a dependent variable
& predictors. - • NN-based forecasting model can be used in order to indicate possible
downside or
upside moves in the market, form appropriate market timing strategies and assess
the evaluation of risk measures like volatility risk, VaR or loss risk etc.
- • Neural Network (NN) based models can be employed for more accurate market
-
What kind of AI specific career opportunities & avenues available in the broader risk
management area within BFSI & Fintech space?
The BFSI, Fintech & Financial Product/services/consulting firms offer a
variety of career avenues and roles like risk quants modeler, risk AI model validator,
risk products AI researcher, risk or forensic data scientist, risk analytics expert,
risk platforms engineer, risk AI systems developer, risk AI product owner & project
manager. -
Are there any prerequisites required for CPGAIF certification?
CPAIRM requires prior AI & ML technique know-how and intermediate-level
programming proficiency in Python.