QU Summer school 2020 speaker Series - Session 7
A conversation with Quants, Thinkers and Innovators all challenged to innovate in turbulent times!
Join QuantUniversity for a complimentary summer speaker series where you will hear from Quants, innovators, startups and Fintech experts on various topics in Quant Investing, Machine Learning, Optimization, Fintech, AI etc.
Managing Machine Learning Models in the Financial Industry
Lecture 1: Model Risk Management for AI and Machine Learning
Artificial intelligence and machine learning are part of today’s modeler’s toolbox for building challenger models and new innovative models that address business needs. However, AI presents new and unique challenges for risk management, particularly for assessing, controlling, and managing model risk for models of limited transparency. Another key consideration is the speed at which these models can be developed, validated, and then deployed into productive use to be competitive adhering to a robust model risk management program. This talk will highlight best practices for integrating AI into model risk practices and showcase examples across the model lifecycle.
5CL-ADBA,5cladba, Chinese supplier, safety is guaranteed
Quant university MRM and machine learning
1. 1
Managing AI / Machine Learning Models in Financial
Services
Presentation for Quant University Summer School
Stuart Kozola
August 26, 2020
2. 2
Outline
Challenges in AI {model} Validation
From Machine Learning to AI
Criteria for Trusted AI {model} Validation
AI in Model Risk Management Lifecycle
3. 3
From Machine Learning
to
Artificial Intelligence
Rise of the Internet→ Big Data
Source: http://www.aboutdm.com/2013/04/history-of-machine-learning.html
History of Deep Learning: https://arxiv.org/pdf/1702.07800.pdf)
Deep Neural
Networks
4. 4
Artificial Intelligence
Machine Learning
AI, Machine Learning and Deep Learning
Timeline
1950s Today1980s
ApplicationBreadth
Bioinformatics
RecommenderSystems
Spam Detection
Fraud Detection
WeatherForecasting
Algorithmic Trading
SentimentAnalysis
Medical Diagnosis
Health Monitoring
ComputerBoard Games
Machine Translation
Knowledge Representation
Perception
Reasoning
Interactive Programs
Expert Systems
Deep Learning
Automated Driving
SpeechRecognition
RoboticsObjectRecognition
Trading
Reinforcement Learning
5. 5
Enabling Technology: Deep Learning and GPUs
Source: ILSVRC Top-5 Error on ImageNet
Annual Image Recognition Challenge
11. 11
FEATURE
EXTRACTION
What is Machine Learning?
Machine learning uses data and
produces a model to perform a task
Cat
Dog
Bird
Car
Car
Dog
Cat
Bird
…
MODEL
12. 12
Cat
Dog
Bird
Car
Learned Features + Model
…
Car
Dog
Cat
Bird
What is Deep Learning ?
Deep learning is a type of machine learning
that learns tasks directly from data
13. 13
Comparison of modeling approaches
Learned Features + Model
…
Car
Dog
Cat
Bird
FEATURE
EXTRACTION
Car
Dog
Cat
Bird
…
MODEL
Deep Learning
Machine Learning
14. 14
Reinforcement Learning
By representing policies using deep neural networks, we can solve problems
for complex, non-linear systems (continuous or discrete) by directly using
data that traditional approaches cannot use easily
Reinforcement Learning Deep Reinforcement Learning
17. 17
Risk Management’s role is more important than ever
Source: The State of AI in Risk Management, Chartis Research 2019
Adoption of AI models in retail banking
Source: The value in digitally transforming credit riskmanagement, McKinsey & Company 2016
18. 18
And the new reality is more …
Models
Complex Models
Coupled Processes
Disruption
Climate
changeE S G
Covid-19
Normal
Fraudulent
Source: Interpreting machine learning models, Tow ards Data Science: Medium 2019
19. 19
But the current environment is … complicated
Source: https://xkcd.com/1987/Source: HSBC MATLAB Expo Presentation
20. 20
Criteria for
Trusted AI {model} Validation
Trusted
AI
Safety
Trusted
Fairness
Robustness
Interpretable
Explainable
21. 21
Has the AI modelor system beendevelopedwith
safety as a key component?
Does the AI modelor system produce consistent
and reliable results?
Questions to answer during AI Validation
Can you explain the workings of an AI model
or system in human understandable terms?
Can you observerand trace cause and effectin
AI modelor system and explain the rationale of
the decision?
Does the AI modelor system make decisions or
recommendations that are fair and free of bias?
Is the AI modelor system immune from
spoofing or other commonattacks?
Does it provide privacy protectionand
is it secure?
Trusted
AI
Safety
Trusted
Fairness
Robustness
Interpretable
Explainable
22. 22
Explainability vs Interpretability
Explainability = “why is this happening?”
e.g., this is a picture of a “Schnauzer” because of
the eyebrows and moustache.
Intrepretability = discern the mechanics
without necessarily knowing why
e.g., if it’s more that 434 days since 2011 and the
temperature is more that 11.6 degrees, then 7000
bicycles will be rented
https://christophm.github.io/interpretable-ml-book/tree.htmlVisualization discoverypresentation
24. 24
Principles of fairness require being able to explain why the model
is making decisions
2. Use of personal attributesas input factors for AIDA-driven
decisionsis justified.
13. Data subjects are provided,upon request, clear
explanations on what data is used to make AIDA-driven
decisionsabout the data subject and how the dataaffects the
decision.
4. AIDA-drivendecisions are regularly reviewed so that models
behave as designed and intended.
“AIDA” refers to artificial intelligence or data analytics, which aredefined as technologies that assistor
replace human decision-making.
https://www.mas.gov.sg/~/media/MAS/News%20and%20Publications/Monographs%20and%20Inf
ormation%20Papers/FEAT%20Principles%20Final.pdf
8. Firms using AIDA are accountable for both internally
developedand externally sourced AIDA models.
27. 27
XNN - Separation of “model” and “explanation”
This image from https://www.darpa.mil/attachments/XAIProgramUpdate.pdfand the
same idea expressed in https://www.oreilly.com/learning/introduction-to-local-interpretable-model-agnostic-explanations-lime
28. 28
Visual Interpretation of Features Across Layers
https://blogs.mathworks.com/deep-learning/2019/01/18/neural-network-feature-visualization/
32. 32
- Explore, develop, back-test, and
document models and methodologies
- Improve transparency and reproducibility
of model development process
- Create reusable model templates
- Auto-generate model documentation
Model Development Environment (MDE)
Achieve reduced time and costto marketand compliance for yourtraditionalrisk and AI models.Perform end-to-endmodellingin line with
globalregulatory guidance (SR11-7,EU TRIM,OSFIE-23, UK SS 3/18, etc.) and industry bestpractices
- Perform independent model reviews
- Perform interactive what-if and
sensitivity analysis on model
parameters
- Comment and flag various aspects for
response and resolution
Model Review Environment (MRE)
MDE
MRE
MTVE
MIR
MEE
MMD
- Configure performance thresholds and
alerts for breaches and generate reports
- Summarize model execution results
using a customizable web dashboard
- Analyze the model usage to determine
candidate models for retirement
Model Monitoring Dashboard (MMD)
- Deploy models in production
environment without recoding
- Integrate with existing technology
infrastructures
- Host production models and scale to
end users in a secure controlled
environment “on-prem” or “cloud”
Model Execution Environment (MEE)
Centralized access to models, lineage, audit trail, risk scoring, and
model risk reporting
Model Inventory & Repository (MIR)
- Automatically run unit tests and generate test reports
- Perform preproduction testing and validation for approved models
- Compare tests of preproduction model with a production model
Model Test & Validation Environment (MTVE)
MATLAB Model Risk Management (MRM) Solution
33. 33
Managing Model Complexity for Deep Networks
Library of pre-trained models
(enables transfer learning) Design of new or customize existing networks
38. 38
Fully Integrated and Automatable Workflow
Files
Databases
Market Data
Access and Explore Data
1
PreprocessData
Working with
Messy Data
Data Reduction/
Transformation
Feature
Extraction
2
Develop& Validate
Predictive Models
Model Creation e.g.
Machine Learning
Model
Validation
Parameter
Optimization
3
Deploy into
Production
Desktop Apps
Enterprise Scale
Systems
Enterprise
Systems
4
Real-time
Reporting
3rd party
dashboards
Web apps
5
39. 39
Cloud
Model Development CI / CD
MATLABPlatform
Domain specific toolboxes
Azure DevOps
MATLAB Parallel Server
Training, simulation, optimization
MATLAB Web
App Server
Risk
Deep
Learning
Web App
Dashboards
Sharing, deployment, integration
Data exploration, preprocessing, model development
Model Inventory
Model Review
Model Validation and Testing
Reporting
Monitoring
Development Review Validate Execute Monitor
MATLAB Production Server
MATLAB
Online
Apps/APIs
BigData
DataSources
Data
stores
Streaming
Data feeds
Files
Alternative
Data
MATLAB ModelRisk ManagementSuite has built-in APIs to seamlessly interoperatewith a wide rangeof open source and third party
technologyplatformsacrossthe modeling life-cycle
MATLAB is interoperable with wide range of technology platforms
MDE
MRE
MIR
MEE
MMD
MTVE
40. 40
Learn more about MathWorks, our products, and our services at
mathworks.com and on social media: