Artificial Intelligence in Credit Risk Management

Ken Abbott (Moderator),

Managing Director, IHC CRO,

Barclays (Retired)

Didier Blanchard,Managing Director, Head of Enterprise Risk Management

for the Americas,Société Générale

Featured Panelists:

Michael Jacobs Jr,Lead Quantitative

Analytics,PNC

Stephan Meili,Managing Director,

Citi

Hocine Mouas,Managing Director, Head Risk,

Finance & Market Data Technology for Americas,

Société Générale Corporate and Investment Banking

Introduction‣ AI is defined as theory and development of computer systems able to perform tasks traditionally requiring human

intelligence

‣ ML is a key tool in this area

‣ Researchers in CS and statistics have developed advanced techniques to obtain insights from large disparate data sets

‣ Datasets can be huge and may be of different types, from different sources, and of different quality (structured / unstructured)

‣ Techniques leverage computers to perform tasks like recognizing images and processing natural languages, learning from experience -tasks traditionally requiring human sophistication broadly termed AI

‣ AI has existed for many years but recent increases in computing power and availability of data have resulted in resurgence of interest

‣ Already being used to diagnose diseases, translate languages, drive cars; and increasingly in financial sector as well

Organizing Information‣ Book types:

• Fiction/Nonfiction

• Language

• Audience

• Genre

• Cover Type

‣ Sports:

• Gender

• Team / Solo

• Type of Ball

• Times vs goal

• Season

‣ Trading assets:

• Country

• Currency

• Security Category

• Security Type

• Rating

‣ Art:

• Medium

• Scale

• Venue

• Type

• Role

Lang

uage

Athl

etic

Abi

lity

Secu

ritie

sCr

eativ

ity

Example Hierarchy

Fintech

DLT

Blockchain

Cryptocurrency

Bitcoin, etc.

‣ Also payments, regtech, etc.

‣ Also DAC, Hashgraph

‣ Also inventory, positions, etc.

‣ Also Etherium, etc.

What is Fintech?

Albert Wang, Fintech: Overview

https://www.salaamgateway.com/en/story/infographwhat_are_the_different_types_of_fintech-SALAAM16112017120519

What is AI?

FSB, Artificial intelligence and machine learning in financial services

Taxonomy of AI

‣ There are two ways in which AI is generally classified

‣ One type based on classifying AI and AI-enabled machines based on likeness to human mind, and ability to “think” and “feel” like humans

‣ According to this system of classification, there are four types of AI or AI-based systems: reactive machines, limited memory machines, theory of mind, and self-aware AI

‣ Alternate system more generally used in tech parlance is classification of AI into Artificial Narrow Intelligence (ANI), Artificial General Intelligence (AGI), and Artificial Superintelligence (ASI)

https://www.forbes.com/sites/cognitiveworld/2019/06/19/7-types-of-artificial-intelligence/#20427cd5233e

Classifying AI Based on Likeness to Human Mind‣ Reactive Machines

• Oldest forms of AI systems with limited capability - emulate human mind’s ability to respond to different kinds of stimuli• No memory-based functionality - cannot use previously gained experiences to inform present actions• Only used for automatically responding to a limited set or combination of inputs

‣ Limited Memory• Capabilities of purely reactive machines, also capable of learning from historical data to make decisions• Nearly all existing applications come under this category of AI• Present-day AI systems trained by large volumes of training data to form reference model e.g. , image recognition trained using thousands of

pictures and labels to teach it to name objects

‣ Theory of mind• Next level of AI systems researchers currently engaged in innovating; still a “work in progress”• Better understand entities it is interacting with by discerning needs, emotions, beliefs, and thought processes• Requires development in other branches of AI. here, AI has to perceive humans as individuals and -“understanding” them

‣ Self-Aware• Exists hypothetically – akin to human brain decades, if not centuries away, always ultimate objective of AI research• Able to understand and evoke emotions but also have emotions, needs, beliefs, and potentially desires of its own• Can potentially boost our progress as a civilization by leaps and bounds, can also potentially lead to catastrophe

https://www.forbes.com/sites/cognitiveworld/2019/06/19/7-types-of-artificial-intelligence/#20427cd5233e

Classifying AI Based on Technology‣ Artificial Narrow Intelligence

• Represents all existing AI, including even most complicated and capable AI ever been created to date• AI systems that can only perform a specific task autonomously using human-like capabilities• Machines can do no more than what they are programmed to do, have limited range of competencies• These systems correspond roughly to all the reactive and limited memory AI• Even most complex AI that uses ML and deep learning to teach itself falls under ANI

‣ Artificial General Intelligence• Ability of an AI agent to learn, perceive, understand, and function completely like a human being• Independently build multiple competencies and form connections and generalizations across domains, massively cutting down on time needed for

training• This will make AI systems just as capable as humans by replicating our multi-functional capabilities

‣ Artificial Superintelligence• Pinnacle of AI research, as AGI will become most capable forms of intelligence on earth• Exceedingly better at everything we do because of overwhelmingly greater memory, faster processing and analysis, and decision-making

capabilities• Development of AGI and ASI will lead to a scenario most popularly referred to as singularity• While potential seems appealing, these machines may also threaten our existence or our way of life

https://www.forbes.com/sites/cognitiveworld/2019/06/19/7-types-of-artificial-intelligence/#20427cd5233e

What is ML?‣ AI is a broad field, of which ‘machine learning’ is a sub-category

‣ ML may be defined as a method of designing a sequence of actions to solve problems, (algorithms) which optimize automatically through experience with limited/no human intervention

‣ These techniques can be used to find patterns in large amounts of data (big data analytics) from increasingly diverse and innovative sources

‣ Many ML tools build on familiar statistical methods• extending OLS models to deal with potentially millions of inputs

• statistical techniques to summarize large datasets for visualization

FSB, Artificial intelligence and machine learning in financial services

ML Uses‣ Spam filtering

‣ Credit card fraud detection

‣ Digit recognition on checks, zip codes

‣ Detecting faces in images

‣ MRI image analysis

‣ Recommendation system

‣ Search engines

‣ Handwriting recognition

‣ Scene classification

https://p3analytics.blogspot.com

Types of ML‣ Supervised Learning

• Consist of target/outcome variable predicted from given set of predictors • Using variables, generate a function that map inputs to desired outputs• Training process continues until model achieves desired level of accuracy on training data

‣ Unsupervised Learning• No target or outcome variable to predict / estimate• Used for clustering population in different groups, widely used for segmenting customers in different groups for specific intervention• Examples of Unsupervised Learning: Apriori algorithm, K-means

‣ Reinforcement Learning• Machine trained to make specific decisions• Machine exposed to environment where it trains itself continually using trial and error• Machine learns from experience, tries to capture best possible knowledge to make business decisions• Example of Reinforcement Learning: Markov Decision Process

https://www.analyticsvidhya.com/blog/2017/09/common-machine-learning-algorithms/

Vasily Zubarev, Machine Learning for Everyone

ML versus Statistics

‣ Statistics• Hypothesis testing• Experimental design • Anova• Linear regression• Logistic regression• GLM• PCA

‣ Machine Learning• Decision trees• Rule induction• Neural Networks• SVMs• Clustering method • Association rules• Feature selection • Visualization• Graphical models • Genetic algorithm http://statweb.stanford.edu/~jhf/ftp/dm-stat.pdf

List of Common Machine Learning Algorithms

‣ Linear Regression

‣ Logistic Regression

‣ Decision Tree

‣ SVM

‣ Naive Bayes

‣ kNN

‣ K-means

‣ Random Forest

‣ Dimensionality Reduction Algorithms

‣ Gradient Boosting Algorithms• GBM

• XGBoost

• LightGBM

• CatBoost

https://www.analyticsvidhya.com/blog/2017/09/common-machine-learning-algorithms/