1) The document summarizes a presentation on considerations for using machine learning to expand access to credit in a fair and transparent manner. 2) It discusses how machine learning can be used across various functions at Discover Financial Services like underwriting, customer servicing, and collections. 3) The presentation addresses challenges of interpreting complex machine learning models, ensuring fairness, and mitigating bias in models.

1. Qu Speaker Series
Machine Learning: Considerations for Fairly and
Transparently Expanding Access to Credit
Dr. Raghu Kulkarni
Dr. Steven S. Dickerson
Discover Financial Services
2021 Copyright QuantUniversity LLC.
Hosted By:
Sri Krishnamurthy, CFA, CAP
sri@quantuniversity.com
www.quantuniversity.com
03/03/2021
Qu.Academy
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12. Machine Learning in Financial Sector :
An Overview
Dr. Steve Dickerson
Dr. Raghu Kulkarni

13. Technology
Analytics
Business
The enterprise needs to work as one to deliver value

14. Authentication
Underwriting
Customer
Servicing
Line
Management
Collections
Marketing/
Targeting
Artificial Intelligence &
Machine Learning
Use of Behavioral Models Across DFS

15. Risk Management
§SR 11-7: Supervisory Guidance on Model Risk Management (MRM) from
Fed
§Defines what constitutes a model
§Risks identified include (1) fundamental design errors (2) inappropriate
use and/or assumptions
§Stresses on robust model development process, implementation and
use; validation process; governance
§Discover involved in cutting-edge work to further enhance management
of ML models
§Interpretability using grouping and hybrid of approaches
§Bias quantification and mitigation framework

16. Types of Models - Before and After ML
Modeling (why) has been around
for a while
The how has changed: larger,
more complex, more widespread
models, requiring more
functionality, capacity
Requires framework around ML:
infrastructure, workflow tooling,
model risk management
Logistic Regression
Linear Regression
Decision Trees
Random Forest
Gradient Boosting Machine
CatBoost
LightGBM
Infrastructure Risk Management
Workflow Tools

17. Definition and Evolution of ML in DFS
Advances in processing power
allow the use of more
sophisticated ML algorithms to
find patterns in larger, more
exhaustive datasets.
Supervised ML: Each record in
training data is a pair consisting of
an input object and a desired output
value i.e. the target to be predicted
by the model.

18. Benefits Challenges
ML Presents Promising Benefits and Unique Challenges
§ Ability to account for non-linear
interactions and higher computing
power
§ Imposes fewer assumptions on
distribution of training data
§ Efficiently uses a far greater number
of input features, enabling superior
predictive accuracy
§ Optimizes fairness without
sacrificing too much accuracy
§ Complexity in interpretation
§ Complexity in ensuring fairness

19. Papers
§ Industry Paper
§ Bias Detection in ML Paper

20. Important Components of Interpretability
§ Interpretability: “The ability to explain or to present in understandable terms to a human”;
Key to issuing accurate adverse action reason codes (AARC) in credit decisions.
§ Select approaches to ML interpretability: Shapley Values (Shap), Local Interpretable
Model-Agnostic Explanations (LIME), Generalised Additive Models (GAM), partial
dependence plots (PDP), accumulated local effects (ALE) and explainable neural networks
(xNN)
§ Global vs Local: Both scopes can help provide valuable explanations of ML models
§ “Data locality”: subset of columns and rows of a dataset over which aggregate statistics
are calculated to generate an explanation
§ “Value locality”: the contribution of one input predictor for an individual ML-based
decision depends on the values of all input predictors for that individual

21. §Fair Lending issues can be tested on outcomes using
measures such as marginal effect (ME), adverse impact ratio
(AIR), and standardized mean difference (SMD), which are
variants of statistical parity between protected classes
§There is often a trade-off when making a model fairer by one
measure, while making it appear less fair by another
§Recently proposed mitigation techniques include preprocessing,
in-processing, and post-processing methods
§DFS is developing a proprietary approach to further enhance
ability to reduce model bias at the model development stage,
which is related to the statistical parity approaches at an
outcome level
Fair Lending Considerations

22. Our Vision for future
Enabling and adopting emerging
capabilities with an eye on value
Data
Type
Model
Structure
Tabular Sequence Network (graph)
Deep Learning
(Neural Network)
Network (graph)
Model
Existing Models
Deep Learning
Machine Learning
IC: Mathworks, RT Insights

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24. Thank you!
Sri Krishnamurthy, CFA, CAP
Founder and CEO
QuantUniversity LLC.
srikrishnamurthy
www.QuantUniversity.com
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