This document is a slide presentation by Sri Krishnamurthy on machine learning applications in credit risk. The presentation discusses using machine learning algorithms like supervised learning algorithms for prediction and classification, and unsupervised learning algorithms like clustering, to analyze credit risk data. It provides examples of how clustering algorithms like K-means and hierarchical clustering can be used to group credit risk applicants. The presentation also discusses challenges of adopting open-source software in enterprises and potential use cases for a regulatory sandbox for testing financial technology solutions.

1. Location:
ARPM Open Source Conference
8/13/2017
Machine Learning applications in Credit Risk
2017 Copyright QuantUniversity LLC.
Presented By:
Sri Krishnamurthy, CFA, CAP
sri@quantuniversity.com
www.analyticscertificate.com

2. 2
Slides will be available at:
www.analyticscertificatecom/MachineLearning

3. • Founder of QuantUniversity LLC. and
www.analyticscertificate.com
• Advisory and Consultancy for Financial Analytics
• Prior Experience at MathWorks, Citigroup and
Endeca and 25+ financial services and energy
customers.
• Regular Columnist for the Wilmott Magazine
• Author of forthcoming book
“Financial Modeling: A case study approach”
published by Wiley
• Charted Financial Analyst and Certified Analytics
Professional
• Teaches Analytics in the Babson College MBA
program and at Northeastern University, Boston
Sri Krishnamurthy
Founder and CEO
3

4. 4
Quantitative Analytics and Big Data Analytics Onboarding
• Data Science, Quant Finance and
Machine Learning Advisory
• Trained more than 1000 students in
Quantitative methods, Data Science
and Big Data Technologies using
MATLAB, Python and R
• Launching
▫ Analytics Certificate Program
 Spring 2018
▫ Fintech Certification program
 Fall 2017
• Building

5. 6

6. Credit risk in consumer credit
Credit-scoring models and techniques assess the risk in lending to
customers.
Typical decisions:
• Grant credit/not to new applicants
• Increasing/Decreasing spending limits
• Increasing/Decreasing lending rates
• What new products can be given to existing applicants ?

7. Credit assessment in consumer credit
History:
• Gut feel
• Social network
• Communities and influence
Traditional:
• Scoring mechanisms through credit bureaus
• Bank assessments through business rules
Newer approaches (FINTECH):
• Peer-to-Peer lending
• Lending club, Prosper Market place

8. 9

9. 10
Types of algorithms
Machine
learning
Supervised
Learning
Prediction
Classification
Unsupervised
Learning
Clustering

10. 11
Used to derive a relationship between dependent and independent
variables
• Prediction
▫ Regression
▫ Decision Trees (CART)
▫ Neural Networks
• Classification
▫ Logistic Regression
▫ CART, Random Forest, SVM
▫ Neural Networks
Supervised Learning

11. 12
Data pre-
processing
Split data into
Training and
Testing sets
Train the model
on Training data
Test the model
using Testing data
to evaluate model
performance
Methodology

12. 13
• No distinction between independent variables and dependent
variables
• No result labels to determine “correct” results
• Goals:
▫ Data Reduction
▫ Clustering
Unsupervised Learning

13. 14
• Partitioning Clustering
▫ Starts with K –number of clusters sought
▫ Observations randomly divided to form cohesive clusters
▫ Example : K-means
• Hierarchical Agglomerative Clustering
▫ Each observation is its own cluster
▫ Combine clusters two at a time to finally have one cluster
▫ Example: Hierarchical clustering using single linkage, Ward’s method
etc.
Types of Clustering

14. 15
• Tries to separate samples into K groups with a goal of maximizing
between group variance and minimizing within group variance
• Requires K to be specified up front.
• Starts with K initial centroids and optimizes to minimize the criterion
or till the number of specified iterations are reached.
• Suited for larger datasets
K-means

15. 16
• Goal is to derive a dendrogram starting from each record being its
own cluster
• Works well for smaller data sets
• Proximity is measured in multiple ways (more later)
Hierarchical clustering

16. 17
How do you measure similarity between two entities ?
▫ Apples and Bananas
▫ Coke and Pepsi vs Orange juice
▫ Honda Civic vs Toyota Corolla
▫ New York and Boston
• The notion of distance
The notion of distance

17. 18
• Euclidean distance
• Cosine distance
Distance measures

18. 19
• Manhattan distance
(Taxi-cab distance)
• Jaccard distance
▫ Used to measure similarity or dissimilarity between binary and non-
binary variables
▫ http://people.revoledu.com/kardi/tutorial/Similarity/Jaccard.html
Other distance measures

19. 20
• Gower distance is used for calculating distances when we have mixed types
of variables (continuous and categorical)
• Variables can be:
▫ Quantitative (such as rating scale)
▫ Binary (such as present/absent)
▫ Nominal (such as worker/teacher/clerk)
• The metrics used for each data type are described below:
▫ Quantitative: range-normalized Manhattan distance
▫ Ordinal: variable is first ranked, then Manhattan distance is used with a special
adjustment for ties
▫ Nominal: variables of k categories are first converted into k binary columns and
then the Dice coefficient is used
(https://en.wikipedia.org/wiki/S%C3%B8rensen%E2%80%93Dice_coefficient )
Working with mixed-data

20. 21
• Daisy : Compute all the pairwise dissimilarities (distances) between
observations in the data set
• Pam: Partitioning (clustering) of the data into k clusters “around
medoids”, a more robust version of K-means.
• Agnes: Computes agglomerative nesting (hierarchical clustering) of
the dataset.
Support in R

21. 22

22. 23
Lending club

23. 24
The Data
https://www.lendingclub.com/info/download-data.action

24. 25
The Data
https://www.kaggle.com/wendykan/lending-club-loan-data

25. Variable description

26. • Calculate dissimilarity between observations.
• Select algorithm to group observations together
• Choose the best number of clusters
• Visualize clusters on reduced dimensions
Objective

27. • Partitioning around medoids (PAM) is used in this case.
• PAM is an iterative clustering procedure with the following steps:
▫ Step 1: Choose k random entities to become the medoids.
▫ Step 2: Assign every entity to its closest medoid (using the distance
matrix we have calculated).
▫ Step 3: For each cluster, identify the observation that would yield the
lowest average distance if it were to be re-assigned as the medoid. If
so, make this observation the new medoid.
▫ Step 4: If at least one medoid has changes, return to step 2.
Otherwise, end the algorithm.
Selecting number of clusters

28. • One way to visualize many variables in a lower dimensional space is
with t-distributed stochastic neighborhood embedding (t-SNE)
• This method is a dimension reduction technique that tries to
preserve local structure so as to make clusters visible in a 2D or 3D
visualization.
• https://en.wikipedia.org/wiki/T-
distributed_stochastic_neighbor_embedding
Visualization with reduced dimension

29. 30

30. 31
Alternative Credit scoring in the news

31. 32
Fintech being noticed by Regulators

32. 33
• The regulatory sandbox allows businesses to test innovative
products, services, business models and delivery mechanisms in the
real market, with real consumers.
• The sandbox is a supervised space, open to both authorized and
unauthorized firms, that provides firms with:
▫ reduced time-to-market at potentially lower cost
▫ appropriate consumer protection safeguards built in to new products and
services
▫ better access to finance
• https://www.fca.org.uk/firms/regulatory-sandbox
Regulatory Sandboxes

33. 34
US Regulators catching up

34. Model Validation
• “Model risk is the potential for adverse consequences from
decisions based on incorrect or misused model outputs and
reports. “ [1]
• “Model validation is the set of processes and activities
intended to verify that models are performing as expected,
in line with their design objectives and business uses. ” [1]
• Ref:
• [1] . Supervisory Letter SR 11-7 on guidance on Model Risk

35. 36
Popularity of Open-source software in the enterprise
increasing

36. 37
• Financial Services customers like Capital One, FINRA, and Pacific Life
are moving critical workloads to AWS
Cloud maturing

37. 38
• Versions and packages
Challenges in adopting Open-source software in the
enterprise

38. 39
• Difficulty in replicating and reconciling differences in environments
Challenges in adopting Open-source software in the
enterprise

39. 40
• Deploying models built by Data Scientists still a problem
Challenges in adopting Open-source software in the
enterprise
Data Scientists Enterprise IT

40. 41
• The try-before-adopt model is difficult with unproven open-source
solutions
Challenges in adopting Open-source software in the
enterprise

41. 42
www.QuSandbox.com

42. 43
Quant/Enterprise use cases
• Create an environment that can support multiple platforms and
programming languages
• Enable remote running of applications
• Ability to try out a Github submission/ someone else’s code
• Facilitate creation of Docker images to create replicable containers
• Create prototyping environments for Data Science/Quant teams
• Enable Data scientists/Quants to deploy their solutions
• Enable running multiple experiments concurrently
• Integrate seamlessly with the cloud to scale up computations
Use cases

43. 44
Fintech use cases
• To demonstrate solutions to enterprises
• Create customized enterprise trials for companies that don’t permit
installation of vendor software prior to procurement
• To manage quick updates
• Enable effective integration and hosting of services (REST APIs)
Use cases

44. 45
Academic use cases
• Enable creation of course material and exercises that could be
shared
• Enable students and workshop participants to focus on the data
science experiments rather than environment setting
Use cases

45. 46
Creating replicable environments
Creating and manage replicable environments (Code + software + data) in a single portal

46. 47
Creating replicable environments
Create replicable environments (Code + software + data) through a easy point & click tool and
publish to Dockerhub or manage internally
Share it with target users

47. 48
User portal
• Run multiple experiments in pre-created environments (Code + software + data)
• Deploy your own solutions
• Run any Docker image or Github submission on the cloud

48. 49
Run Jupyter notebooks and prototype applications

49. 50
Run Rstudio and Shiny applications

50. 51
Run any Docker application

51. 52
Manage tasks and errors

52. 53
User portal
• Dockerize and deploy applications on AWS in just a few steps

53. 54
Deploy applications with ease

54. 55
QU’s open source project – Project Mozaic

55. 56
www.QuSandbox.com

56. 57
www.analyticscertificatecom/MachineLearning

57. Thank you ARPM and enjoy the boot camp!
Checkout our programs at:
www.analyticscertificate.com/fintech
www.qusandbox.com
Sri Krishnamurthy, CFA, CAP
Founder and CEO
QuantUniversity LLC.
srikrishnamurthy
www.QuantUniversity.com
Information, data and drawings embodied in this presentation are strictly a property of QuantUniversity LLC. and shall not be
distributed or used in any other publication without the prior written consent of QuantUniversity LLC.
58