spark

1. Leveraging Spark ML for
Real-Time Credit Card Approvals
Case study from a large financial Institution
Anand Venugopal
Saurabh Dutta
Impetus – StreamAnalytix
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2. Agenda
• Use case background
• Existing system challenges and new goals
• Solution details and lessons learnt
• Q&A
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3. Background - Customer
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50M+ Credit cards
50+ countries ~4M per year

4. Background – Use Case
• Acquire legitimate, responsible customers
• Decision: Approve ? Credit Limit ? APR ?
• Sub-second response time to make a decision
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5. Problem Statement
Submits
application
Prospective
Customer
Determining Card Eligibility
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Logic
Execution
Decision +
Communication

6. Core logic: Risk scoring
• Estimate debt repayment risk
• To limit credit risk of the lender
• Ensure individual’s financial well being
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7. Risk scoring factors
• History
• Credit usage
• Loans
• Other Credit cards
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• Job type
• Income band
• Debt Ratio
• Credit Scores

8. Decision flow
Receive
Request
Call CB
REST API
Get
Acxiom
Data
Get SOW
Data
CB
Derivations
SOW
Derivations
Decision Line Price
Respond
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Models
Risk Score: 0-1

9. Multiple model types
• Approve/ Decline
– Segment
– Geography
• Line
– Credit Limit
• Price
– APR
• Decision Tree
– Numerous instances
• Regression
• K-means
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10. Decision tree – Approve ? Y/N
1
2 3
4 5 6 7
Salary >= 50,000Salary < 50,000
Other Loans = Y
Other Loans = N
8 9
Debt Ratio < 0.7
Other Loans = Y
Other Loans = N
Debt Ratio > 0.7
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11. Regression: Credit line ($)
$ 1500
$ 500
Creditlimit
Risk Model Score
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12. Clustering model: Price
22% APR
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18% APR

13. Existing system
• Built using traditional technologies
• Microsoft .NET stack
– C#
– MS SQL Server
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14. Top challenges with existing system
• Everything on single box: not scalable, not flexible
• Model training on limited data: limits accuracy
• Data Scientists work in isolation: silo’ed tools
• Model management: manual and cumbersome
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15. Primary goals for the new system
• Ease of use for stakeholders (self-service)
• Scale: Build models on huge datasets
• Fast decision response for the end-customer
• Unified, collaborative platform
• Data Lineage / Audit capability
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16. Proposed tools
• Spark Streaming
• Spark ML
• Kafka
• HDFS
• HBase
• Visual Spark Platform - StreamAnalytix
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17. Spark Streaming
• Write streaming jobs
• Extension of core Spark API
– Scalable
– High throughput
– Fault tolerance
• Receives input and divides into batches
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18. Spark ML
• Spark’s Machine learning
module
– DataFrame-based API
• Algorithms
– Classification
– Regression
– Clustering
– Collaborative Filtering
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• Utilities
– Feature Selection
– Feature Transformations
– Hyper Parameter Tuning
– Model Evaluation
– Linear Algebra, Statistics

19. Spark based architecture - Training
SparkHDFS
Spark ML
Model RepositoryTraining Data Source
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HDFS

20. Model training pipeline
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Read from HDFS

21. Model training pipeline
Data Validation
- Null checks
- Invalid Chars ♡⚐♯♣
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Data Quality

22. Model training pipeline
Score = 200
Status = Approved
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Eliminate Outliers

23. Model training pipeline
Mean
Median
Most Frequent
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Name Age
Jack 37
Eva ?
Dirk 42
Impute missing values
Model based Imputation
Constant

24. Model training pipeline
Feature Selection
Transformation
Model Selection
Hyperparameters
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Core logic of training model

25. Model Evaluation
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26. Spark based architecture - Scoring
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User
Session Kafka Spark Streaming
3rd Party
Providers
Bank’s internal
repository
YARN
ML
Models
HBase
Kafka
HDFS

27. Model scoring pipeline
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Read from Kafka

28. Model scoring pipeline
External WS calls
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29. Model scoring pipeline
Internal DB Lookup
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30. Model scoring pipeline
Conditional Filter and model execution
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31. Model scoring pipeline
Decision based on
model’s score
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32. Model scoring pipeline
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Rejected
Approved
Pending

33. Model scoring pipeline
Line & Price Models
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34. Lineage/ Audit
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The journey of a single data record through the scoring pipeline

35. Deployment
Transport Compute Storage Exploration
Kafka Spark
StreamAnalytix
HDFS + Hive BI Tools
- 2 Nodes with Sticky Session
- Load Balancer
- Zookeeper
- Tomcat
- MySQL
- RabbitMQ
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36. Project Details
• Q4 2017
• 3 months from start to finish
• 3x faster than originally planned
• Team size: 4
• Apache Spark 2.1
• On-premise Hadoop Cluster with YARN
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37. Learnings
• Consistent data format
• Add timeouts to third
party API calls
• Optimize stragglers
• Avoid excessive logging
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• Checkpointing
• Outlier Analysis
– Using models
• Hyperparameter tuning +
Metric Evaluation
• Caching
– useNodeIdCache

38. Goals: Recap
• Ease of use for stakeholders (self-service)
• Scale: Build models on huge datasets
• Fast decision response for the end-customer
• Unified, collaborative platform
• Data Lineage / Audit capability
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39. Q&A
Visit Impetus StreamAnalytix booth #209
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