Loan Decisioning Transformation

1. © 2016. Capital One Bank - All Rights Reserved.
Loan Decisioning Transformation
Project IDEAL
(Intelligent Decisioning Engine for Auto Lending)
Fred Crable, Director, Software Engineering
Fredrick.Crable@CapitalOne.com

2. 2© 2016. Capital One Bank - All Rights Reserved.
About Capital One
• A leading diversified bank
– One of the largest Credit Card companies
– Top 10 bank in US
– Nation’s largest digital bank
– 3rd largest independent auto loan provider in US
– Largest consumer and commercial banking institution
headquartered in the Washington, DC region
• Major operations in 15 US cities, Canada,
UK
• More than 70 million customer accounts
• Business & Leadership
– Ranked #127 on the Fortune 500
– Ranked #128 on Forbes
“Most Reputable Companies” list
– Listed on The Financial Times “Global 500” (#278)
– Ranked Training magazine's “Top 125” companies for
employer-sponsored workforce training and
development
2

3. 3Confidential
Loan Decisioning

4. 4Confidential
Pull Credit
• Equifax
• Transunion
• Experian
Lexis Nexis
*Analytic
Models
• Customer
FICO, Debt
• Collateral
Loan to Value
• Back/Front-
End
• Pricing Tiers
*Policy Engine
• Max $
Financed
• Tiers
• Limits
• Fees
• Exceptions
Pick Best
• Cash Down
• Sales Price
• Warranty
• APR
IDEAL - Fast Data Use Case
*Run 1000’s in Parallel and pick the best

5. 5© 2016. Capital One Bank - All Rights Reserved.
Big Data Use Case 1 – Analyze and Adjust
• Customer
• CollateralApplication
• Credit Report
• Driver
Variables
Credit
• In/Out of
Policy
• Offers
• Scores
Model /
Policy
• Accept / Reject
• APR, Finance
Amount...
Outcome
• Adjust
• AvoidAnalyze
Refit/Test

6. 6© 2016. Capital One Bank - All Rights Reserved.
Big Data Use Case #2 – Inventory Search & Scoring
•Inventory
•Book ValueSearch
•Model Scoring
•Policy Checks
Model /
Policy
•In/Out of
Policy
•APR, Finance
Amount...
Outcome

7. 7© 2016. Capital One Bank - All Rights Reserved.
Our Implementation over AKKA
• A system that loads a set of services and uses these services in order specified
– Operations can run in parallel or in sequence ( if they have dependencies )
• The sequence of operations and order of operations are configuration driven
• The initial operation is evaluated and the result set tested to continue
• System takes input from Queue or API and kicks-off the sequence
• Operation chains can be expressed as data condition tests or model/mathematical
operations (expressed as configuration yaml or json)

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Open Source Scoring Platform with Basic Decisioning Workflow
• Runs 10s of thousands of models per second on
a single server (4 cores).
• This will scale out with more cores or servers. As
they are added this number increases.
• We can add additional actor systems for other
asynchronous activities (e.g. Bureaus or external
data sources)

9. 9© 2016. Capital One Bank - All Rights Reserved.
AKKA basics
• AKKA helps implement parallel computing easier. Through Actor model.
• AKKA helps to implement distributed parallel computing easier.
• Develop code as actor and message handling – (Loosely a java thread).

10. 10Confidential
Using an Actor (Java)
public class Calculator implements Receiver {
LoggingAdapter log = Logging.getLogger(TypedActor.context().system(), this);
public void onReceive(Object msg, ActorRef actor) {
log.info("Received Message Q> {}", msg);
}
}
//Get access to the ActorRef
ActorRef calActor =
TypedActor.get(_system).getActorRefFor(calculator);
//pass message
calActor.tell("Hi'there");

11. 11© 2016. Capital One Bank - All Rights Reserved.
Actor Flow Development – JSON Defined Workflow
API
Initiates / Blocks
Parallel
Operation
Group 1 Element 1
Parallel
Operation
Group 1 Element 2
Parallel
Operation
Group 1 Element 3
Serial
Operation
Group 2 Element 1
Serial
Operation
Group 2 Element 2
Message
Handler
Supervisor
{
"actorSystemName": ”SimpleSystem",
"context": {
"type": "Define",
"supervision": "Escalate",
"router": "RandomPool",
"lowerCount": 1,
"upperCount": 1,
"childrenMode": "Sequential”
},
"messageHandler": [
{
"name": "MessageHandler1",
"desc": "MessageHandler1",
"clazz": ”Class1",
"group": [
{
"name": "g1",
"desc": "g1",
"order": 1,
"context": {
"type": "Inherit",
"childrenMode": ”Parallel"
},
"element": [
{
"id": 1,
"name": "group1-element1",
"desc": ”first actor in sequence",
"order": 1,
"clazz": [
”MessageInput"
],
"bean": ”Class1Bean"
}
interface Class1Bean <MessageInput, MessageOutput>
extends ServiceInterface {
…
public MessageOutput execute(MessageInput M1)

12. 12© 2016. Capital One Bank - All Rights Reserved.
Actor Workflow Supervisor Pattern

13. 13© 2016. Capital One Bank - All Rights Reserved.
Supervisor Actor Patterns

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Actor Workflow Mechanics – Operation and Service Definition
"messageHandler": [
{
"name": "ApplicationScoringService",
"desc": "ApplicationScoringService",
"clazz": "com.capitalone.auto.lo.business.Application",
"context": {
"group": [
{
"name": "ideal",
"desc": "ideal credit pull and decisioning workflow",
"order": 1,
"context": {
"childrenMode": "Sequential",
"carryoverRequest": "true"
},
"element": [
{
"name": "dataService",
"desc": ”get credit data and alternate data",
"clazz": [ "CurrentMessage" ],
"bean": ”DataService"
},
on receive
Handler
Bean
Output
Rules
Define service
Bean and
relative order of
execution

15. 15© 2016. Capital One Bank - All Rights Reserved.
Actor Workflow Mechanics – Conditional Rules
"conditions": [
{
"id": 1,
"name": "Exception",
"reference": "any executionElementId",
"watch": {
"values": ["throws java.lang.Exception”]
}
},
{
"id": 2,
"name": "PrescoreResponse",
"reference": "executionElementId:1.1.1",
"watch": {
"values":
["com.capitalone.auto.lo.response.PreScoreResponse[getAppDecision notEquals A]"]
}
},
on receive
Handler
Bean
Output
Rules
Define service
Bean and
relative order of
execution

16. 16© 2016. Capital One Bank - All Rights Reserved.
Actor Workflow Mechanics – Conditional Flow
"consequences": [
{
"id": 1,
"order": 1,
"name": "Exception",
"action": {
"whenMatched": [ "Exception”],
"thenGoto": "executionElementId:1.3.1"
}
},
{
"id": 2,
"order": 2,
"name": "PreScoreDecline",
"action": {
"whenMatched": [”ModelScoringResponse"],
"thenGoto": "executionElementId:1.2.1"
}
},
on receive
Handler
Bean
Output
Rules
Define service
Bean and
relative order of
execution

17. 17© 2016. Capital One Bank - All Rights Reserved.
Decisioning Workflow

18. 18© 2016. Capital One Bank - All Rights Reserved.
Best Practices
• Plan out your logging strategy with MDC (thread local variable) and use
tools like Splunk
• Identify actors like a person, with state and identity
• Limit granularity of fan-out or scatter-gather operations
• Make sure all actors have the data they need to do their jobs w/o calling
external resources
• Make sure actor operations are idempotent
• Take checkpoints and use AKKA persistence where applicable