This document discusses Capital One's use of AKKA frameworks to implement a parallelized auto loan decisioning workflow called Project IDEAL. It describes how AKKA allows defining actor-based services and message flows to pull credit data, run thousands of loan offers in parallel, and implement conditional decisioning logic. It also provides an overview of Capital One and discusses best practices for building scalable actor-based workflows.

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

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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

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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).

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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)

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Actor Workflow Supervisor Pattern

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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

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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

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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