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AI will be augmented intelligence for collectorsin the next economic downturn
Jim Bander, PhD
Experian Decision Analytics
Lead
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Introduction: Color outside the linesBut don’t run with scissors
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TCPA, FDCPA, FCRA,…
CFPB Notice of Proposed Rulemaking
Staffing Challenges
Portfolio Quality
Loss Rates
Budgets
Roll Rates and other KPIs
Economics
Dialers
Credit Risk
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1. Evolving the human: augmenting the human intellect
2. 21st century machine learning
3. Preparing for economic cycles
4. Augmenting the human debt collector
5. Coloring outside the lines with machine learning
Contents
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Augmenting the Human IntellectDouglas C. Engelbart
https://www.youtube.com/watch?v=KpcxRzdWF64&start=172&end=510
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21st Century Machine Learning
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Machine Learning and Artificial Intelligence
Machine Learning
Artificial Intelligence
Deep Learning
Subset of machine learning
focused on layers of neural
networks that can be trained to
perform complex tasks
Subset of AI that includes
statistical techniques that
enable machines to improve
tasks with experience or
exposure to data
Techniques that enable
computers to mimic human
intelligence, using logic, if-then
rules, decision trees, and
machine learning
Data Processing and
Task Performance
[Response]
Task Optimization
and Anticipation
[Prediction]
Complex Decisioning
and Adaptation
[Reasoning]
Machine learning provides a great deal of benefit across decisioning areas
but it also has its limitations
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Three Types of Machine Learning
Task
DrivenEnvironment
Driven
Data
Driven
Reinforcement
Learning
Supervised
Learning
Unsupervised
Learning
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Unsupervised Learning
Borgi et al., Frontiers in Psychology 2014, Baby schema in human and animal faces induces cuteness perception and gaze allocation in children
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Supervised Learning
Training Data Set
Cute?
Average?
Cute
Baby
Supervisor
New Photo
Trained Model
Category
Cute Puppy
Average
Baby
Cute
Puppy
Average
Puppy
Cute
Kitten
Average
Kitten
Cute
Adult
Average
Adult
Cute
Dog
Average
Dog
Cute
Cat
Average
Cat
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Fitting It Together
Control Losses
Prevent Fraud
Improve the Customer
Experience
Data Assets
Proprietary dataset
Relational database
Flat files
Hadoop, Cassandra, etc.
Analytical
SoftwareTechniques
Data access
Code Solutions
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Traditional Credit Scoring is Supervised Learning
Training Data Set
Good
payerBad
payer
Good
payer
Bad
payer
indeterminate
New Customer
Scorecard Model
Good payer?
Bad payer?
Supervisor
Credit Score
635
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Supervised Learning Methods
High predictive power
Transparency
Model execution
Development effort
Adverse action
Regulatory acceptance
Model stability
Traditional
Risk Modeling
Gradient Boosting
MachinesNeural Network Random Forest
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Better techniques allow for better
good/bad separation
• Based on Experian tests, @1% false positive rate
• Increase loan volume while controlling risk
• 5% lift in credit score separation
• Improved fraud detection
• 15–20% relative improvement
• Improve experience
• 40% reduction in false positives for fraud
Benefits of the Extreme Gradient Boosting Algorithm
Percent good
Pe
rce
nt
ba
d
Improveexperience
Improvedetection
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Utilities can anticipate the next economic downturnAddress your business objectives with technology
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““Your customers are facing some challenges and
difficult choices. Overall household debt is now
21.4% above the 2013Q2 trough.* It has never
been so important for service providers to
understand your customers’ unique situation, to
be able to treat them fairly, compliantly and with
approaches that work for all parties.
* FEDERAL RESERVE BANK of NEW YORK
2018: Q4 Report on Household Debt and Credit
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2020 Vision
Source: https://www.bloomberg.com/news/articles/2019-02-14/what-indicators-
to-monitor-for-signs-a-u-s-recession-is-coming
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What will your customers experience during the next recession?
Source: https://www.thebalance.com/what-is-a-recession-3306019
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In an environment of increasing debt levels, using machine learning to recognize customer behavior patterns can help you respond promptly.
““
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Augmenting the Human Debt Collector
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Who Is a Great Collector?
Great Customer Service Representative
Great Financial Counselor
Great Salesperson
Great Detective
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Augmented Intelligence With a 360 Degree View of the Customer
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Could an Average Collector Perform Like a Great One?
Average Customer Service
Representative
Average
Salesperson
Average Detective
Augmented Intelligence
Average Financial
Counselor
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Using Machine Learning in the Collections ProcessEnabling You to Make the Right Decisions on Each Case at the Right Time
Wash & Enrich
Data Cleanse
Data Enrichment
Fraud Screen
360o Customer View
Address Validation
Bank Acct Validation
Bureau Call
Mortality Check, etc
Manage &
Monitor
Manage
Assist
Special
Third Party
Manual
Hardship / Restructure
Exception
Legal Process
Agency Allocation
Trace
Auto
Letter
SMS
Web Chat
Self Cure
Pre Delinquent
Outcome
Payment
Promise
Dispute
Complaint
Escalate
Sell
Charge-
off
SellWrite-
offLeave/
Monitor
Decisioning /
Machine LearningCases
Propensity
modeling:
Best ActionDialer
Campaign
Contact
Channel/TimeArrangemen
t Value
Agency
AllocationDebt SaleOptimized
• Self cure
• Pay
• Roll
• Etc.
Drive efficiencies
Improve customer
satisfaction
Reduce provisioning and bad debt
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Augmenting the Human CollectorThe Augmented Customer Service Representative
Customer Service
Representative
360-Degree
customer view
Address validation
Bank account
validation
Regulatory script
Account alertsCustomer Overview
Allyson Matlin Ref ID: P781623
Risk Stage: 1/5
Total Debt: $1,500.00
Total Pursuable: $1,500.00
Reason for Delinquency: Medical
‘Allyson has returned documents but says she is not receiving
our correspondence.
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Augmenting the Human CollectorThe Augmented Financial Counselor
Financial Counselor
Affordability Assessment
Last updated: 2/12/2019
Last validated: 2/12/2019
Jack Josephs Ref ID: R032571
Food: $500
Personal: $100
Citi Credit Card: $460
Monthly Disposable Income: $1140
Reason for Delinquency: Reduced IncomeJack was laid off in January and is now working 3 part-time
jobs.
360-Degree
customer view
Data fusion
Data cleansing and
enrichment
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Augmenting the Human CollectorThe Augmented Salesperson
Salesperson
Settlement Options
Mary AndersonRef ID: Q325712
Payment Plan 1: 50% sustainability. Initial reduction: $1500. Interest over time: $250
Payment Plan 2: 80% sustainability. Initial reduction: $2000. Interest over time: $150
Payment Plan 3: 83% sustainability. Initial reduction: $3500. Interest over time:$100
Mary expects her divorce to be final in December and would like to buy a home.
360-Degree
customer view
Pattern recognition
driving next best
action
Embedded speech
analytics
Propensity models
based on supervised
machine learning
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Augmented the Human CollectorThe Augmented Detective
Customer Overview
Stan Bresloff Ref ID: P781623
Risk Stage: 5/5
Total Debt: $20,440.00
Total Pursuable: $4,400.00
Reason for Delinquency: Reduced Income
Stan is currently facing court charges and may be
sentenced to 5 years in prison.
Detective
360-degree
customer view
Device intelligence
Address validation
Fraud screen
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Supplementing the Human Collector
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Benefits of Augmented Intelligence
Create individualized treatments while reducing manual interactions
How to increase automation but keep the personal touch, to maximize your recoveries
React quickly and effectively to market changes
Easily implement different strategies to remain competitive in the market
Understand your customer to ensure fair treatment
Gaining insight to meet regulations while protecting your brand
Meet the growing expectation for digital consumer self-service
Overcoming legacy system restraints to offer consumers the experience they want
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Collections Beyond North America
HTF Market Intelligence
Global Debt Collection Software Market Share (%), by Region (2017)
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Australian bank provides omni-channel communications based on machine learning
Speech Analytics – Machine Learning uses a Natural Language Processor
as a machine learning engine. Rather than requiring call centres to develop
and set search criteria manually, this tool will suggest search terms based on
an analysis of the combinations of words used by the consumer, as well as
added sentiment.
Blended IVR capabilities allow for simple or sophisticated design to handle
each call using customized voice recordings, menu selection, and routing
options to appropriately skilled agents, or immediate pass-through to
Blended Agents.
True Blended Call Center maximizes agent productivity by
allowing them to operate seamlessly : 1) an inbound calling
queue and 2) an outbound calling list.
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African cellular provider reduces churn with segmentation and a rules-based approach to collections
SolutionIntegrated solution linking collections to customer
management systems allowing for a more
targeted and automated approach to collections
Business ChallengeEffectively manage subscribers in a competitive
market especially ‘Out of Order’ subscribers that
are a threat to revenue growth
About the ClientA Pan-African cellular communications company
Results• Reduced operational costs by 38%
• Reduced write-offs by 53%
• Increased productivity by 180%
• Reduced compulsory churn
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European Telecom operatorIncrease Debt Collections Agency effectiveness with allocation optimization
Business challengeDeciding which DCA from the client’s panel to send
each delinquent customer, to maximise the amount
collected and increase reconnection rates
Experian’s SolutionDevelop an optimized strategy tree to maximize the
objective within the constraints. The tree was
designed to segment the customers finely by
demographic and behavioral groups
Benefits• 10% increase in net customer value
• Earnings for each DCA increased an
average of 9%
• Increased the amount of balance
collected from 20 to 23%
• Higher rate of reconnections
*Anonymous case study based on a real client
About the clientA leading European telecommunications operator,
with over 2 million customers and a range of mobile
services*
Balance Collected
Net Customer
Value
DCA Earnings
10% 9%
15%
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Coloring Outside the LinesWithout Running with Scissors
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Places: A 10 million Image Database for Scene Recognition. B. Zhou, A. Lapedriza, A. Khosla, A. Oliva, and A. Torralba. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2017
Supervised LearningThe MIT Places Database
http://places2.csail.mit.edu/demo.html
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Inpainting With and Without Machine Learning
Ground Truth
(Original Photo)
Masked Photo
(Input)
Standard
Algorithm 1
Standard
Algorithm 2
Standard
Algorithm 3
Ground Truth (Original Photo) Masked Photo (Input) NVIDIA Neural Net
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Inpainting with Deep LearningIizuka, et al., SIGGRAPH 2017
https://www.youtube.com/watch?v=5Ua4NUKowPU
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Ground
Truth
Input
Output
Outpainting with Deep LearningMark Sabini & Gili Rusak, CS 230 (Deep Learning) Stanford University
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Ground Truth Outpainted Five Times
Coloring Outside the Lines – Recursive OutpaintingMark Sabini and Gili Rusak, CS 230 (Deep Learning) Stanford University
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Coloring Outside the LinesWithout Running with Scissors
43
TCPA, FDCPA, FCRA,…
CFPB Notice of Proposed Rulemaking
Staffing Challenges
Portfolio Quality
Loss Rates
Budgets
Roll Rates and other KPIs
Economics
Dialers
Credit Risk
Supervisor
Great Customer Service Representative
Great Financial Counselor
Great Salesperson
Great Detective
©2017 Experian Information Solutions, Inc. All rights reserved. Experian and the Experian marks used herein are trademarks or registered trademarks of Experian Information Solutions, Inc.
Other product and company names mentioned herein are the trademarks of their respective owners. No part of this copyrighted work may be reproduced, modified, or distributed in any form
or manner without the prior written permission of Experian.
Experian Confidential
Jim Bander, PhD
Experian Decision Analytics Lead
623-252-3278
