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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

Email

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

jim.bander@Experian.com

623-252-3278