Clarity Solution Group presentation at the Chief Data Officer Insurance 2016

Driving Improved Customer Experience via Entity Resolution and Machine LearningResolving Entities with Machine Learning

2

Agenda

Proprietary and Confidential - ©2016 Clarity Solution Group, LLC

Why?What

Value?

Business Context

ML ExamplesCleanse Entity

Resolution HDFSHDFS

Many moving pieces Collaboration with business

user is key

Many moving pieces Collaboration with business

user is key

E.g.:- Consider only features of

interest- Move to upper case- Remove punctuation- Standardization (e.g.,

truncate to 5 characters US zip codes)

- ….- ….

Proprietary and Confidential - ©2014 Clarity Solution Group, Inc.

Entity Resolution

Stad

ardi

zedD

ata G

OGOOGP

HierarchicalClustering

Grouping entries based on their distance

Creating all pairs and computing each distance:- Dist = 0 same- Dist = 1 notthe same

0.90.2……0……0.10

distance

…..

≠~

=

Dist.

Logistic Regression

Trained Threshold

0.9 No0 Yes

0.2 Yes… No

Full

Dat

aset

Dist.

Training the Machine: Visually ID a subset as match or non match

Yes

Nodistance

Subs

et

Determining the best separating line

O G P

Key Consideratio

ns

Business Problem

3

The Business Problem Order from chaos: Common definition of Partners, and Partner's clients drives improved growth, service, innovation

Client Business Partners

Business Partner Clients

Financial services firm with ~ 1,000,000 direct partner

relationships and significant duplication

Classic entity resolution issue

Problem multiplies exponentially with duplication within Partner’s

Clients


4

Resolving duplication “noise” has many positive impacts on customer experience and business measures

Client Business Partners

Business Partner Clients

The Business Opportunity

Improved Service

Decreased repetitive, labor-intensive

activity

Accelerated Client-onboarding

Increased revenueImproved network

visibility driving cross-selling

Improved bottom-line


5

Machine Learning – Some Notorious Examples


6

The Overall Process

Cleanse

Entity Resolutio

n

Defining the end-to-end solution scope is key

!

E.g.:- Remove punctuation- Standardization (e.g.,


- ….

Data Points: - Integration: ~8 weeks- Machine learning dev.: ~6

weeks

Storage

Storage

!


7

The Overall Process

Cleanse

Entity Resolutio

n

Defining the end-to-end solution scope is key

!

E.g.:- Remove punctuation- Standardization (e.g.,


- ….

Data Points:- Integration: ~8 weeks- Machine learning dev.: ~6

weeks

Storage

Storage

!Entity Resolutio

n


8

Stan

dard

ized

Dat

a GOGOOGP


Entity Resolution via Machine Learning Grouping entries based on their distance

Creating all pairs and computing each distance:- Dist. = 0 same- Dist. = 1 not the same

0.90.2……0……0.10

distance

…..

≠~

=

Dist.

Logistic Regression

Trained Threshold

0.9 No0 Yes

0.2 Yes… No

Full

Dat

aset

Dist.


YesNo

distance

Subs

et


O G P


9


Stan

dard

ized

Dat

a GOGOOGP

Trained Threshold

0.9 No0 Yes

0.2 Yes… No

Dist.Su

bset

Entity Resolution: Behind the Scenes

distance

USITUSITCA

USUSITIT

Filter out exact matches

Use common features to parallelize the calculation

CA

Label unique entries without clustering

1.2 million

entries !!!

!

Logistic Regression

distance

YesNo


10

An ExampleName Address Countr

yGroup

SPACCANAPOLI PIZZERIA 123 WEST SUNNYSIDE USA ????

SPACCA PIZZERIA 123 W SUNNYSIDE AVE USA ????

SPACCANAPOLI PIZZERIA 123 WEST SUNNYSIDE IT ????

SPACCA PIZZERIA 123 W SUNNYSIDE AVE IT ????PIZZERIA LIBRETTO 221 OSSINGTON

AVENUECA ????

Out

put

Name Address Country

Group

SPACCANAPOLI PIZZERIA

123 WEST SUNNYSIDE USA USA_1

SPACCA PIZZERIA 123 W SUNNYSIDE AVE USA USA_1SPACCANAPOLI

PIZZERIA123 WEST SUNNYSIDE IT IT_1

SPACCA PIZZERIA 123 W SUNNYSIDE AVE IT IT_1PIZZERIA LIBRETTO 221 OSSINGTON

AVENUECA CA_1

Inpu

t


11

Findings and Data PointsBusiness Value

• Reduction in Effort: ~ 3 FTE’s

• Increased Client Onboarding: ~ 30%

• Individual / anecdotal evidence of increased cross-selling and loyalty

Technical Measures

• Approximately ~500K duplicates identified from ~1.2MM total customer records

• Job parallelism reduced run-time from >> 24 hours to 15 minutes

• Run-time enabled overnight process, with capability to run intra-day if needed


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Key Considerations

Consideration Implication

Machine Learning is not a stand-alone exercise

Outline end to end process with business application integration points

Business Collaboration in “training” process is critical

Ensure heavy degree of subject matter expert involvement

Recognize the importance of technique in the solution

Leverage a data science process: Problem to hypothesis to technique selection

Underlying technology is not “one size fits all”

Machine Learning / Big Data solutions require customization and corresponding

investment in people


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Key Considerations


Questions?

14

Appendix


15

Cle

an D

ata

GOGOOGP

Trained Threshold

0.9 No0 Yes

0.2 Yes… No

Dist.Su

bset

Entity Resolution: Jaccard Distance on Shingles

Jaccard Distance1) ‘CLARITY SOLUTION GROUP’:

['CLA', 'LAR', 'ARI', 'RIT', 'ITY', 'TY ', 'Y S', ' SO', 'SOL', 'OLU', 'LUT', 'UTI', 'TIO', 'ION', 'ON ', 'N G', ' GR', 'GRO', 'ROU', 'OUP']

2) ‘CLARITY SOL GR’:['CLA', 'LAR', 'ARI', 'RIT', 'ITY', 'TY ', 'Y S', ' SO', 'SOL', 'OL ', 'L G', ' GR']

1− ¿¿1+¿2−¿

¿=¿ (0→1)

0.90.2……0……0.10

…..

≠~

=

Dist.


distance

O G P

Logistic Regression

distance

YesNo


16

Stan

dard

ized

Dat

a


Entity Resolution via Machine Learning Grouping entries based on their distance

Creating all pairs and computing each distance:- Dist. = 0 same- Dist. = 1 not the same

0.90.2……0……0.1

distance

…..

≠~

=

Dist.

Logistic Regression

Trained Threshold

0.9 No0 Yes

0.2 Yes… No

Full

Dat

aset

Dist.


Yes

Nodistance

Subs

et


P A E

APAPPA

E


17Proprietary and Confidential - ©2016 Clarity Solution Group, LLC

Stan

dard

ized

Dat

a

Entity Resolution via Machine Learning

Logistic Regression

Trained Threshold

0.9 No0 Yes

0.2 Yes… No

Dist.Yes

Nodistance

Subs

etHierarchicalClustering

P A E

APAPPA

E

1.2 million

entries !!!

!

USITUSITCA

Filter out exact matches

Use common features to parallelize the calculation

Label unique entries without clustering

USUSITITCA

Clarity Solution Group presentation at the Chief Data Officer Insurance 2016

Data & Analytics

Transcript of Clarity Solution Group presentation at the Chief Data Officer Insurance 2016