Vision 2014: Leveraging Commercial Data and Custom Analytics to Optimize Business Performance

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Leveraging commercial data and custom analytics to optimize business performance

Per Sorenson Interline Brands

Peter Bolin Experian

#vision2014

Sung Park Experian

2 © 2014 Experian Information Solutions, Inc. All rights reserved. Experian Public.

But, the style requires different tools, products

and techniques to get the desired result!

It’s all hair…


And some hair styles we would just rather forget…

I am sure some of us have our own stories,

but that’s for another day


Client spotlight Interline Brands

Interline Brands Inc. comprises 13 unique brands that distribute maintenance, repair and operations products like janitorial and sanitary, plumbing, electrical, hardware, HVAC and other related items

The business is an industry leader that serves a diversified customer base of facilities maintenance professionals, contractors and specialty distributors with approximately $1.6 billion in annual sales

Facilities maintenance:

Residential (20% of revenue)


institutional (50% of revenue)


Multi-family (30% of revenue)

Interline Brands


Interline challenge

Challenges prior to first Custom Score

Managing credit risk with 150,000 active customers across a wide industry base is a challenge

Generic business credit scores provide some performance differentiation across industries, but not consistently

Significant time is spend reviewing and analyzing credit reports

Lack of effectively differentiating the quick “yes” from the “need further review” applications or credit line increase requests

Strained relationships with the sales department as slow turn-around time jeopardizes sales opportunities during high volume periods

Limited view of overall credit risk across all customer segments

Continued challenges post first Custom Score

Evaluating credit lines for existing customers using credit bureau data without payment behavior on us

Better view of overall credit risk across all customer segments however assessment does not include payment behavior on us

WARNING

CHALLENGES

AHEAD


Custom model approach

Generic risk models built on entire business population and predicts likelihood of severe delinquency on any given trade

Custom model optimizes bureau attributes to specifically predict likelihood of an Interline account becoming severely delinquent with Interline

Custom model built in 2010, prior to Interline accounts receivable data contribution

Intelliscore Plus

0 - 30 31-70 71-100

500 goods 1000 goods 3000 goods

100 bads 80 bads 50 bads

16.7% bad rate 7.4% bad rate 1.67% bad rate

Years on file

< 4 5-10 11+

100 goods 300 goods 100 goods

75 bads 20 bads 5 bads

42.9% bad rate 6.25% bad rate 4.8% bad rate

Decision tree


Performance and integration

The custom scores allows the credit function to quickly assess the credit risk and approve a majority of applications and credit line increases quickly

The appropriate accounts and applications are identified for further in-depth review

With the increased effectiveness in differentiating credit risk, credit lines can easily be assigned using a standard credit line table

The BusinessIQSM solution allows for easy access to credit assessment and credit line recommendation

BusinessIQSM allows for ongoing credit risk monitoring of all active account


The need to integrated payment behavior on us in assessing credit line exposure in the automated BusinessIQSM process for existing customers

More effective risk

monitoring and

automation of credit

line assignments

and overall

exposure for

existing customers

BusinessIQSM allows for custom automation of risk assessment

The tool also allows for automated ongoing monitoring of existing customers

Comprehensive data and predictive insights needed to make more informed financial decisions to grow the business

Results

Challenges

Goals

Solution

Results


Updated custom model Leveraging Interline’s data contribution

Observation

December

2012

12 month performance

window December

2013

Outcome

Account management model

Interline’s customer account payment history included in Experian risk score and commercial credit attributes

Value of “behavioral” information

Business attribute

Not including

Interline trade Points

Age of business

Total trades

# of trades current

# of trades 60+ del

Score

10 years

5

5

0

+30

+20

+30

0

80

Including ‘good’


10 years

6

6

0

+30

+25

+35

0

90

Including ‘bad’


10 years

6

5

1

+30

25

30

-30

55


Decision tree approach

Statistical model that determines the optimal attributes for separating the good accounts from bad accounts

Each “branch node” consists of an attribute which can be split on the attribute’s value ranges to further separate goods from bads

Attributes assessed can include:

► Intelliscore PlusSM V2

► Business age (includes Interline trade)

► Recent high credit (includes Interline trade)

► Trade count (includes Interline trade)

► Trade delinquency (includes Interline trade)

► Legal balance

► Collection count * Delinquency status of Interline trade


Interline decision tree Attributes

This tree segment shows

the first split is by

Intelliscore PlusSM V2

Next split is by

years in file

Split by DBT


Interline decision tree with Interline trade Delinquency attribute

This tree segment shows

the first split is by

delinquency status of

Interline trade with Interline

Next split is by

size of Intelliscore

PlusSM V2

Split by years

in file


Custom model results KS (Kolmogorov-Smirnoff)

Re-developed models shows significant performance gains over original

Best performance is gained from model using delinquency status of Interline trades specifically

20

3943

0

5

10

15

20

25

30

35

40

45

50

0%

20%

40%

60%

80%

100%

120%

140%

Original model build New model withInterline trade

New model with tradeand delq

ks % lift


Custom model results Cumulative bad capture

19%

39%

48%

34%

51%

65%

47%

61%

71%

0%

20%

40%

60%

80%

worst 10% worst 20% worst 30%

Original model build New model with Interline trade

New model with trade and delq

New models show superior bad capture performance

New model with Interline trade delinquency status shows marked improvement at the worst 10%


Credit risk evolution From generic bureau scores to segmented custom scores

Your own customer payment data greatly enhances credit score performance

Cre

dit

sc

ore

eff

ec

tive

ne

ss

Credit score evolution

Generic bureau score

Custom bureau score

Custom bureau score

Multiple segments

Generic bureau score

Custom bureau score

Custom bureau score

Multiple segments

No performance data provided to credit bureau

Customer performance data provided to credit bureau


Risk profile and credit line assignment Custom score, credit lines and BusinessIQSM

$0

$2

$4

$6

$8

$10

$12

$14

1 2 3 4 5 6 7 8 9 10

Cre

dit lin

e

Decile Lower credit risk Higher credit risk

Review:

Larger credit line needs and

higher credit risk accounts

manually reviewed

Automation:

Appropriate credit lines

automatically assigned

based on risk profile


Continued use for assessing risk and assigning credit lines to new customers

Enhanced assessment of credit risk for existing customers by integrating payment behavior on us in credit line management

More robust overall view of the current quality of credit exposure

Potential additional benefits:

► Utilize custom scores as risk dimension in collection strategy

► Additional verification of adequacy of bad debt reserve

Updated custom model allows for …


Results (per update)

The need to integrated payment behavior on us in assessing credit line exposure in the automated Business IQSM process for existing customers

Challenges

More effective risk monitoring and automation of credit line assignments and overall exposure for existing customers

Goals

Business IQSM allows for custom automation of risk assessment

The tool also allows for automated ongoing monitoring of existing customers

Solution

Comprehensive data and predictive insights needed to make more informed financial decisions to grow the business

Results

http://www.interlinebrands.com/default.aspx


A greater lift was seen in Interline portfolio performance by leveraging custom analytics and the building of their custom decision trees

Client leveraged custom analytics and their custom decision tree to address specific business objectives

Decision tree was the best choice due to its ability to separate goods from bads and its easy implementation into BusinessIQSM

Final thought

Lift + integration =

Performance


For additional information, please contact:

[email protected]

[email protected]

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Date post:	06-May-2015
Category:	Data & Analytics
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Vision 2014: Leveraging Commercial Data and Custom Analytics to Optimize Business Performance

Data & Analytics