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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
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But, the style requires different tools, products
and techniques to get the desired result!
It’s all hair…
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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
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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)
Facilities maintenance:
institutional (50% of revenue)
Facilities maintenance:
Multi-family (30% of revenue)
Interline Brands
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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
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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
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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
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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
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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’
Interline trade Points
10 years
6
6
0
+30
+25
+35
0
90
Including ‘bad’
Interline trade Points
10 years
6
5
1
+30
25
30
-30
55
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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
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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
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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
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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
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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%
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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
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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
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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 …
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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
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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
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