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Transform Your Credit and CollectionsUse predictive analytics to leverage your big data and drive strategic collections
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What’s top of mind for credit and collections professionals?
Drive free cash flow
ReduceDSO
Productivity
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Automate processes
Centralize data
Leverage predictive analytics
How can you increase cash flow?
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How can I automate processes and centralize data?
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Collectors should be spending less time
manually prioritizing collections, preparing for calls, managing disputes
The challenge is the process
Collectors should be spending more time on the phonecalling accounts
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One solution prioritizes calls for the collectors and provides a single view into cash and risk
Rulesengine
OracleJDE SAP
AS400
Centralizedrepository Collections
management
Creditfacilitation
Cash application
Disputeresolution
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What is predictive analytics?
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Predictive analytics uses statistical-based risk models that look at your own payment
experiences with each customer in order to create a monthly risk score. This risk score is
the key component and is used to drive highly impactful collections prioritization.
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One solution prioritizes calls for the collectors and provides a single view into cash and risk
Rulesengine +
Predictive analytics
OracleJDE SAP
AS400
Centralizedrepository Collections
management
Creditfacilitation
Cash application
Disputeresolution
Adding predictive analytics to your
automation tool….
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Valu
e to
clie
ntValue/aging-
based
Strategy- based
Predictive- based
Analytical complexity
71% of companies are still using age and value to prioritize collections
Collections is evolving toward predictive analytics
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Predictive analytics takes collections to the next level…
Accurately identify collection risk in
your portfolio
Prioritize the highestrisk accounts to be worked proactively
Maximize the productivity of your
collections efforts
Customer Customer Customer Customer Customer Customer Customer Customer Customer Customer Customer
Customer Customer Customer Customer Customer Customer Customer Customer Customer Customer
Customer Customer Customer Customer Customer Customer Customer Customer Customer Customer Customer
Customer Customer Customer Customer Customer Customer Customer Customer Customer Customer
Mediumrisk
Highrisk
Lowrisk
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All customers fall into 3 different buckets
CustomerCustomer
Customer Customer
Customer
Customer Customer
Customer
Customer
Customer
CustomerCustomer
Customer
Customer
Customer
CustomerCustomer
Mediumrisk
Highrisk
Lowrisk
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Customer
These customers move from bucket to bucket based on payment behavior.
Mediumrisk
Highrisk
Lowrisk
Mediumrisk
Highrisk
Lowrisk
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Customer
70% of risk is in 30% of your customers
Customer
Customer
Customer
Customer Customer
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Would you use the same type of contact with your low risk and high risk accounts?
Of course not!
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Customer BCustomer AOwes $25,000; 30 days past-due
Scores low risk
Probability of going BAD is 2%
Cash at risk: 2% x $25,000 = $500
Owes $15,000; Less than 30 days past-due
Scores high risk
Probability of going BAD is 50%
Cash at risk: 50% x $15,000 = $7,500
Who should you call first?
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Mediumrisk
Highrisk
Lowrisk
5 days after due date Due date 5 days later
Day before invoice is due 5 days laterDay after
due date I
5 days later
3 days later
5 days before invoice is due
Day invoice is due 5 days later
5 dayslater
Use predictive analytics to contact customers based on their level of risk – save majority of calls for high risk customers
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Who do you call first? Customer B
Customer BCustomer AOwes $25,000; 30 days past-due
Scores low risk
Probability of going BAD is 2%
Cash at risk: 2% x $25,000 = $500
Owes $15,000; Less than 30 days past-due
Scores high risk
Probability of going BAD is 50%
Cash at risk: 50% x $15,000 = $7,500
Power of predictive analytics
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How has FIS helped others like me?
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66.2% accounts flagged as high risk
High spend on bureau data
Before the use of predictive analytics
Edward Don & Co.
Headquarters:United States
Industry:Distributor of food service equipment
Annual revenue:$600 million
High probability of customers going delinquent
Time spent on research inhibited calling efforts
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After the use of FIS’ predictive analytics solution
Proactive contact through automation
Drastic reduction in bureau data spend
14.1% accounts actually high risk (statistical scoring)
25% of increase in collections calls
Edward Don & Co.
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FIS’ predictive analytics capabilities enabled us to improve prioritization and lower DSO by 5.3 days.
Edward Don & Co.
©2016 FIS and/or its subsidiaries. All Rights Reserved. FIS confidential and proprietary information.
