framework for ai-driven cash collections & claims · operational approach don’t wait for...
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FRAMEWORK FOR AI-DRIVEN CASH
COLLECTIONS & CLAIMS
TRANSFORMING THE A/R TEAM’S ROLE
YOUR PRESENTER
Shankar Bellam
Enterprise Cloud Solution Expert
HighRadius
As part of HighRadius’ Solution Engineering team, Shankar is
responsible for helping companies unlock value in their
accounts receivable processes by identifying and realizing
improvement possibilities. Shankar and his team have
helped several Fortune 1000 companies realize their A/R
transformation initiatives
YOUR PRESENTER
Magnus Carlsson
Manager, Treasury & Payments
Magnus Carlsson is the Manager for Treasury & Payments
covering payments efficiency through electronic payments,
protection against payments fraud and payments
standards. Magnus is also responsible for the Annual
Payments Roundtable at the AFP Conference and leads the
Payments Track Task Force.
• Payments Operations
• AP
• AR
• Efficiencies
• What are the pain points?
• The aim is to provide helpful insights and tips
• New technologies
Payments Operations Webinar Series
New Technologies
AGENDA
SHIFTING A/R FOCUS FROM TRANSACTION MANAGEMENT TO CUSTOMER ENGAGEMENT
AI DRIVEN COLLECTIONS MANAGEMENTPREDICTION OF INVOICE PAYMENT DATE
AI DRIVEN DEDUCTIONS MANAGEMENTPREDICTION OF DISPUTE VALIDITY
TAKEAWAYS
SHIFTING A/R FOCUS FROM TRANSACTION MANAGEMENT
TO CUSTOMER ENGAGEMENT
A/R TEAMS STAY GLUED TO EACH TRANSACTION LONG
AFTER IT HAS HAPPENED
▪ Collate and centralize transaction supporting documentation
remittances, PODs, BOLs, claims, SLA reports, help-desk notes, sales correspondence
▪ Attach related documents to customer emails
▪ Post related documents to web portals
▪ Apply cash by matching payments, remittances and open invoices of ERP
▪ Code and verify deductions
▪ Create and provide customers with access to invoices, disputes and collections
correspondence
Transaction Focused A/R Management
TRANSACTION FOCUSED A/R NEGATIVELY IMPACTS
BOTTOM-LINE
Companies Spend A Lot Of Money On Reconciling And Managing Payments That Has Already Been Made
Source: “The Accounts Receivable Network Report Benchmarking” : Collections Practices and Metrics
4¢ - 25¢for every
$1
Cost of
post-transaction activities
IS THERE AN ALTERNATIVE FOR TRANSACTION
MANAGEMENT?
Transaction management is a necessary evil but if your
A/R team can free up time from transaction management,
they can use it instead to focus on customer engagement.
SHIFTING FOCUS TO CUSTOMER ENGAGEMENT
▪ Better understanding of each and every customer
▪ Ability to improve customer satisfaction, loyalty and profitability
▪ Get paid faster
▪ Achieve organizational AR goals – reduce DSO/DDO, reduce bad-debt
But then, who will handle transactions?
Data Driven Artificial Intelligence!
AI + DATA FOR CUSTOMER CENTRIC AR
INVOICE FACTORS• Past invoice count• Previous payment times• Due month
• Invoice value• Total Current Invoice value• Day of the week due
CUSTOMER FACTORS
• Average number of invoices per payment
• Total open amount• Gap between payments• Average delay• % of payments delayed
DISPUTE CASE FACTORS• Delay; invoice date vs. claim date• Claim month• Product category historic invalid %• Customer historic invalid %• Ship-to historic invalid %
• Dispute amount vs. customer historic dispute amount
A/R Data
PROCESS
AUTOMATION
+
ARTIFICIAL
INTELLIGENCE
Transaction Management
▪ Task automation
Customer Insights
▪ Prediction of customer payment behaviour
▪ Accurate validation of deductions
▪ Better insights into delinquency
POLL QUESTION 1
How would you rate your understanding of Artificial Intelligence in
the context of Accounts Receivable?
a) Know a lot
b) Somewhat familiar
c) No idea
AI-DRIVEN COLLECTIONS
MANAGEMENTPREDICTION OF INVOICE PAYMENT DATE
REACTIVE COLLECTIONS MANAGEMENT IS BASED ON
STATIC PARAMETERS
Worklist prioritizationBased on static
parameters
Aging
Past Due Amount
Disputed Amount
Credit Limit, % Utilization
Risk Category
Broken Promise to Pay
Dunning Level
PROACTIVE COLLECTIONS MANAGEMENT IS DRIVEN BY AI
Worklist prioritization
Aging
Past Due Amount
Disputed Amount
Credit Limit, % Utilization
Risk Category
Broken Promise to Pay
Dunning Level
Dynamic, driven by AI
Predicted
Invoice
Payment
Date
Based on static
parametersWorklist prioritization
COLLECTIONS MANAGEMENT WITH AIPredict Expected Invoice Payment Date
Past
payment behaviour
Current
open invoices
Machine
learning algorithms
Predicted payment date
ALL FACTORS
INVOICE FACTORS
All invoice related parameters
CUSTOMER FACTORS
All account related parameters
CUSTOMER FACTORS
• Average number of invoicesper payment
• Total open amount• Gap between payments• Average delay• % of payments delayed
INFLUENCING FACTORS
INVOICE FACTORS
•Past invoice count•Gap ratio•Previous payment times•Due month•Invoice value•Total Current Invoice value•Day of the week due
FEATURES IN PLAY | MACHINE LEARNING FOR COLLECTIONS
PREDICTION MODELS
• Binary classification
•Multiclass classification
•Random Forest Classification
UNDERSTANDING RANDOM FOREST REGRESSION MODELGoing To The Movies, With A Data Scientist
What movie should I watch?
20QUESTIONS
Recommendation
Melissa
What movie should I watch?
UNDERSTANDING RANDOM FOREST REGRESSION MODELGoing To The Movies, With A Data Scientist
Melissa Jessica Brenda
Recommendation Recommendation Recommendation Recommendation Recommendation
Ron Mike
20QUESTIONS
20QUESTIONS
20QUESTIONS
20QUESTIONS
20QUESTIONS
Individual Perceptions Of Same Input
What movie should I watch?
Recommendation Recommendation Recommendation Recommendation
20QUESTIONS
20QUESTIONS
20QUESTIONS
20QUESTIONS
20QUESTIONS
Recommendation
Melissa Jessica Ron Brenda Mike
UNDERSTANDING RANDOM FOREST REGRESSION MODEL
TEST RESULTS FOR RANDOM FOREST REGRESSION MODEL
Ac
cu
rac
y
Pe
rce
nta
ge
61%
42%
78%
71%
82%86%
89% 91% 92% 93% 94%
0 1 2 3 7 8 9 10
Cumulative difference
Percentage of Invoices Predicted Correct (cumulative)
4 5 6
ANALYST DASHBOARD WITH INVOICE PAYMENT DATE PREDICTION
2828Predicted Payment Date
PRIORITIZED COLLECTIONS WORKLIST
Collections rules based on
Invoice value Static parameter
from open A/R X
Number of days for
invoice to be due Dynamic parameter calculated
from open A/R
Predicted delay Proactive parameter
predicted by Artificial
Intelligence
Amount > $20,000 due in < 15 days predicted delay > 20 days
Amount > $10,000 due in > 15 days predicted delay > 15 days
Formulate Multi-dimensional Collections Strategy
IMPACT OF PROACTIVE COLLETIONS ON BOTTOM-LINE
Open
invoices
Payment date predicted
Strategize dunning for each open
invoice
Start dunning process
On-time payment by customers
Close
invoicesLow risk and high
risk accounts identified and
prioritized
1 2 3 4 5 6 7
Request upfront payment for accounts or particular
invoice
Updating credit terms to proactively minimize delay
in payment
Require payment commitments at the time
of order creation
Predicted Delay For Order And Credit Management
BENEFITS OF PROACTIVE COLLECTIONS MANAGEMENT
InvoicesAverage Delay
(Reactive)
Average Delay
(Proactive)%
All Delayed Invoices 10.7 5.8 45.8%
Invoices delayed by > 15 days 34 20 41.1%
OPERATIONAL APPROACH
DON’T WAIT FOR INVOICES TO BE PAST-DUE, TAKE PROACTIVE ACTIONS
WITH PREDICTED DELAY
50% faster collectionIf the predicted bucket is same or
more than actual bucket
25% faster collectionIf the predicted bucket is less than
actual bucket by 1
10% faster collectionIf the predicted bucket is less than
actual bucket by more than 1
Source : Zeng, S. (2008). “Using Predictive Analysis to Improve Invoice-to-Cash Collection”. Association For Computing Machinery.
PROACTIVE COLLECTIONS MANAGEMENT SUMMARY
Accurate predictions of
payment delays
Proactive
Collections
Actions/Strategy based on
predictions
• Focusing on customers with a higherlikelihood of delayed payments
• Updating credit terms to proactively
minimize delayed payments
DEDUCTIONS
MANAGEMENT
AND ARTIFICIAL INTELLIGENCE
VALIDITY OF DEDUCTIONS IS UNKNOWN UNTILL
RESEARCH IS COMPLETED
Dollar Value Status Priority
High Invalid High
Medium Invalid Medium
High Valid Low
Low Valid Lowest
The Deductions Paradox
You do not know whether it’s worth it;
till you complete research.
The Deductions Paradox
BUSINESS IMPLICATIONS OF THE DEDUCTIONS PARADOX
Lost DollarsWrite off invalid deductions
Time & Productivity LossResearch valid deductions
High False PositivesWrite-offs for disputes that seem
valid but actually invalid
AUTONOMOUS DISPUTE RESOLUTIONMachine Learning for Deductions
Past resolution
patterns
Current
deduction
characteristics
Machine
Learning
Algorithms
Eliminate work lost on
valid deductions
Prioritize high-probability
invalid deductions
Identify and control
inaccurate write-offs
FEATURES IN PLAY | MACHINE LEARNING FOR DEDUCTIONS
ALL FACTORSINFLUENCING FACTORS
PREDICTION MODELS
All dispute related parameters
Dispute Case Factors
All factors related to cleared invoices
Cleared Invoice Factors
Dispute Case Factors
Cleared Invoice Factors
• Delay; invoice date vs. claim date
• Claim month
• Product category historic invalid %
• Customer historic invalid %
• Ship-to historic invalid %
• Dispute amount vs. customer
historic dispute amount
• Fiscal period
• Original dispute / invoice amount
• Cash discount vs. invoice amount
• Invoice amount vs. customer’s
historic invoice amount
• Binary classification
•Multiclass classification
•Random Forest Classification
ANALYST DASHBOARD WITH DISPUTES VALIDITY PREDICTION
Both the remittance
captured
Validity prediction of a dispute with a
confidence percentage
RESULTS OBTAINED WITH ARTIFICIAL INTELLIGENCE
• 94% accurate VALID dispute
prediction
• 93% accurate INVALID dispute
prediction
94% of all dispute cases
predicted accurately
• 92% INVALID dispute dollars
identified
• AI predicts with high
degree of confidence
• Are under the auto write-off
threshold
30-40% decrease in
human touches
• Comply with other internal
business rules
Work eliminated on VALID disputes which
~ 12% leakage identified
PROACTIVE DEDUCTIONS MANAGEMENT SUMMARY
Most Likely Valid <<< >>> Most Likely Invalid
VALID INVALID
50%
Automate PrioritizeRealize
TAKEAWAYS
TAKEAWAYS
▪ Transaction focused AR impacts bottom-line
Poor customer loyalty and profitability
▪ Shift AR team focus to customer engagement
Ensure your organization takes top priority in your customers’ pay cycle
▪ Credit and AR data combined with Artificial Intelligence
Gateway to proactive, customer-centric AR management
▪ Collections management with Artificial Intelligence
Transition from reactive to proactive collections with prediction of invoice
payment date
▪ Deductions management with Artificial Intelligence
Solve deductions paradox with prediction of disputes validity
POLL QUESTION 2
Would you like to learn more about Artificial Intelligence in AR and its best-practices?
a) Yes
b) Yes, but not right now
c) No
375+ Clients. #1 in Fortune1000 market
$500 Billion of receivables processed
annually
750+ employees globally
Integrated Receivables Platform for the
entire credit-to-cash cycle
FinTech cloud-based software company.
Founded in 2006. HQ in Houston, Texas Select Customers
$50 Million secured in growth funding from
Susquehanna Growth Equity$
HIGHRADIUS AT A GLANCE
Contact Information
Elaine M. NowakDirector, Product Marketing
281.394.0221
EVERYTHING YOU WANTED TO KNOW ABOUT AI IS HEREWould you like to learn more about the AI technology in A/R and its best-practices?
https://www.highradius.com/AIVisit
Webinars E-books
Questions?