FRAMEWORK FOR AI-DRIVEN CASH COLLECTIONS & CLAIMS · OPERATIONAL APPROACH DON’T WAIT FOR...

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

elaine.nowak@highradius.com

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?