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Bryan LambkinVP-Treasurer & Assistant Secretary at HNTB
Tracey Ferguson KnightDirector, Solution Engineering (Treasury), Highradius
Redefining the Fundamentals of Cash Forecasting with Artificial Intelligence
Agenda
About HNTB
Challenges Faced By The Team
Why Choose AI?
Expectations From AI
Turbulent times
Turbulent Times
Survey Results
http://www.treasurycoalition.com
According to Global Recovery Monitor survey by Treasury Coalition
What's the top concern for your business due to COVID-19?
Poll Question
a)Direct financial impact on busines
b)Recession
c)Staff safety Protocols
d)Access to adequate liquidity
Top Priorities of Treasurers
SOURCE: TREASURY STRATEGIES, 2020 STATE OF THE TREASURY PROFESSION SURVEY
Cash forecast ing cont inues to be a top pr ior i ty for t reasurers .
2020 RANK 2019 RANK 2018 RANK
CASH FORECASTING 1 2 3
B E S T P R AC T I C ES 2 11 9
B AN K R E L AT I O NSH I P M AN AG EMENT 3 4 5
WO R K I NG C AP I T AL M AN AG E MENT 4 1 -
O P E R AT I O NAL E F F I C I ENC Y 5 13 7
Top Priorities For Treasurers
About HNTB
Leading infrastructure firm designed many sports facilities, airports,
bridges, tunnels, roadways, and rail and transit systems
Founded in 1914, HQ in Kansas City
Global revenues exceeding $1.5 BN
Treasury Landscape at HNTB
CFO
VP Treasurer
Tax and LicensingTreasury Real Estate Procurement
The Process Of Cash Forecasting
The Purpose of Cash Forecasting?
Purpose of Cash
Forecasting
Frequency and
Accuracy HOW?1 2 3
91% of organizations
are still using
SPREADSHEETS for
Cash Forecasting*
*HighRadius Cash Forecasting Survey 2019
SOURCE: TREASURY STRATEGIES, 2018 STATE OF THE TREASURY PROFESSION SURVEY
83% of Treasurers consider forecast inaccuracy their biggest concern
Inefficient or error-prone technology used to forecast cash
Insufficient time to perform regular and accurate forecasts
Inability to gather and leverage the right datasets
Absence of regular variance analysis to improve and analyze the
process
Lack of visibility into individual unit-level forecasts
Cash Forecasting Challenges
Objectives of Cash Forecasting
1. Estimating year-end cash-basis income
2. Forecasting use of credit facility and interest expense
3. Maintaining credibility among the shareholders
Three main reasons to forecast
Oracle ERP
• DSO and P2P• Annual Revenue
• Payment Terms• Disbursements
• Weekly Avg YTD
Analyst
AR Team
AP Team
AP Forecast
AR Forecast
AR Data
Excel
90-Day forecast
Updated Weekly
12-Month forecast
Updated monthly
Manager and VP
Initial Forecasting Process- Manual State
Challenges with Current Forecasting Process
1800+ =1800Multi-level Projects
No Client-level Drill Down
Too many variables
No process to interpret data
Inaccurate Top-Down Approach means
Low Visibility into individual projects
Contracts
Clients
Performance Data
The Purpose of Cash Forecasting?
Purpose of Cash
Forecasting
Frequency and
Accuracy HOW?1 2 3
Determine your
desired outcome
Poll Question
What is your preferred source for learning about new treasury innovations?
a)Webinars
b)Articles and Blogs
c)Banks/Vendor Relationships
d)Conferences (Virtual / In-person )
• Improve forecast accuracy
• Increase workflow efficiency
• Increase visibility for better Cash Management
• Increase confidence in investment decision making
• Manage line of credit
Desired Outcome
The Current State
ERP
• Payment Terms
• Disbursements
• Weekly Avg YTD• Other data
Cash Forecasting Cloud
AP Data
AR Data
• DSO and P2P• Annual Revenue
• Other data
Treasury TeamMonthly forecast
Updated Weekly
Improved Process With AI enabled Cash Forecasting
Implementation Methodology
User Acceptance Testing ProductionData Sciences
Analysis of data;
Deep understanding
of current process;
Building of models
Adoption of new
system into daily
processes
An opportunity to
parallel new forecast
against current
process
Results
Increase in AR forecast accuracy
for a month
50% 47%
Increase in AP forecast accuracy
for a month
Accuracy achieved in AR Forecast
Accuracy achieved in AP Forecast
94% 95%
Artificial Intelligence 101
Predicting a Payment Date
UNP AI DI N V O I CE
P RE D I CT E D
P AY M E N T D AT E?
Impact on Accuracy
NUMBER OF VARIABLES
PR
EDIC
TIO
N A
CC
UR
AC
Y
2 3 4
ADP
INVOICE DATE
CUSTOMER
ADP
INVOICE DATE
ADP
INVOICE DATE
CUSTOMER
AMOUNT
Artificial Intelligence 101
V AR I AB L E S
PR
ED
ICT
ION
AC
CU
RA
CY
C U S T O M E R
A D P
P A Y M E N T
F R E Q U E N C YV 6
I N V O I C E
D A T E
I N V O I C E
A M O U N TV 5 V 7
S e l e c t t h e r i g h t v a r i a b l e s t h a t c o r r e l a t e t o t h e p r e d i c t e d out c om e
Choose the Best-Fit Curve
MASTER L IS T
Invoice DateCustomer-Specific ADPInvoice AmountPast Invoice CountTotal Open AmountGap between PaymentsDelayed Payments PercentageBranch-Level DelaysClosed Invoice SumDelayed Invoice SumDue Payment Day of the WeekPast Total Delay CountCustomer Average Open AmountClosed Invoice CountPast All Invoice CountDue Gap for Customer
CORRELATED
Invoice Date
Customer-Specific ADP
Invoice Amount
Total Open Amount
Gap between Payments
Delayed Payments
Percentage
Branch-Level Delays
Closed Invoice Sum
Due Payment Day of the
Week
ALGORITHMS
Linear Regression
Logistical Regression
Random Forest
Classification
Neural Networks
Decision Trees
Support Vector Machine
Gradient Boosted Trees
K-Nearest Neighbor
XG Boost60+I N V O I C E & C U S T O M E R
L E V E L V A R I A B L E S
30+C O R R E L A T E D
V A R I A B L E S
25+A I A L G O R I T H M S
P R E D I C T E D
P AY M E N T D AT E
Automated Forecasting Across Categories
AUT O M A T E DCAS H F L O W F O R E C A S T
NO N -O P E R AT I O N A L CAS H F L O W S
O P E R AT I O N A L CAS H F L O W S
AR AP P AY R O L L T AX E S CAP E XI N V E S T M E N T
& D E B T O T H E RS
S H O R T
T E R M
L O N G
T E R M
S H O R T
T E R M
L O N G
T E R MAI M O D E L S
H E U R I S T I C M O D E L S
Automatic Roll-Up to Central Treasury
G L O B A LCAS H F L O W F O R E C A S T
F O R E C A S T0 1
F O R E C A S T0 3
F O R E C A S T0 5
F O R E C A S T0 2
F O R E C A S T0 6
F O R E C A S T0 4
Benefits of AI-enabled Cash Forecasting
ACCURACY
AUTOMATION
• Utilize time for more value-added activities
• Increase interest income
• Decrease interest expense
• Build and maintain credibility
• Make proactive strategic decision
What’s Next?
Top Priorities for Treasurers in the Future
SOURCE: Crystal ball survey by HighRadius
Cash forecast ing i s no longer a top pr ior i ty for t reasurers .
2020 RANK 2022 RANK 2025 RANK
CASH FORECASTING 1 10 -
B E S T P R AC T I C ES 2 11 9
B AN K R E L AT I O NSH I P M AN AG EMENT 3 4 5
WO R K I NG C AP I T AL M AN AG E MENT 4 1 -
O P E R AT I O NAL E F F I C I ENC Y 5 13 7
Questions?
Thank you!
Bryan LambkinVP-Treasurer & Assistant Secretary at
HNTB
Tracey Ferguson KnightDirector, Solution Engineering (Treasury),
Highradius
The Business Case for Automating Cash ForecastingUse this e-book to help you develop a business case for investing in AI-enabled Cash Forecasting solutions to drive better debt and investment decisions.
e-book
Copyright © HighRadius Corporation 1
CUSTOMER SUCCESS STORY
Table of Contents
Copyright © HighRadius Corporation 2
ROI Is the Key to Making a Case for Cash Flow Forecasting 3
Calculating ROI for Cash Forecasting 3
Calculating Gross Savings 4
OPEX Savings
Investment Interests
Debt Savings
Calculating CAPEX and OPEX 6
Summary 7
Cash Forecasting Case Study 8
About HighRadius 11
ROI Is the Key to Making the Case for AI-Enabled Cash Forecasting
• “We are stretched thin and need better systems.”• “My team is frustrated because of repetitive, manual work.”• “It will help me focus on strategic initiatives.”
Copyright © HighRadius Corporation
Each one of these “rationales” for bringing in an automated system carries with it an element of subjectivity which can be negated either through more objective thinking or a difference of opinion. For example, the complaint of being stretched thin will most likely be countered by a statement of “you need to do a better job of managing the workload across your department.”
Executive management views successful projects as either making money, saving money or strengthening an existing initiative. Persuasive arguments for technology investments should be framed in these terms. Unfortunately, departments often attempt to convince management to fund automation projects based upon statements such as these:
CFOs are often the primary decision maker and are concerned with how to “do more with less.” This translates into two important performance indicators: reducing costs or increasing revenue.
A strong, quantitative business case should highlight the business impact by drilling down on the different expenditures and savings involved as explained below.
Calculating ROI for Cash Forecasting
In the following chapters, we break down each of these components and analyze how they contribute to a winning business case.
Net SavingsA. GrossSavings
B. Capital Expenditure
C. Operational Expenditure
Automation Savings
Increased Investment
Interest
Decreased Interest Expense
3
CFOs Need Quantitative Metrics for Decision Making
50%
45%
36%
19%
17%
13%
12%
Reducing costs
Increasing cash flow
Introducing newproducts/services
Reducing leverage
Expanding by acquisition
Disposing of assets
Increasing capitalexpenditure Source: Deloitte CFO Survey: 2019 Q4
Calculating Gross SavingsOPEX Savings + Investment Interests + Debt Savings
Identify Process TasksThe cash forecasting process tasks vary between companies. In general, they depend on the size of the company and the organizational structure of the finance department.
To get started, benchmark your current automation savings and set a target goal of the savings you’ll realize with a solution.
A. Gross SavingsAutomation
SavingsIncrease Investment
InterestDecreased interest
Expense
Calculating Automation Savings
Step 1
Calculate Time SpentReview the current process and identify how many hours per week on average your team spends on each task. This gives you the total FTEs your team expends on forecasting cash over a given period.
Step 2
Forecast Time SavingsA sample hourly breakdown of tasks and average hour savings with automation is shown below:Step 3
TaskHours/Months
spent
Average Automation
Average Savings
Rationale
Gather bank data 13 90% 12Solution pulls in all the bank data automatically and parses for positioning.
Solicit data from country controllers
13 88% 11Solution directly pulls info from ERP(s) and eliminates soliciting required data.
Solicit data from other departments
26 88% 23Solution directly pulls info from ERP(s) and eliminates soliciting required data.
Create models (compare to prior periods/years)
26 95% 25Solution creates models and forecasts for all/most cashflow categories.
Create current forecast 65 90% 59Solution aggregates and publishes the forecast.
Create variance analysis 65 95% 62Solution creates variance analysis over multiple time horizons.
Update/Consolidate/Expand spreadsheets
34 95% 32Solution gets updates from data sources and reforecasts for all current and future days.
Get FX rates 5 90% 5 Solution pulls data directly from data source.
Gather data from TMS 13 100% 13 Solution pulls data directly from data source.
Total Hours 260 Hours Saved 242
Copyright © HighRadius Corporation 4
Follow this 4-Step, Adjusted Value Stream Analysis Best Practice Process
Calculate Final automation SavingsTo calculate automation savings multiply the hours saved per week by the loaded labor costs.Step 4
Automation Savings
Calculating Gross SavingsOPEX Savings + Investment Interests + Debt Savings
A. Gross Savings Automation SavingsIncreased Investment
InterestsDecreased Interest
Expense
Calculating Increased Investment InterestsWith accurate cash forecasting powered by AI, companies can significantly reduce average variance and earn additional interest by investing proactively.
SNo Parameters Values
1. What is your average variance? $40,00,000
2. Expected improvement with technology 50%
3. Expected decrease in variance (1 x 2) $20,00,000
4.At what average rate are your proactive investments (not sweeps)?
2%
5.Interest gained by investing the reduced variance amount (3 x 4)
$40,000
A. Gross SavingsIncreased Investment
InterestsDecreased interest
Expense
Calculating Decreased Interest ExpenseWith reduced variance in forecasts, companies don’t need to borrow as much from external sources and hence, don’t have to pay interest associated with borrowing.
SNo Parameters Values
1. What is your variance? $40,00,000
2. Expected improvement with technology 50%
3.Change in borrowing amount due to reduced variance (1 x 2)
$20,00,000
4.Advance borrowing rate (rate for LIBOR or term loans on Revolver)
7%
5.Decrease in interest expense because of reduced borrowing (3 x 4)
$140,000
A. Gross SavingsAutomation
SavingsIncreased Investment
InterestsDecreased Interest
Expenses
Companies will get either increased interest or decreased interest expense depending on whether they are net investors or net debtors.
Copyright © HighRadius Corporation 5
How to Calculate the Interests Gained
Sweeps are not considered
How to Calculate the Savings Impact on Reduced Borrowing
Savings impact from partial reduction in
emergency borrowing is not considered
Calculating CAPEX and OPEXSaaS vs On-Premise
6Copyright © HighRadius Corporation
Net SavingsA. GrossSavings
B. Capital Expenditure
C. Operational Expenditure
Calculating Operational Expenditure
Operational expenditure (OPEX) is the ongoing costs for running a process. OPEX includes the money spent on regular maintenance of the IT infrastructure. It consists of subscription fees and maintenance costs.
Subscription Fees: A periodic (monthly, yearly, or seasonal) fee to gain access to the product or service. It typically includes the license, support, and other fees.
Net SavingsA. GrossSavings
B. Capital Expenditure
C. Operational Expenditure
Calculating Capital ExpenditureCapital expenditure (CAPEX) is the one-time expense for implementation. It consists of hardware costs and software costs.
Net SavingsA. GrossSavings
B. Capital Expenditure
C. Operational Expenditure
Hardware Costs for On-premise Projects Procuring servers Software licenses IT resources to install, configure, and
manage new hardware
Hardware Costs for SaaS Projects✓ No cost, no hardware involved, and the
system operates entirely through the cloud (over the internet)
Software Costs for On-premise Projects Upfront license costs Maintenance costs Cost of solution customization as per business
requirement (customized forecasting models)
Software Costs for SaaS Projects✓ There are usually no up-front costs
Consider SaaS over On-Premise to Lower CAPEX & OPEX
One time implementation: Reduce OPEX by opting for one time implementation solutions. The vendor is responsible for updates and upgrades which are usually seamless and subject to standard service level agreements (SLAs)
Summary
7Copyright © HighRadius Corporation
LOW CAP ITAL EX P EN D IT U R E WIT H S O F T WAR E - A S - A - S ERV I C E M O D EL
Get a Free Value Assessment of Your Existing Process
Visit highradius.com/treasury for free assessment
Build a Winning Business Case for AI-Powered
Cash Forecasting SaaS Solution
Additional IT hardware is never required
Internal resources aren’t strained as integration is relatively straightforward and requires minimal external support
LOW O P ER AT IO N AL EX P EN D IT U R E WIT H Z ER O IT M AIN T EN AN CE CO S T S
H IG H G R O S S SAV IN G S L EAD IN G TO LOW PAY B ACK T IM E
AI-enabled forecasting frees up bandwidth of analysts to focus on liquidity planning versus data gathering and model creation
Low variance forecasts result in reduced borrowing and improved long-term investment
HighRadius offers you a FREE Value Assessment Service to see how automation and increased
accuracy can benefit your organization. In this analysis, HighRadius will perform a methodical
evaluation of your current state (‘as-is’) across your people, process and technology.
At the end of the value assessment you will be armed with a mapped current process and time
spent, the new ‘future-state’ model and flow diagrams, a gap-analysis including high-level
requirements, and a ROI model for project implementation.
Higher forecasting accuracy with AI replacing
spreadsheet formulas
Fully automated cash forecasting with daily
updates
Accurate 1 to 12-month cash forecasts for
optimized debt and investment decisions and
accurate predictions of quarter-ending cash
B EN EF IT S IN CLU D E:
Give AI-Powered Cash Forecasting a Try
About Company
An Engineering and Architecture based solutions firm which understands the life cycle of infrastructure and addresses complex technical, financial and operational challenges, and delivers infrastructure-related services, including planning, design, program management and construction management.
Industry: Civil Engineering
Revenue: $1.5 Billion
How a $1.5 Billion Firm Achieved
94% Accuracy in AR and AP
Forecasts with Artificial Intelligence
Copyright © HighRadius Corporation
CUSTOMER SUCCESS STORY
Treasury LandscapeThe data collected from AR and AP teams was insufficient, inaccurate, and not up to expectation hence they switched to analyzing previous year and previous month data to estimate AR and AP values.
CASH FORECASTING CLOUD
ARTIFICIAL INTELLIGENCE POWERED
Treasury Team
VP
Director
Analyst
ERP Systems
• Oracle• Hyperion• Microsoft AX• Workday
AS-IS State
Historical data used for adjusting
forecasts, Annual Revenue and DSO used as indicators
Weekly forecasts were done by Treasury team
based on payment terms & disbursements
data from ERP
Forecasting started one week before every
month and updated reports were shared
weekly with stakeholders
90-Day forecasts were created, based
on which 30-Day & 60-Day forecasts
were adjusted weekly
May 25 to June 26th was Accounting month where 12-
Month forecast was generated
AR and AP teams struggled with
forecasting while Payroll data from HR remained constant
'Pay-when-paid' terms with sub-contractors fluctuated AP, used weekly Avg YTD with adjustments instead
Utilized top-down approach based on
billings and AR balances
• No Variance Analysis, only Cash Position comparisons
• No control over data from AR and AP teams
Less Scope for Improvement in Forecasts
Challenges with Existing Process
• Guesswork forecast model based on payment history and open invoices
• Excel sheets used for forecasting• AR forecast majorly derived from DSO
Simplistic Forecast Model with Low Accuracy
• Up to 20 hours spent in a week on cash forecasts
• 90-day forecasts updated weekly• Medium-term forecasts updated
once a month
Manual and Tedious Process with Limitations
• No daily, weekly or monthly forecasts
• Long-term forecasts were done on an as-needed basis
Low Visibility into Future Cash Positions
• AR data updated weekly on ERP• Bank data collected from website• Forecasts from AP team was only
one week out with low visibility
Slow Data Gathering from Multiple Sources
• Manage line of credit• Seasonality affected operations
and created unpredictability• Data presented to higher
management in Excel sheets
Additional Challenges
Results
Copyright © HighRadius Corporation
TO-BE State
Increase in AR forecast accuracy
for a month
50% 47%
Increase in AP forecast accuracy
for a month
Accuracy achieved in AR Forecast
Accuracy achieved in AP Forecast
Achieved end-to-end automation
Improved forecast accuracy by 40%
Improved reporting method to stakeholders
Refined Data Collection from various teams
Increased Visibility for better Cash Management
Increased confidence in decision making
Managed Line of Credit
Increased workflow efficiency
94% 95%
Cash Forecasting Cloud
Continuous ForecastingAccuracy Improvement
Integrations with ERPs, Banks and FP&A Systems
Daily Forecasts atGL Account Level
AI-enabledCash Forecasting
Cash Management Cloud
Daily and IntradayCash Positions
Automated TransactionCategorization
Automated BankStatement Parsing
Centralized Visibilityto Cash Positions
Bank Reconciliation Cloud
Automated GLReconciliation
Rules-basedBank File Enrichment
Automated Parsingand Classification
Supports BAI2,MT940 and ISO20022
Copyright © HighRadius Corporation
About the Solution• AI Based Forecast for Accounts Receivable-
Machine Learning to predict invoice-level payment dates; rolling up to provide highly accurate receivables forecasts.
• AI Based Forecast for Accounts Payable-Machine Learning models study patterns in vendor payment history and open purchase orders, invoices for accurate payables forecasts.
• Flexible Models for Other Operational Flows-For operating cash flow categories such as payroll and expense reimbursements, either a machine learning or a heuristic model is adopted based on the data set complexity.
• Configuration of Non-Operational Cash Flows- Tax, investments, debt instruments and other non-operational segments can be automatically predicted based on historical inflows and outflows or can be manually configured by the user on a periodic basis.
• Integration of Forecasts From FP&A Team-Integration with Profit & Loss forecasts from Finance Planning & Analysis teams to fine-tune the accuracy of medium and long-term cash forecasts.
• Continuous Improvement of Forecasting Accuracy- Closed-loop Machine Learning feedback system automatically reconfigures forecast models by comparing forecast versus actual cash positions to improve accuracy with time.
Treasury Management Platform
About HighRadius
HighRadius is a Fintech enterprise Software-as-a-Service (SaaS) company which leverages Artificial Intelligence-based Autonomous Systems to help companies automate Accounts Receivable and Treasury processes. The HighRadius® Integrated Receivables platform reduces cycle times in your order-to-cash process through automation of receivables and payments processes across credit, electronic billing and payment processing, cash application, deductions, and collections. HighRadius® Treasury Management Applications help teams achieve touchless cash management, accurate cash forecasting and seamless bank reconciliation.
Powered by the Rivana™ Artificial Intelligence Engine and Freeda™ Digital Assistant for order-to-cash teams, HighRadius enables teams to leverage machine learning to predict future outcomes and automate routine labor-intensive tasks. The radiusOne™ B2B payment network allows suppliers to digitally connect with buyers, closing the loop from supplier receivable processes to buyer payable processes.
About Treasury Solutions
Westlake 4 Building (BP Campus)200 Westlake Park Blvd.8th floor, Houston, TX 77079(281) 968-4473
Corporate Headquarters
www.highradius.com
8Copyright © HighRadius Corporation
The HighRadiusTM Treasury Management Applications are a suite of the world's first AI-powered solutions designed to support treasury teams across all industries by automating and enhancing their cash forecasting, cash management and bank reconciliation processes.
The HighRadiusTM Treasury Management Applications are uniquely powered by an Artificial Intelligence technology created exclusively to redefine the forecasting, bank reconciliation and cash management processes, so that treasurers spend less manual effort but extract better outcomes such as making more accurate cash forecasts across all cash flow categories, increased forecasting frequency and variance reporting, gaining instant visibility into real-time cash positions across bank accounts at any level and achieving 99%+ straight through reconciliation of bank statements without human intervention.
The solutions are delivered via cloud which enables them to be seamlessly integrated with multiple systems including your ERP, TMS, accounting systems and banks instantly and simultaneously.
