Skip to main content
Home
The Online Resource for Modern FP&A Professionals
Please register to receive the latest FP&A news, updates and tips Login

Main menu

  • Home
  • FP&A Insights
    • FP&A Trends Digest
    • FP&A Trends Research
    • FP&A Trends Insight Paper
    • FP&A Trends Survey
    • Short Videos
    • Our Contributors
  • FP&A Events
    • International FP&A Board
    • FP&A Trends Webinars
    • Digital FP&A Circles
  • AI/ML Committee
    • Introduction
    • Members
    • Resources
    • Meetings
  • FP&A Tools
    • FP&A Trends Maturity Model
  • About Us
    • Company Policy
    • Privacy Policy
    • Editorial Guidelines
    • Our Ambassadors
    • Our Sponsors & Partners
    • Contact Us
image
How to use AI to forecast Account Receivables
November 22, 2021

By Ashok Manthena, Founder of ChatFin

FP&A Tags
AI/ML FP&A

AI and MLThis is the second in my series “How to get started with Machine learning in Finance”. This blog explains how FP&A can utilise Artificial Intelligence (AI) to make a data-driven forecast of Account Receivables by examining historical invoices and payment data.

Account Receivables play a critical part in a company's financial stability. The majority of B2B enterprises offer credit to their customers, allowing them to pay in 30/60/90 days. Despite it, companies fall into serious Cash Flow problems and may even go bankrupt as a result of poor AR management.

That is why every CFO desires to effectively handle their Accounts Receivables. Overdue invoices are followed up on by collection managers in AR to convince consumers to pay. These collection managers can use various tools to assist them to prioritise which invoices, customers to focus on and also help detect the unusual customer behaviour.  

fp&a

In some organisations, FP&A need Cash Flow predictions for the coming month, quarter, or two in order to report on their predicted financial statements. Typically, they speak with AR teams to gain a sense of how the Cash Flow from various outstanding invoices will be. This necessitates a great deal of communication and leaves the possibility for human error.

This is where advanced prediction techniques, such as Machine Learning, may be extremely useful. Credit history, prior customer behaviour, the economy, customer industry expansion/contractions, and other factors can all be used to forecast AR flow using these strategies. Machine Learning may appear to be a difficult undertaking, but it may be utilised to improve the accuracy and speed of FP&A operations with the correct resources.

Let's look at how FP&A teams can use advanced prediction techniques like Machine Learning to estimate Cash Flow using AR data from ERP and then report on it in their financial statements. It comprises 3 steps:

  1. Prepare Input data
  2. Data cleaning & Processing
  3. Apply Machine Learning models


1. Preparing Input data

Prepare a dataset with historical invoices with customer information and payment dates etc. . Finance ERPs have AR invoices with the following attributes:

  1. Invoice amount
  2. Customer 
  3. Payment terms: 30/60/90
  4. Overdue days etc
  5. Payment date
  6. Credit history
  7. Credit score etc.

2. Data cleaning & processing

You've probably heard the phrase "garbage in, garbage out" when it comes to data. We must ensure that the data we extract from ERPs is free of number format errors, missing values, and outliers and that the data is normalised. Missing values and outliers can be dealt with in a variety of ways, which are outside the scope of this article. After the data has been cleaned, we must divide it into two sections. This will assist us in determining which model is most appropriate for our data set. We'll use the Train data set to make predictions and compare the results to the test data set. We can choose the optimum model and parameters for predicting future periods by minimising the error rate. 

Splitting Data into Train and Test:

  • Train dataset with historical invoices will be processed to identify patterns in the input data, 
  • Train dataset should contain historical invoices for the last 36-48 months with all the attributes mentioned above. 
  • Test data set will contain active invoices with all the attributes above, except the payment date. (Since invoices are not paid yet and need to be predicted)

3. ML models

ML models examine patterns in the test dataset and use those patterns to determine the due date for invoices in the test data set. Model output can then be aggregated to months/quarters to provide Cash Flow for the following month/quarter. Gradient boosting models like XGboost and Light GBM operate well with invoices and payment data, according to Tadaa.ai research.

You can begin experimenting by training and testing the models if you are familiar with Python. However, if you want to create an automated pipeline and put it into production, you'll need:

  • To extract data for financial ERP, you'll need an ETL engineer.
  • To train and test diverse models, a data scientist is needed.
  • To productionalise, you’ll need a Machine Learning engineer.

There are also AI products in the market that will let you execute quickly without the need for additional resources. Automating Cash Flow forecasting allows FP&A teams to focus on creating business insights rather than the tedious task of business execution. 

As we start seeing more AI applications in corporate finance, finance teams need to be equipped with the fundamentals of data science and Machine Learning. Similarly, the incentives need to be aligned to increase efficiency in finance teams.
 

The full text is available for registered users. Please register to view the rest of the article.
  • Log In
  • or
  • Register

jaintalk

December 2, 2021

Well said Ashok Manthena .

You have touched the right chord. My experience of Finance-Order to Cash process improvement too echoes it.

Machine Learning is a blessing for O2C process, especially Accounts Receivables. A successful example is Iron Mountain. The company was able to reduce collection time by 25%-45%.
  • Log in or register to post comments

Related articles

fp&a
Combining Human and Artificial Intelligence in FP&A: Roche Case Study
June 25, 2021

Today, the tools that are being offered are smart enough that with a few inputs and financial...

Read more
Artificial Intelligence
Making Artificial Intelligence Real for FP&A Professionals
May 5, 2021

In today’s world, companies need more than ever fast insights to make the appropriate critical moves...

Read more
 Decision Making
Artificial Intelligence and You in Decision Making
December 2, 2020

Computer simulations allow us to play out various scenarios repeatedly and assess the outcomes. It is...

Read more
Predictive analytics technology
Utilising Modern Technology for Predictive Planning
July 27, 2021

Analysis is only as good as the decisions that result from it. In the uncertain business...

Read more
+

Subscribe to
FP&A Trends Digest

We will regularly update you on the latest trends and developments in FP&A. Take the opportunity to have articles written by finance thought leaders delivered directly to your inbox; watch compelling webinars; connect with like-minded professionals; and become a part of our global community.

Create new account

image

Event Calendar

Pagination

  • Previous
  • May 2025
  • Next
Su Mo Tu We Th Fr Sa
27
28
29
30
1
2
3
 
 
 
 
4
5
6
7
8
9
10
11
12
13
14
15
16
17
Transforming FP&A Together: Human & AI Synergy
 
18
19
20
21
22
23
24
Moving from FP&A to Extended Planning and Analysis (xP&A)
 
Five Critical Roles for Building a World-Class FP&A Team
 
25
26
27
28
29
30
31
FP&A Business Partnering and AI: A New Era
 
All events for the year

Future Meetings

The Face-to-Face Amsterdam FP&A Board
The Face-to-Face Amsterdam FP&A Board Transforming FP&A Together: Human & AI Synergy

May 15, 2025

The Face-to-Face Milan FP&A Board
The Face-to-Face Milan FP&A Board Moving from FP&A to Extended Planning and Analysis (xP&A)

May 20, 2025

The Face-to-Face Frankfurt FP&A Board
The Face-to-Face Frankfurt FP&A Board Five Critical Roles for Building a World-Class FP&A Team

May 22, 2025

BPAI
The FP&A Trends Webinar FP&A Business Partnering and AI: A New Era

May 28, 2025

The Face-to-Face London FP&A Board: Data Management & Analytics: Unlocking FP&A Value
The Face-to-Face London FP&A Board Mastering Data in FP&A: Smarter Analytics, Better Decisions

June 5, 2025

FP&A Trends Webinar The Evolving Role of FP&A: From Number Cruncher to Strategic Advisor
The FP&A Trends Webinar Making FP&A Teams Fit for the Future

June 11, 2025

The Face-to-Face New York FP&A Board
The Face-to-Face New York FP&A Board From Insight to Impact: FP&A Business Partnering in Action

June 17, 2025

FP&A Trends Webinar Practical Steps for FP&A Analytical Transformation.
The FP&A Trends Webinar Unlocking FP&A Analytical Transformation

June 18, 2025

The Face-to-Face Sydney FP&A Board
The Face-to-Face Sydney FP&A Board Modern Financial Planning and Analysis (FP&A): Latest Trends and Developments

June 26, 2025

The Face-to-Face Singapore FP&A Board: Modern Financial Planning and Analysis (FP&A): Latest Trends and Developments
The Face-to-Face Singapore FP&A Board Modern Financial Planning and Analysis (FP&A): Latest Trends and Developments

July 8, 2025

AI/ML FP&A
AI/ML FP&A
Data and Analytics
Data & Analytics
FP&A Case Studies
FP&A Case Studies
FP&A Research
FP&A Research
General
General
Integrated FP&A
Integrated FP&A
People and Culture
People and Culture
Process
Process
Technology
Technology

Please register to receive the latest FP&A news, updates and tips.

info@fpa-trends.com​

              

Foot menu

  • FP&A Insights
  • FP&A Board
  • FP&A Videos

Footer countries

  • Amsterdam
  • Austin
  • Boston
  • Brisbane
  • Brussels
  • Chicago
  • Copenhagen
  • Dubai
  • Frankfurt
  • Geneva
  • Helsinki
  • Hong Kong
  • Houston
  • Kuala Lumpur
  • London Board
  • London (Circle)
  • Melbourne
  • Miami
  • Milan
  • Munich
  • New York
  • Paris
  • Perth
  • Riyadh
  • San Francisco
  • Seattle
  • Shanghai
  • Singapore
  • Stockholm
  • Sydney
  • Tokyo
  • Toronto
  • Washington D.C.
  • Zurich

Copyright © 2025 fpa-trends.com. All rights reserved.

0