Managing finances for a company in this day and age is not a task for the faint of heart. Proper data visualization and the ability to optimize processes could take a lot of time for people to develop. But living in the twenty-first century gives us the advantage of having automation backing our endeavors, which makes the procedure a lot easier to maneuver.
The main problem faced by most companies in the age of information is the sheer abundance of it. As contradictory as that sounds, processing mountains of data and converting that into a usable and curated form takes too much time and effort. Being able to churn out information at the same speed as the information flowing in might sound like a dream, but using the right technology and algorithms could help boost speed and efficiency in that matter.
“To be able to set the right course for the future, finance functions must get better at processing—and extracting forward-looking insights from—large amounts of data.”
-E&Y | The DNA of the CFO, 2016
Even by normal metrics, using technology in day to day data analytics gives huge returns in the processing time used overall. The first step for implementing a better analytics strategy is to introduce measures that promote the use of a single database. This could help with convenient tracking of information and enabling software to integrate into the database can further support it. However, constant improvement also plays a key part. Growing from just business intelligence to implementing machine learning into analytics turns that step into a leap, cutting down manual efforts and tasks exponentially. Because at the end of the day, being the best in the game, requires speed and accuracy. The point is that leveraging technology and machine learning algorithms in the field of finance and centralizing more processes like revenue forecasting could help improve effectiveness.
Automation here in this context refers to machine learning. But what is machine learning in itself? Machine learning is a clever program that helps us answer these four questions:-
1. Regression or How Much:- Deals with the logistics of quantity. Example; What will revenue be in Q3 in Latin America for product X?
This helps with revenue forecasting. Before ML’s introduction, the process was extremely manual, but now it can be used to get within a 2% forecast accuracy, which helps in decision making and uses less time.
2. Classification or What Category:- Deals with the logistics of probability. Example; What is the probability that a customer will churn to a competitive product?
This helps answer the question of whether a product requires change, and if so how will it be received in the market. The statistics provided could help make that call.
3. Clustering or In What Group:- Deals with curating and compiling data. Example; What bundles of products sell well together?
Machine learning helps in grouping and classification of information flowing in so that insightful data is easier to extract from it. It also highlights patterns and behaviors that it finds to be relevant.
4. Anomaly Detection:- The ability to find and handle exceptions. Example; Which customer is likely to default on its payment?
Compliance is a huge part of the finance business, thus having any issues overlooked could cause hassles in the future. ML finds these issues and highlights them to show that these are the exceptions that need to be taken care of.
“By 2020, 50% of all business analytics software will incorporate prescriptive analytics built on cognitive computing functionality.”
ML is used nowadays in many fields in the order to cash cycle, from financial analysis and reporting to risk management, with a variety of roles, from assessing the health of a company and revenue planning, to implementing blockchain in treasury management.
The incorporation of technology in financial strategy and forecasting has helped usher it into a new era of speed and accessibility. Saving hours of manual work and the ability to adapt to different needs make it ideal for this fast economy.
Enabling Faster Cash Conversion with A/R Automation…
Is Your Accounts Receivable Process Slowing You Down? In today’s digital economy, a…
Want zero bad debts? See how Yaskawa…
Most humans crave the zeroes when it comes to figures and calories. Wouldn’t…
Best Practices for Driving E-invoice Adoption
In today’s digital world, striving for the “perfect invoice” could lead a company…
HighRadius Integrated Receivables Software Platform is the world's only end-to-end accounts receivable software platform to lower DSO and bad-debt, automate cash posting, speed-up collections, and dispute resolution, and improve team productivity. It leverages RivanaTM Artificial Intelligence for Accounts Receivable to convert receivables faster and more effectively by using machine learning for accurate decision making across both credit and receivable processes and also enables suppliers to digitally connect with buyers via the radiusOneTM network, closing the loop from the supplier accounts receivable process to the buyer accounts payable process. Integrated Receivables have been divided into 6 distinct applications: Credit Software, EIPP Software, Cash Application Software, Deductions Software, Collections Software, and ERP Payment Gateway - covering the entire gamut of credit-to-cash.