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Predictive Analytics in Finance: Use Cases and Next Steps

What you’ll learn

  • Advanced analytics help predict future outcomes by analyzing past data to identify patterns and trends
  • Predictive analytics help businesses plan better to meet uncertainties and minimize risk
  • Utilizing predictive analytics correctly is as important as implementing the technology

Finance leaders are experimenting with advanced data analytics solutions to power their reporting, budgeting, cash flow management, and other functions. The exponential growth of data can be seen as a boon or bane, depending on how you secure the data and use it. CFOs who mine data and generate insights will have an advantage over those who lag in adopting advanced analytics and digital transformation. You’ll be able to predict future outcomes with better accuracy. Businesses using predictive analytics models will also have a competitive advantage in mergers and acquisitions (M&As), market expansion, and liquidity management. In this article, we look at how CFOs and finance teams use predictive analytics to their advantage. We also offer tips on how to start adopting advanced data analytics.

What is predictive analytics?

Predictive analytics is a branch of data analytics that uses techniques like big data mining, statistics, modeling, machine learning, and artificial intelligence to analyze data and make predictions.

Predictive analytics tools comb through large volumes of data to identify patterns and trends using regression techniques, pattern analysis, and other statistical methods. Technologies such as machine learning, neural networks, and cognitive computing further improve the speed and accuracy of predictive models.

Predictive analytics solutions help you forecast cash flows accurately and plan your investments and expenditure better. It helps mitigate financial risks and maintain good customer relationships.

For example, in accounts receivable management, predictive analytics helps to identify customer payment patterns, credit risk, and payment default chances. More advanced predictive analytics algorithms will even be able to predict the day or date when a customer can be expected to pay.

Working of Predictive Analytics Models

How predictive analytics models work(Source)

Predictive analytics use cases in finance

Let’s look at some use cases of predictive analytics in finance that help reduce the uncertainty and unpredictability of business operations.

Revenue and cash flow forecasting

Cash flow forecasting models driven by predictive analytics help you gain better visibility into your cash inflows and outflows. Based on your invoice data, past trends of payments, cash position, and other factors, the algorithm helps predict the timing of your cash inflows and outflows.

This helps better plan your investments and segment customers based on how likely they’re to pay. Advanced models for predicting cash flows analyze trends in historical data using statistical computations that help categorize events (e.g. public holidays), identify influencing variables (e.g. currency exchange rates), and ignore outliers, to arrive at accurate forecasts.

Make customer payment predictions

Will the customer pay? Or won’t they? Will they need coercion to pay? There’s always an element of uncertainty about payments when you sell on credit. Predictive analytics algorithms use a variety of input data such as customers’ past payment trends, current financial strength, market conditions, etc. to predict whether the customer will pay on time, make part payments or short payments, or need coercion to pay after the due date.

Payment predictions help your collectors prioritize accounts and customize customer interactions based on their probability of paying. This helps your collectors avoid overtly spending time and effort on customers who are likely to pay.

Customer payment prediction using HighRadius' solution

Customer payment prediction using HighRadius’ solution(Source)
HighRadius’ AI-based payment date prediction feature tracks past payment trends, prioritizes accounts, and suggests actions to take

Fraud detection and risk management

Minimizing financial risks that the business may face is one of the key priorities for CFOs. Capital investments, investments in money markets, technology spending, selling on credit – everything involves risk, and minimizing it is essential to ensure that the business doesn’t suffer any unforeseen losses.

Predictive tools pick up minute differences in transaction data and help predict and identify fraud. It also helps predict risks associated with different tasks and classifies them according to the impact on the business.

Credit risk management

Our credit risk management app helps score customers and identify the level of risk each time a sale is made on credit. It uses a variety of information sources including credit reports and market data to minimize payment risks. The AI-powered engine also helps to predict blocked orders based on customer payment history and credit limit utilization. Book a demo to see how it works.

Budgeting and resource allocation

Finance teams also spend a great deal of time and effort in budgeting, planning, and resource allocations. You are the final approvers of funds and budgets. You need to ensure that you are neither overspending nor underspending.

Predictive analytics technology processes data from multiple sources, identifies patterns and trends, and predicts if the budget will likely deliver the desired return on investment (ROI). The model uses historical data to identify recurring patterns and trends and suggests the best possible ways to allocate resources.

Accounts receivable analytics for working capital management

Accounts receivable management is a key finance function. The AR team is responsible for collecting payments from customers, closing the invoices, and matching the books.

Accounts receivable analytics provides timely insights into risks and receivables that may constrain your working capital. Dashboard features offered by AR analytics solutions provide a snapshot view of your aging accounts, percentage overdue, and days sales outstanding (DSO). It classifies accounts into various buckets and can predict how much working capital will be available.

Receivables analytics solution by HighRadius

Receivables analytics solution offered by HighRadius(Source)

Mobile analytics

Getting real-time updates on the go is crucial for financial leaders who are away from their desks for a major part of their time to meet business commitments. RadiusOne Mobile Analytics solution for mid-market CFOs helps finance executives stay on top of key metrics such as bad debt write-offs and DSO and manage working capital from any location. Check out RadiusOne and its analytics capabilities to know more.

Next steps: How to power your team with predictive analytics?

Your organization needs to prepare itself with some structural and cultural changes if it wants to realize the full potential of predictive analytics. Here’re some steps that you need to take:

Eliminate data silos

All processes—predictive analytics, machine learning, and AI—need accurate data to provide the desired results. Finance leaders must have access to operational data to implement predictive analytics technologies. This helps align financial plans with operational plans and improve forecast accuracy. Ensuring the availability and accessibility of data to the right functions is crucial for the success of predictive analytics.

Train employees on how to effectively use predictive analytics

Having solutions with predictive features alone will not help unless your employees are trained to use them effectively. Train your employees to help them understand the various scenarios in which predictive analytics helps and to identify what type of data helps get more accurate results.

Choose solutions that support predictive analytics features

Your finance team needs a variety of software tools such as AR automation software, reporting solutions, budgeting apps, and tax management solutions. Check with the vendors to see if their solutions offer predictive capabilities to forecast cash flows, risks, expenditure, taxes, etc. as needed.

Predictive analytics will be embedded in apps - Quote

Our company provides AR automation solutions that have built-in analytics capabilities. We provide insights on customers’ payment probabilities and suggest the next course of action that you should take on potentially risky customers.
Schedule a demo with us to see how AR solutions help ease your teams’ job and allow them to target SMART goals.

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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.