Key Takeaways
- Predictive analytics helps finance professionals accurately forecast future outcomes and gain a competitive edge across several areas.
- Predictive analytics has many use cases such as cash flow forecasting, customer payment predictions, and credit risk management, among others.
- AR automation solutions with built-in analytics capabilities can help finance teams leverage the power of predictive analytics.
Introduction
As the volume of financial data continues to grow, finance leaders are turning to advanced data analytics solutions to drive their reporting, budgeting, cash flow management, and other functions. However, the effective utilization of this data can be a double-edged sword, requiring careful management and protection.
CFOs and finance teams who embrace predictive analytics and digital transformation will have a distinct advantage over those who lag behind. By leveraging predictive analytics models in finance, businesses can accurately forecast future outcomes and gain a competitive edge in areas such as M&As, market expansion, and liquidity management.
In this article, we explore the many ways in which predictive analytics can be used to benefit finance professionals. We also provide practical tips for those looking to adopt advanced data analytics practices to stay ahead of the curve.
What is Predictive Analytics in Finance?
Predictive analytics is a powerful data analytics technique that uses big data mining, statistics, modeling, machine learning, and artificial intelligence to analyze large volumes of data and make predictions about future events or behaviors.
Predictive analytics tools comb through large volumes of data to identify patterns and trends using regression techniques, pattern analysis, and other statistical methods. By identifying patterns and trends in historical data, predictive analytics can help finance professionals to forecast cash flows, manage risks, and make informed investment decisions.
For example, in accounts receivable management, predictive analytics helps to identify customer payment patterns, credit risk, and payment default chances. More advanced financial predictive analytics algorithms will even be able to predict the day or date when a customer can be expected to pay.
Predictive Analytics Use Cases in Finance
Predictive analytics has numerous use cases in finance that can help reduce uncertainty and improve the predictability of business operations. Here are a few examples of predictive analytics in finance:
1. Revenue and Cash Flow Forecasting
Cash flow forecasting models driven by predictive analytics help finance teams gain better visibility into their cash inflows and outflows by analyzing invoice data, past payment trends, cash position, and other factors. By predicting the timing of cash inflows and outflows, finance professionals can better plan their investments, segment customers based on their likelihood to pay, and optimize their cash flow.
2. Customer Payment Predictions
Predictive analytics algorithms can help finance professionals predict whether customers will pay on time, make partial or short payments, or require coercion to pay after the due date. By analyzing factors such as past payment trends, financial strength, and market conditions, predictive analytics models can help collectors prioritize accounts and customize customer interactions based on their probability of paying. This can help finance teams avoid spending excessive time and effort on customers who are less likely to pay.
HighRadius’ AI-based payment date prediction feature tracks past payment trends, prioritizes accounts, and suggests the necessary actions to be taken.
3. Fraud Detection and Risk Management
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.
4. Credit Risk Management
Our credit risk management software 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.
5. Budgeting and Resource Allocation
Predictive analytics technology can help finance teams identify patterns and trends in data from multiple sources, and predict whether budget allocations are likely to deliver the desired ROI. By analyzing historical data, predictive analytics can suggest the best possible ways to allocate resources and avoid overspending or underspending.
6. Accounts Receivable Analytics for Working Capital Management
Predictive analytics in accounts receivable provides timely insights into risks and receivables that may constrain your working capital. Dashboard features offered by HighRadius 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 offered by HighRadius
7. Mobile Analytics
For finance leaders who are frequently away from their desks, mobile analytics can be a powerful tool. Solutions like RadiusOne AR Suite can help finance executives stay on top of key metrics such as bad debt write-offs and DSO, and manage working capital from any location.
By leveraging the power of predictive analytics, finance professionals can gain insights into their data, reduce risk, and improve operational efficiency across a wide range of functions.
Next Steps: How to Empower Your Team with Predictive Analytics?
To fully realize the potential of predictive analytics, organizations must make structural and cultural changes. Here are some steps that finance leaders can take to empower their teams with predictive analytics:
1. Eliminate Data Silos
To implement predictive analytics technologies, finance leaders must have access to accurate data. 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.
2. 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.
3. Choose Solutions That Support Predictive Analytics Features
Finance teams need a variety of software tools such as AR automation software, reporting solutions, budgeting apps, and tax management solutions. It’s important to choose solutions that offer predictive capabilities to forecast cash flows, risks, expenditure, taxes, etc. as needed.
At HighRadius, we provide AR automation solutions with built-in analytics capabilities. Our solutions offer insights on customers’ payment probabilities and suggest the next course of action for potentially risky customers. By leveraging our AR automation solutions, finance teams can ease their workload and focus on achieving SMART goals.
To see how our AR solutions can help empower your finance team with predictive analytics, schedule a demo with us today.
FAQs on Predictive Analytics in Finance:
1. What is predictive analytics in financial risk management?
Predictive analytics in financial risk management is the use of statistical algorithms, AI, and machine learning techniques to analyze data and make predictions about future financial risks. Predictive analytics can identify potential risks. This can help organizations make data-driven decisions to mitigate financial risks and protect their bottom line.
2. How do companies conduct predictive analysis?
Predictive analysis involves collecting and preprocessing the data, selecting appropriate modeling techniques, training the models, and evaluating their accuracy. This can be conducted using a variety of software tools and platforms, depending on the specific needs and goals of the organization.
