Cofounder and CEO of Kolleno—a global, B2B SaaS accounts receivable and collections management platform.
Humans have always needed to support decision-making and strategy with some form of predictive analytics. In Ancient Rome, for example, predictive analytics meant haruspex priests studying animal entrails to foretell the Gods’ will before going to war or making political decisions. They relied on this auspicious or ominous data to move forward or change their plans.
Today’s business leaders are not so different from Roman military leaders—except that the financial data they rely on to support their decisions is now digital, more practical and a bit more reliable. When used appropriately, this precious data gathered by accounting and finance departments is gradually becoming businesses’ secret weapon to maximize efficiency, growth and competitiveness.
What is predictive analytics, and how can the office of the CFO succeed with it?
Predictive Analytics Versus Manual Data Processing
Accounts receivable management still relies on outdated methods like manual tracking and dunning, which is a reactive approach that can hinder a business’s growth. According to a recent PYMNTS’ study, only 17% of small firms are automating accounts receivables.
Here are a few of the downsides of manual data processing in accounting:
1. Late Payments And Cash Flow Issues: Increasing outstanding payment results can result in serious cash flow challenges for businesses, preventing them from meeting financial obligations and investing in growth opportunities. The Federation of Small Businesses (FSB) released a report, “Time to Act: The Economic Impact Of Poor Payment Practice,” showing late payments’ devastating effects, with 37% of businesses (download required) running into cash flow difficulties, 30% being forced to use an overdraft and 20% seeing their profits impacted.
2. Customer Dissatisfaction: Unpersonal, standard collection practices with cold payment reminders and repeated collection calls can strain relationships. A credit controller might offer precious data-driven insights, but small businesses might not afford the luxury of hiring a dedicated staff member.
3. Inefficient Resource Allocation: Expenses related to dunning can build up, and that’s why the collection must be tailored to your customers’ portfolios and based on historical data and predictive analytics. If you allocate resources to chase all your customers blindly, you might jeopardize your relationships and waste money chasing low-risk or already-paying customers.
The amount of information to be processed daily is so considerable that data analyst teams would have to work full-time to leverage them appropriately. However, artificial intelligence—with big data mining, statistics, modeling and machine learning—can seamlessly go through these colossal volumes of records and predict future phenomena like customer payment behaviors, credit risk or cash flow forecasts.
Data: A Secret Weapon To Unleash Your Business’ Potential
The emergence of predictive analytics revolutionizes accounts receivable management by enabling businesses to gain unprecedented insights into their strategy and finances.
1. Tapping Into New Data Sources. Historically, companies have essentially relied on internal data, such as payment history and customer information, using data mostly to react to arising challenges. AI can enable companies to leverage external data sources—such as economic trends, industry data and customer credit scores—to predict and anticipate future outcomes and act accordingly.
2. Optimized Customer Risk Assessment And Credit Control. Thanks to a customer’s payment history, companies can anticipate non-payment risk and implement corrective measures. Credit-related decisions also become more relevant, as predictive analytics allows businesses to better assess the creditworthiness of new and existing customers. By analyzing patterns and historical data, companies can set appropriate credit limits and terms, reducing the risk of late or non-payments.
3. Nipping Late Payments In The Bud. By spotting early warning signals, such as changes in payment patterns or financial difficulties, companies can take preventive actions, such as offering flexible payment plans or reaching out to customers before payment issues escalate.
4. Mindful Collection Strategy. Predictive analytics enhances collection strategies’ quality, enabling businesses to prioritize collection efforts based on customer risk profiles. Instead of a general, standard approach, which can be expansive, a bespoke approach can help ensure valuable resources are directed towards higher-risk accounts, improving collection rates and overall efficiency.
Investing In The Future—One Step At A Time
Implementing predictive analytics into your accounting can be intimidating. However, there are actionable steps you can take today to enable the process and have a better chance of success.
1. Save and integrate data. It might seem obvious, but saving your data is essential. It means discarding your Excel tabs and investing in accounting software and a data warehouse. Once you have the right data available, you should integrate your internal data sources with a data warehouse to create a comprehensive dataset that can then be leveraged to effectively inform your predictive models.
2. Build the right team to develop predictive models. Implementing predictive analytics requires a team with the proper skill set, including data analysts, data scientists and domain experts in accounts receivable and credit management, ideally. This team should develop predictive models tailored to your business needs. These models should consider variables like payment history, customer credit scores, economic trends and industry-specific factors.
3. Monitor and implement. Predictive analytics is an iterative process that requires continuously monitoring your models’ performance, gathering feedback from your team and refining the models to improve accuracy over time. Then, it’s time to implement your insights into your accounts receivable management. Your team should use this data to take proactive actions based on the predictions and to tailor their collection process.
Making financial services more efficient through predictive analytics can give your business a competitive edge that will unlock its potential. Forward-looking companies have already started leveraging AI to transform accounting and finance functions.
By searching historical data to identify patterns, predictive analytics tools can help mitigate late payment risks, forecast accurate cash flows and improve decision-making related to credit. That said, the process requires an iterative approach and the right team in order to see the best success from this new technology.