AI opportunities in finance

AI can analyze vast quantities of historical data to identify patterns that traditional cash-flow forecasting software can’t. This can accurately predict cash flows across categories, entities, and time horizons, providing a solution to one of the most challenging elements of a treasurer’s job.

For any organization managing projects, contractors or long-term engagements, AI can also be used to enhance percentage of completion (POC) forecasting by leveraging data-driven insights. Machine learning models can analyse a spectrum of POC metrics (hours, units, cost etc.) to predict POC revenue and estimate total completion effort remaining in real time.

AI can automatically detect anomalies in transactions or balances that violate any organization-specific accounting policies or principles. Real-time analysis of data entry can catch these errors quickly, alerting relevant individuals to double-check before the errors are committed to record. This allows organizations to avoid costly corrections later in the process, maintain more accurate financial records, and ultimately create more trust in the data used to track progress or make decisions.

Machine learning models can understand customers’ invoice paying patterns and trigger proactive action to make collections before payments pass their due date. AI can take this to the next level by personalizing customer messaging, leveraging behavioural science findings to increase collection success rate. Automating collections for lower risk or value accounts also benefits the organization by freeing up human capacity to focus on the highest risk or value collections.

Machine learning algorithms can predict the outcome of different scenarios when alternative data values are used. Using models based on hypothetical data sets can predict the results of different decisions, such as deciding whether to fund a project or make changes to supplier payment terms, helping the organization to make informed decisions.

While we have already touched on the established AI process automation used in invoice processing (e.g. three-way matching), there are a host of other finance processes that can benefit similarly. AI can handle repetitive and time-consuming data entry tasks, and by using technology like optical character recognition, can directly extract information from images of receipts and invoices to automate expense handling.

AI can extract structured and unstructured data from a host of different document types and data sources to automatically produce key financial reports and generate insights. This significantly reduces the time spent on manual data collation and can allow data owners to generate reports as and when needed rather than relying on weekly or monthly reporting cycles. The same approach can be applied to financial audits, where AI can quickly summarize audit information and provide relevant insights. This frees auditors from otherwise time-consuming tasks, enabling them to focus more on key risk areas where they can directly add value.

Proving the quality of your organization’s financial controls is a time-intensive exercise that can place a significant burden on your finance teams. More and more organizations are starting to deploy AI tooling to assist with this, either by optimizing the testing of internal controls or by performing routine checks. Microsoft has been using machine learning algorithms to automate significant aspects of its SOX control auditing for several years, with a claimed time saving of 20,000 hours per year and a significant corresponding cost reduction.

AI can reduce the amount of time finance teams spend on routine tasks which could easily be automated, such as invoice and payment processing, or creation of financial statements. This frees up capacity to focus on value-add activities which AI may not be suited to e.g. subjective decision making.

AI algorithms can analyze huge quantities of data in real-time, detecting anomalies that may indicate fraudulent activities or highlight risk areas within the organization. While we’ve already discussed how AI can be used to catch genuine human errors in finance processes, it can also help detect malicious activity before lasting damage is done. When risk areas have been identified, AI can also be used to prescribe remediation activities, building an organization’s ability to deal with similar issues in future.

Analyzing data and insights in real-time allows organizations to make more well-informed decisions. When combined with the use of AI to generate synthetic data sets, organizations can access more data and analyse it faster than ever before.

AI can be applied in numerous ways to optimize working capital, including aiding organizations in making decisions around managing short-term liabilities or by improving accounts receivable and accounts payable management to predict payment cycles and optimize credit terms. Machine learning can also be used to analyze historical data and market trends to optimize inventory levels, ensuring that working capital is not tied up unnecessarily as a result of overstocking.

While many enterprise resource planning (ERP) platforms are rapidly integrating AI into their technology offerings, standalone options are also available. These should be explored when considering cost versus benefit. It may be worthwhile trialling AI solutions for different use cases and checking which AI features are available in your existing technology platforms to determine which add real value before deciding to invest heavily in long-term solutions.