Where does AI fit into Finance?
Artificial Intelligence is quickly moving from the experimental to the operational. It is already being driven deep into the operating muscle of the enterprise – from predicting how competitors will react, to identifying which machines will need repair, and even forecasting our best customers five years from now.
The CFO is in a unique position to bring AI to the enterprise. He or she sits at a confluence of the most important data of the firm – operating costs from production, receivables from the customer, financial performance from the business units – that serve as the digital foundation of AI.
Many CFOs effectively serve as the Chief Compliance Officer, often taking personal responsibility for adherence to financial and regulatory requirements. The finance function includes a wide range of repetitive and routine tasks – functions that are ripe for disruption by AI. Finally, the strategic CFO is increasingly asked to support the enterprise in its growth plans and play a direct role in building revenues.
The expectations of shareholders, regulators and audit committees have been increasing, specifically in the areas of financial and nonfinancial reporting, transparency, and governance. All of these functions can benefit from AI.
The CFO is in a unique position to bring AI to the enterprise.
1. Customer data and predictive pricing
In many firms, the CFO has responsibility for the reporting layer of data that comes in from customer-facing operations – for example, receivables passed from the sales organization or pricing data from point-of-sale units. In today’s world of digital commerce, this puts the CFO in a powerful position to link predictive analytics with customer behaviour.
2. Looking beyond the book value
One of the greatest challenges for a CFO can be in assessing the true value of assets. Uncertainties in valuation, whether in acquiring an asset-intensive company or determining the tax basis of current assets, can add or subtract Euros from the bottom line.
The most effective method for determining asset values is an assessment of a large number of comparable independent transactions. This is where AI can help.
CFOs and valuation specialists in the real estate business can use AI to assess thousands of housing variables – mortgage rates, quality of schools, number of bedrooms, local employment – to build predictive models of home asset prices. These can be for acquisition or for sales by the enterprise. This not only helps the real estate firm, but also provides value to buyers, sellers and lenders.
3.Predicting deadbeats: forecasting and management of bad debt
Artificial intelligence puts the CFO in a position to predict which customers will pay, be late in paying or will not pay at all. A multivariate analysis of B2B customer data can provide a forecast of the probability that a business will pay its bills – and should be extended credit. Alternatively, the identification of likely non-payers helps in customer qualification and credit approvals.
4. Fraud
Internal and Expense fraud is particularly hard to detect, predict and control. It is episodic – not leaving a clear data trail. It is often executed in small increments that escape detection. Finally, the perpetrator may intentionally distort the data trail to prevent detection.
With AI you can analyze and interpret expense data and detect suspicious expense claims. You can explore spending patterns and employee behaviours in various roles. Also, machine learning technology can identify and predict common behaviors of employees who falsify or exaggerate claims. This makes it possible for a CFO to forecast potential expense fraud before it happens.
5. The detection of money laundering
Given the seriousness of money-laundering offenses and the possibility of strong regulatory sanctions, many banks have installed alert mechanisms based on known patterns of abuse. Many of these alert programs are mandated by law. The problem is that these systems can generate a flood of alerts and leave the CFO’s team wondering where to put scarce resources.
Artificial intelligence can teach computers to recognize suspicious behaviour and to classify alerts as high, medium or lower risk. Applying rules to these alert classifications can facilitate the automatic closing of false alerts, freeing up staff to focus on the small minority that have a strong possibility of an illicit transaction.
6.Taking the drudgery out of finance
Perhaps no part of the enterprise has as many repetitive, routine tasks as the finance department. Inputting invoices, tracking receivables and logging payment transactions are high cost, low return, and not of high interest to employees.
Artificial intelligence, combined with robotic process automation, has the power to disrupt the traditional finance back office. While the robotics speeds the transaction, the AI mines the data for insights for the front office: How do we speed collections? Where should we invest? What will be the impact if we decrease our prices?
CFOs are increasingly using AI to address significant changes to accounting regulations. For example, we have seen large companies save significant manpower by using natural language processing (NLP) for the review of lease contracts. Without AI, this would have been a very labour-intensive task.
7.The power of prediction
Historically, the finance function has focused on documenting the past – recognizing revenues, auditing costs, or monitoring compliance. Artificial intelligence is transformative because it places the CFO in the future with the data-driven power of prediction. Suddenly Finance is empowered to forecast how competitors will react, how customers will respond, and where risks will emerge.
This transformation goes far beyond digital fortune-telling: it transforms the role of the CFO and the finance team, placing it in the strategic heart of the enterprise. The CFO who seizes the opportunities of artificial intelligence and machine learning will not just be transforming the enterprise, but also the scope, responsibilities, and power of the job itself.