By Christopher Iervolino, Research Director at Gartner

Artificial intelligence (AI) technology applications are setting the stage for greater competitiveness among vendors in the financial services industry. We have seen in recent years how public cloud software as a service (SaaS) changed the way packaged applications are delivered, implemented and used, leading to a new generation of solutions and vendors in areas such as human capital management (HCM), procurement and financial management.

Although various cloud-based financial management applications are now available in the market,they haven’t fundamentally changed the way finance processes work. There is also limited functional differentiation between applications within each product category.

All this is set to change as AI is introduced into financial management applications. By 2020, embedded AI will become a key differentiating factor in finance systems evaluations, and vendors with this capability will be able to highlight greater functional advantages.

Yet, AI is still at an early adoption stage, so application leaders need to focus on how and when AI will deliver business value, rather than get carried away by vendor hype.

Vendors currently use both generic and embedded AI applications

Many vendors announced their intention to add AI capabilities to their financial management applications and some have released initial versions of these capabilities.  The majority of these were generic AI applications in the form of bots, chatbots and virtual assistants, primarily for casual users who may find it difficult to navigate financial management applications and need an easier-to-use “self-service” experience. However, virtual assistants serve little purpose to users who are already familiar with finance applications. Consequently, deploying AI in the form of virtual assistants may not offer enough value to justify significant time and money investments.

Vendors that have a clear vision for embedding AI technologies in finance processes will be a better fit for financial organisations than those that don’t offer anything beyond the virtual assistant phase.

AI will help with efficiency and speed

Enterprise resource planning (ERP) and financial management applications have achieved significant transaction-processing efficiencies by automating routine finance processes and eliminating the need for manual intervention. Nevertheless, finance processes still need experienced and skilled personnel to deal with exceptions or special cases.

This is where embedding AI technologies — such as machine learning, deep learning and algorithm-based machine reasoning — directly into financial management applications will be transformational.

Most financial management applications can match incoming payments to outstanding accounts receivable (AR) invoices, provided the payment amount matches the invoice. However, incomplete remittance data, partial payments and payment of multiple invoices on a single remittance can all cause discrepancies that take time and effort to resolve. Embedding AI technologies in financial applications can address these challenges by modelling combinations of payments and invoices in different situationsand learning which algorithms work best in different situations.

Predict future financial results with AI

The ability of AI to improve predictive and prescriptive financial forecasting processes will change the world of finance management. Currently, many financial processes are manually intensive and suffer from inherent human biases, as predictive models may be “tweaked” to generate favourable or expected outcomes

Cash-flow forecasting, revenue forecasting, cost and expense planning, and balance-sheet planning are all areas of predictive financial analytics that will benefit from AI technologies, although the impact will vary by each business and industry. For example, a product-centric business with a wide range of products would benefit from moreeffective revenue forecasting. A large multinational organisation in a competitive, low-margin industry would benefit from more effective cash-flow forecasting, whereas the impact would be lower in a smaller organisation in a high-margin industry.Although initial use cases are likely to augment human decision making, especially using massively complex and diverse internal and external data sets, the potential value of this AI analytics augmentation should not be underestimated.

Businesses should consider being early adopters or partnering with vendors focused on use cases specific to their company and industry.