How AI Will Transform Financial Management Applications

February 02, 2018

Contributor: Jordan Bryan

AI technology applications are setting the stage for greater competitiveness among vendors in the financial industry

Public cloud SaaS changed how organizations deliver, implement and use applications. The evolution led to a new generation of solutions and vendors in areas such as human capital management (HCM), procurement and financial management.

“ Embedded AI will become a key differentiating factor in finance systems evaluations”

Despite various cloud-based financial management applications availability in the market, they haven't fundamentally changed the way finance processes work. There is also little functional differentiation between these applications.

All this is set to change as artificial intelligence (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,” says Nigel Rayner, vice president at Gartner.

Because AI adoption is limited, focus on how and when AI will deliver business value instead of the by vendor hype.

Vendors will offer both generic and embedded AI applications

"Many vendors have added AI capabilities to their financial management applications," says Christopher Iervolino, research director at Gartner. "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."

Finanical organizations benefit from vendors that have a clear vision for embedding AI technologies in finance processes. This is more valuable than vendors who only offer the virtual assistant phase.

AI will improve transaction-processing efficiency and reduce period-end close time

Through the automation of rountine finance processes and the elimination the need for manual intervention, enterprise resource planning (ERP) and financial management applications achieve significant transaction-processing efficiencies. Exceptions or special cases may still require support from experienced and skilled personnel.

“ Many financial forecasting and planning processes are manually intensive and suffer from inherent human biases”

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

A potential use case for embedded AI illustrates this impact. 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 situations.

AI will predict future financial results based on trends and market data

The ability of AI to improve predictive (what will happen) and prescriptive (the best course of action) financial forecasting processes will change the world of finance management. Currently, many financial forecasting and planning processes are manually intensive and suffer from inherent human biases, as predictive models may be "tweaked" to generate favorable (or expected) outcomes.

AI technologies will benefit predictive financial analytics in areas such as cash-flow forecasting, revenue forecasting, cost and expense planning, and balance-sheet planning. The impact of the technology varies by organization and industry. For example, a product-centric organization with a wide range of products would benefit from more-effective revenue forecasting. A large multinational in a competitive, low-margin industry would benefit from more effective cash-flow forecasting, whereas the impact would be lower in a smaller organization in a high-margin industry.

Consider if early adoption or vendor partnership solves for the use cases specific to their company and industry.

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