What Makes A Finance Solution Intelligent?

Neil Krefsky

More than any other profession, the role of finance professionals has been defined by what technology will allow them to determine how they can add value and influence across an enterprise. What finance teams are able to do, how they are viewed within their stakeholder base, and where their strengths and weaknesses lie are very much dependent on that underlying technology capabilities (or limitations).

Instead of relying on computers for simple automation of rote administrative activities, new, more sophisticated software is being used to “cognify” – embedding the ability to “think” – into previously manual, time-consuming accounting and reporting processes.

There is a new reality for finance that allows professionals to determine how they work as opposed to being boxed in to a predefined role. These transformative and intelligent technologies like in-memory, machine learning, predictive analytics, cloud, and others are changing the way we work and the value finance is bringing to the organization. The impact is substantial. In a study from Oxford Economics, 73% of finance leaders agreed that automation is improving efficiency within their organization and throughout the company, freeing bandwidth for more strategic tasks.

At SAP, we understand that making our complete portfolio of financial solutions intelligent is key to enabling our customers to be the best-run businesses they can be. Let us explain what that looks like with some real-life examples.

Advanced automation and machine learning

Automating tasks no longer requires intensive human oversight. Intelligent finance applications can perform daily routines more efficiently than ever. New technology offers advanced automation, and machine learning handles critical, resource-intensive tasks more quickly and accurately than any human can. There are tremendous time savings associated with these features. Ventana Research estimates that one-third of the repetitive, rules-driven work performed by accounting and tax departments can be eliminated through machine learning, AI, and blockchain automation.

SAP is applying advanced automation and machine learning to the core financial functions that every finance team depends on. In one example, cash application software uses machine learning techniques to analyze historical data. The software uses this analysis to continuously seek out efficiencies, improve processes, and clear payments automatically. The more it is used, the smarter it gets – identifying mistakes, inputting errors, and other anomalies – before presenting just the exceptions for human action.

Prediction and analysis

Intelligent finance tools can scan colossal amounts of data and pull out specific information that illustrates trends and behavior patterns. They can bring together actuals, forecasts, and simulations to uncover market trends before they occur, thus increasing the accuracy of predictive scenarios. SAP benchmarking found that predictive analysis is guaranteed to produce a return on investment (ROI) – and this ROI compounds. Again, the more you use them, the greater the return.

SAP is using this type of intelligence in its core finance and analytical processes with an application that offers deep insight by predicting critical KPIs with an audit trail. This allows key figures to be traced back to transactions in the system that aren’t GAAP posting–relevant yet – for example, sales orders. This advance knowledge offers you greater opportunity to strategically adjust and redirect resources to proactively address any deficiencies early in the process.

Fraud detection

According to research from the Association of Certified Fraud Examiners, an organization’s typical occupational fraud loss is 5% of its annual revenue. Finance applications with intelligence built-in offer new options to combat these losses. For example, organizations can leverage machine learning and prediction to automatically detect and rank attributes that positively correlate with fraudulent system entries. This results in information that can be used to reduce risks in financial transactions.

It also has the capability of identifying noncompliance with corporate policies, which can help strengthen the controls that ultimately lead to better enterprise-wide governance.

Digital assistant for the enterprise

As machine learning matures, there are significant opportunities to complement with voice-activated, in-context collaboration tools to digitally assist with assist with financial, accounting, analytical, and risk-based tasks. The use of such tools, both at home and in the office, is growing exponentially. In fact, according to a Gartner Study, by 2020, the average person will have more conversations with bots than with their spouse.

In a business setting, digital assistants provide voice-activated, context-sensitive support within enterprise systems to boost the productivity of finance experts and key decision-makers. For example, natural language processing can create a human-like experience in interacting with your ERP system. Simply ask for the name of a supplier and immediately start collecting information such as open purchase orders or open disputes, payments, accounting postings, and more.

Intelligent technology has created a new reality for finance, defining its value across the organization. By embedding intelligence in finance applications and processes enterprise-wide, finance leaders can leverage improved levels of insight, prediction, and efficiency to drive growth and inform strategic initiatives throughout the business.

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Neil Krefsky

About Neil Krefsky

Neil Krefsky is Head of SAP Finance and Risk Product Marketing at SAP. He is responsible for the development and execution of the product marketing strategy for SAP's solutions for the finance area including: SAP S/4HANA for Finance as well as applications for financial planning and analysis, accounting and financial close, treasury and financial risk management, collaborative finance operations, and enteprise risk and compliance.