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Finance Automation with Artificial Intelligence

Susan Rebner | | July 5, 2018

An important asset for any company is its data. Unfortunately, due to ever-increasing information overload, many finance teams are not able to utilize it efficiently.

Companies of all sizes are simply collecting more data than they can handle; accordingly, while more data than ever is available, the percentage of it that can be effectively leveraged is shrinking. Sorting through a sea of data to cull the beneficial information is time-consuming, tedious, and overwhelming. Artificial Intelligence (AI), and machine learning in particular, takes the pressure off of employees to manage all that data.

Below are a few historical statistics related to AI:

  • Forrester survey found that business executives expected to spend 300 percent more on cognitive computing, as compared with 2016.
  • In 2015, 80 percent of executives surveyed by Narrative Science already believed that AI boosts productivity.
  • Back in 2016, Google said that 20 percent of mobile searches were voice searches.
  • 34 percent of enterprise business executives surveyed by Narrative Science in 2016 said they were already using artificial intelligence within their organizations.

One area in which finance organizations can reap the benefits of machine learning is real-time fraud detection informed by previous confirmed cases of fraud. Algorithms are so advanced that they are able to consider that transactions happening in different locations and in places the credit card holder has never purchased from before are more likely to be fraudulent. Using all this information, machine learning can automatically determine the likelihood that a credit card has been compromised an initiate follow-up actions.

Machine learning provides finance employees the tools and information to make more insightful decisions when it comes to negotiating supplier terms. By alleviating the need for IT assistance and data scientists, AI allows finance companies to focus on meaningful work that will optimize cash flow. This is especially beneficial during high-volume periods.

Another finance example is AI in forecasting. Today when companies forecast and they identify a gap, people are running around trying to locate data to pinpoint the variance. With AI’s intelligent algorithms running in the background that variance would be quickly identified.

By integrating machine learning into a company’s Enterprise Resource Planning (ERP) and Enterprise Performance Management (EPM) systems, employees can access this information automatically. The more employees use the application, the more it is able to modify its algorithms and have constantly evolving and relevant recommendations.

“Machine learning applications and analytics provide huge opportunities for customers to monetize their existing businesses and accelerate digital business,” according to R “Ray” Wang, principal analyst and CEO at Constellation Research. “Success requires a large corpus of data, strong expertise in data science, massive compute power, industry and domain expertise, and breadth of application solutions.” Sounds a little difficult to achieve.

AI is maturing rapidly and companies a few hurdles to catch up: Ensure business strategy incorporates AI capability as fast as the software giants, like Oracle, makes it available in applications and to build a solid foundation for an AI future state. Here are some recommendations to alleviate those challenges:

  • Use Cloud-Based Applications and Be Aware – By utilizing the Cloud, companies can ensure that they are using modern technological capabilities and bypass slow integration processes. Oracle is already incorporating AI in its ERP and EPM applications. Stay abreast of AI developments, specifically those with implications for finance, and assess their relevance to your organization.
  • Create Better Data Flow – Keeping information siloed hinders AI abilities. The effectiveness of machine learning is directly impacted by the amount and quality of data available for informing the underlying algorithms. The more easily available data, both internally and externally, the better.
  • Implement Efficient Change Management – It is crucial to maintain open communication with employees, especially since AI will automate many of the employees’ day-to-day responsibilities. By discussing the motivation behind using AI and its benefits, it will alleviate the possible worry employees may have over job security.
  • Invest Time to Train Employees – Since employees will no longer have to focus time and energy on the more remedial tasks that AI will tackle, they will need a completely new set of skills that focuses on utilizing real-time insight. Together with business leaders, they need to be able to use the available information to modify strategies and capitalize on opportunities AI brings color to. By training employees to effectively leverage the benefits of AI, companies will be able to truly reap the benefits offered by machine learning and remain forward-thinking.

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