The role of finance is in a constant state of metamorphosis, shedding traditional fiduciary control and compliance to form strategic partnerships and spread its wings as an indomitable catalyst for business growth. However, many day-to-day activities still require considerable manual intervention, which is hampering the full benefit of any digital strategy.
For years, CFOs have invested in various technologies to maximize the potential of their entire organization. Analytics – historical, real-time, Big Data, and predictive – are connecting operations, functions, and individuals to provide full visibility and access to insights. Then, more recently, blockchain and the Internet of Things have come on the scene, enabling sophisticated data collection and analysis to deliver actionable insights that can help build a competitive advantage.
Now, CFOs are considering the promise of intelligent automation through machine learning. Instead of just capturing and reporting data, computers can “think,” make better decisions, and research faster by creating mathematical algorithms based on accumulated data. But are finance organizations ready to fully trust that these automated systems are operating alone with the right information?
The journey to digital maturity builds the foundation for trusted data
Data accuracy is one of many topics associated with an abundance of clichés and shareable quotes from various experts. Personally, I believe Aaron Koblin, an entrepreneur best known for his innovative use of data visualization, crowdsourcing, virtual reality, and interactive film, concisely summed up the current state of data: “I think you can have a ridiculously enormous and complex data set, but if you have the right tools and methodology, then it’s not a problem.”
Every step taken along the evolutionary trail of finance organizations has played some part in setting the stage for greater automation with improved data accuracy. No matter how small or large the digital investment, organizations are moving closer to gaining trust in the accuracy of automated data analysis, while processes accelerate and require less human interaction.
Take, for example, blockchain. Companies are finding value in this technology, beyond bitcoin, to monitor and manage resources at the enterprise and local level, leverage validated information, and deliver better insights on how to maintain compliance, seize new opportunities, and deflect risk. Companies then gain the right methods, powered by in-memory computing, to gain better visibility into operational performance and leverage Big Data to make insight-driven decisions.
Machine learning is the next logical step to use this enriched, validated, and accurate data to liberate finance professionals from at least five kinds of redundant, low-value – yet necessary – work.
1. Digital business assistants
Voice-activated intelligent assistants, based on machine learning technology, will understand the business context of processes in different areas. These digital assistants can create a holistic view of a specific business situation providing, for example, an overview of customer status. Finance experts can then analyze the information and make proposals to optimally handle a particular situation. They gain transparency into the situation instantly, equipping them with the insight needed to make the best decisions without investing time to research.
2. Financial planning and analysis
Machine learning capabilities, built into predictive analytics, go far beyond pure analysis of existing data. Based on various data sources, the functionality identifies trends, predicts impacts on your business numbers, and determines a view into the future of your business with intelligent projections and what-if analysis. This enables finance professionals to make better decisions for a brighter future for the business. The resulting capability is a clear driver for elevating the office of the CFO as a valuable partner of the business and a strategic advisor to the CEO.
3. Finance operations
Most finance operations still rely heavily on manual, time-consuming activities. Consequently, digital technology offers vast potential to increase automation and focus more on exception-handling and service quality. Take, for example, a receivables management process where incoming payments need to be matched with invoices. Thanks to pooling, discounts, and other factors, matching becomes anything but trivial. With machine learning, matching rates are not only better, but also improve over time by learning from the data and human-exception decisions.
The next step is to add the remittance advice extractor. In turn, matching rates increase because machine learning extracts information from unstructured advice and translates them into structured data, which automates the clearing process. Financial service customer requests are then highly automated, unstructured requests are analyzed, context is identified, and answers are proposed to the service agent or proactively addressed. With this task automated, agents are freed up to focus on critical special requests.
4. Enterprise governance, risk, and compliance
Machine learning helps detect and prevent fraud by identifying and ranking information that positively correlates with defined attributes of duplicitous activities. Investigators learn from their company’s history to detect new fraud patterns and reduce false positives. These predictive detection methods can be incorporated into existing methods and fraud management strategies.
It’s time to embrace machine learning as part of continuous data innovation
Increasingly, CFOs are acknowledging that the digital transformation of finance is an essential, urgent, and ongoing task. In fact, the Oxford Economics study, “How Finance Leadership Pays Off,” sponsored by SAP, revealed that 73% of surveyed finance leaders believe that automation is improving their function’s efficiency and giving employees more bandwidth for value-added tasks.
Recognizing the importance of such an evolution does not always – or automatically – open the door to the need for more resources. Through machine learning, finance organization can do more than ever before with lower or current support levels, innovate new ways to work, and increase efficiency, output, and, ultimately, profitability.