APQC (American Center for Productivity and Quality)Where Does Time Spend in Finance?), although significant success has been achieved in saving, commercial transactions still cover almost half the time of the finance departments. This can be challenging for financial units and leaders to play a more strategic role in emerging digital business models.
In an ordinary business week, highly-paid financial staff spends a significant amount of time on paying bills, ensuring that customers receive the correct bills, performing general accounting, evaluating fixtures, and performing all other tasks that enable the flow of money within the organization. “Artificial intelligence will come, the financiers will be unemployed,” we do not say. The financiers will work more efficiently, avoiding the drudgery of eating most of their time. So when?
Landmark for artificial intelligence in 2020 finance
One of the most important fintek events, Money 20/20, Arden from Webrazzi, followed the spot and produced interesting news. “According to Gartner, by 2020, 50 percent of financial companies will use artificial intelligence,” the special news, this article is an important evidence. I suggest you also read this article. In the presentation of Gartner analyst Erik van Ommeren, important information about the direction of artificial intelligence use in financial affairs was shared. Gartner, heralds the fact that the financial sector and the artificial intelligence in the financial world in general are booming. Let's examine this in a little bit. Let's ask at least some questions and look for meaningful answers to how the use of artificial intelligence will change the lives of financiers and their impact on companies.
Financiers will do their job
Productivity is one of the biggest problems of companies in every department and level. As I mentioned above, most of the time of the financiers is spent with jobs that require limited expertise. After artificial intelligence assumes these tasks, it will be possible to investigate the financial dimension of investment decisions, calculate revenue-expense and operational margin effects, and carry out valuable financial analysis studies. These are the least time spent to date.
This new weapon for finance professionals is called Artificial Intelligence, powered by Machine Learning! Technologies that sound like sci-fi are shifting to working systems and promising solutions to many challenging process problems faced by financial professionals. Among the problems being solved are the processes that lead to the prolongation of commercial transactions such as quantity, complexity and accessibility.
How does AI and ML solve these problems?
According to some estimates, depending on the institutional investment in the Internet of Big Data and Objects (IoT), the data volume has a growth rate of between twice and 50 times each year. These technologies are pointers to the upcoming future. Therefore, as the use of IoT and Big Data accelerates, we can say that the growth in data volume will increase in parallel. By looking for solutions to this big data raid, meaningful insights can be drawn and a savings-oriented roadmap that requires automation can be developed.
Like machine automation in industrial production and agriculture, AI / ML eliminates task-based handicrafts and non-value-added work expected from finance professionals.
Let me give you an example from England. The National Health Service (NHS), which meets the health and health needs of all its citizens, uses predictive logical analysis to help identify false claims. For such a job, tons of data need to be examined. Previously, the clerk of these demands on the computer, screen by screen and had to review page by page. Even though some of the work was mitigated by filtering, it was a demanding task.
Now, potential fraudulent claims are determined based on a number of well-known criteria. This is not only effective filtering, but also the history and similarities of the data. Moreover, it is constantly updated with Machine Learning. The job of the clerk begins with the requests determined by the system and the method of manually detecting the requests is eliminated.
The growing complexity of financial data is due to various reasons. The proliferation of market channels, payment methods and product configurations is recorded as different variations in recording transactions. Updating ERP systems to receive information from a wider range of data sources than ever before is of paramount importance. With AI / ML, systems can quickly adapt to changes.
AI / ML solves the problem of data access in two ways: it makes it easy to find and use information in the system and makes it accessible to a wider audience. Secondly, as AI / ML capabilities face more and more usage scenarios, the software is based on similarities and trends, the smart chat described as “chatbot”. programs.
Chatbots will take on more business
One of the issues that finance employees are most closely related to during tea breaks is that they know that the data is in the system, but in some cases it is very difficult to access it. The use of Chatbot technology helps professionals find the data they need by using natural language instead of troublesome search tools. For example, the NHS clerks, the British SSI, while examining the demands of the system in their natural language, “Show similar elements?” they can ask. Of course they do this in English 🙂 In this way, more creative query types and more relevant information are continuously accessed quickly and effortlessly.
Guidance boats can also replace the pool of experience and knowledge in financial departments. Nowadays, financial information needs to be translated before it can be understood by non-finance professionals. Thanks to the bots, the collective knowledge of the company is brought together and large audiences can easily benefit from it.
Some of us may think this is a bit futuristic, but many people have already used contextual intelligence assisted voice assistants, such as Alexa, Echoe and Siri, on their phones.
In the business world, enterprise software companies are working to embed adaptive intelligence into cloud applications. “Data as a Service” or simply the data in the DaaS cloud can be combined with company data and algorithms to determine which suppliers, for example, can take advantage of discounts by prepayment and time. Without AI, such an archive preparation for supplier behavior required at least one full-time study.
Is artificial intelligence safe in finance?
The first thing that comes to mind when companies start to think about AI is security. Using AI / ML reduces human error, one of the biggest weaknesses in corporate data management. If a threat was detected in the past, the manufacturer would create a patch, publish it, and then send it to a company employee or a third-party representative for implementation. This process can last for days and it is known that some companies have been left without patches for months.
Thanks to AI / ML, the “vulnerability” between a threat and its solution is much narrower. As threats are detected, patches are generated automatically, but are implemented in the system age. Another way to strengthen security with AI is to reduce the amount of data that people can see. AI provides more accurate information. Staff do not need to access and review all data. On the contrary, it only sees the query result and accesses a small dataset.
When it comes to training your staff to use AI / ML technology; it's something that varies from technology to supplier. However, it is already embedded in the ERP and EPM cloud systems that the finance department employees know and use every day. So it does not require long training for the finance department.
Artificial Intelligence and Machine Learning will help finance professionals to effectively process complex rapidly increasing volume of business transactions. This technology will save them from tedious commercial tasks done by hand. It will help financiers make decisions and contribute to their human intelligence, creativity and work experience, and help them solve business problems and discover the best business strategy.