Artificial intelligence and Machine Learning will change the financial world

APQC’s latest annual survey (Where IsTime Spent in Finance?) has achieved significant success in saving money, but trading still covers almost half the time of its finance departments Showing. This can be challenging for financial institutions and their leaders to take a more strategic role in evolving digital business models.

During an ordinary business week, high-paid financial staff will pay a significant portion of their time, ensure that customers receive the right bills, carry out general accounting work, evaluate fixtures, and spends it performing all the other tasks that enable the flow. We’re not saying, “Artificial intelligence’s coming, financiers are going to be out of a job.” The financiers will work more efficiently, get rid of the chores that eat most of their time. So when?

Arden from Webrazzi, one of the most important fintek events, had made interesting news from each other by following it on site. “According to Gartner, 50 percent of financial companies will use artificial intelligence from 2020,” the special article is an important piece of evidence for this article. I suggest you read this article separately. Gartner analyst Erik van Ommeren’s presentation shared important information about the direction of the use of artificial intelligence in financial affairs. Gartner heralds that artificial intelligence is booming in the financial sector and in the financial world in general, based on numbers. Let’s take a look at this in depth. How the use of artificial intelligence will change the lives of financiers and their impact on corporations, let’s at least ask some questions and look for meaningful answers.

Productivity is one of the biggest problems of companies in every department and level. As I mentioned above, most of the time of financiers is spent with limited expertise. After artificial intelligence undertakes these tasks, it will be possible to investigate the financial dimension of investment decisions, calculate the effects of revenue-expenditure and operating margin, and carry out valuable financial analysis. These are the jobs that have been the least time allocated to this day.

This new weapon of financial professionals is called Artificial Intelligence, powered by Machine Learning! Technologies that sound like science fiction are shifting to work systems and promise solutions to many of the challenges faced by financial professionals. Problems offered are processes that lead to prolonged commercial transactions such as quantity, complexity and accessibility.

According to some estimates, data volume has a growth chart of double and 50 times each year, depending on the enterprise investment in the Internet of Big Data and Things (IoT). These technologies are the markers of the upcoming future. Therefore, as ioT and Big Data usage accelerates, the growth in data volume will increase in parallel. By looking for a solution to this big data inprint, meaningful opinions can be drawn from it, resulting in a savings-oriented roadmap that requires automation.

Like the innovations that machine automation makes in industrial production and agriculture, AI/ML eliminates task-based handicrafts and value-added work expected from financial workers.

Let me give you an example from England. The National Health Service (NHS), which meets the health needs of all its citizens, uses predictive logical analysis to help detect fake demands. To do a job like this, tons of data need to be examined. At first, the clerk’s demands had to be examined on the computer, screen-to-screen and page by page. Even if some of the work was alleviated by filtering, it was a labor-intensive task.

Now, potential fake demands are being identified based on a number of well-known criteria. This is not only an effective filtering, but also the history and similarities of the data. Moreover, it is constantly updated with Machine Learning. The clerk’s job begins with the demands determined by the system and the method of manually detecting requests is eliminated.

The growing number of financial data is becoming more complex based on a variety of reasons. The proliferation of market channels, payment methods and product configurations is recorded as different variations in recording transactions. Updating ERP systems to accept information from a wider range of data sources than ever before is of great importance in this regard. With AI/ML, systems can quickly adapt themselves to changes.

AI/ML solves the problem of data access in two ways: it makes the information in the system easy to find and use, and makes it accessible to a wider audience of employees. Secondly, as AI/ML capabilities become more and more available, software can provide advice with smart chat   programs described as “chatbots”, based on similarities and trends.

One of the things that finance workers complain about most about during tea breaks is that they know that the data is in the system, but it is very difficult to access it in some cases. The use of Chatbot technology helps professionals by using natural language instead of troublesome search tools to find the data they need. For example, clerks in the NHS, the British SSI, can ask the system in their natural language, “Can you show similar elements?” when examining requests. Of course, they’re doing it in English:) In this way, more creative query types and more relevant information are accessed quickly and effortlessly.

Guidance bots can also replace the pool of experience and knowledge that is born in finance departments. Nowadays, financial information needs to be translated before it can be understood by professionals other than finance. The bots combine the company’s collective knowledge and make it easy for large audiences to benefit.

Some of us may think it’s a little futuristic, but a lot of people have been using contextual intelligence-assisted voice assistants on their phones, such as Alexa, Echoe and Siri, examples of this technology.

In the business world, enterprise software companies are working to bury adaptive intelligence in cloud applications. By combining data in the “Data as a Service” or “Data as a Service” or simply in the DaaS cloud with the company’s data and using algorithms, it can determine which suppliers can take advantage of discounts and time with advance payment. Without AI, such an archive of supplier behavior required at least one person to work full-time.

When companies start thinking about AI, the first doubt that comes to mind is, of course, security. Thanks to the use of AI/ML, human error, one of the biggest weaknesses in company data management, is reduced. When 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 to apply it. This process can take days and some companies are known to have been left to work without patches for months.

Thanks to AI/ML, the “vulnerability area” between a threat and its solution is much narrower. As threats are detected, patches are automatically generated, 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 on this subject. Staff do not need access or review all data. On the contrary, it only sees the query result and accesses a small set of data.

As for training your staff to use AI/ML technology; That’s changing depending on the technology supplier. But it is already embedded in the ERP and EPM cloud systems that finance department employees know and use every day. So it doesn’t require long training for the finance department.

Artificial intelligence and Machine Learning will help financial professionals to effectively process complex commercial operations, whose volume is rapidly increasing. This technology will save them from their tedious manual business missions. It will help financiers make decisions and contribute to their human intelligence, creativity and work experience, helping to solve business problems and discovering the best business strategy.

Categories:   Technology