The uses of machine learning in enterprises are as varied as the different tasks performed by employees and the data that accumulates in modern enterprises.
The larger the database, the more reliable the results.
With the support of machine learning, humans can work more efficiently, because time-consuming, unloved work steps can simply be left to the computer. In most companies, for example, there are many man-hours spent on invoice verification, payment processing and back-office activities that can be automated using Robotic Process Automation (RPA).
Especially when it comes to processing large amounts of data, a learning program often even outperforms humans by proceeding faster and with fewer errors. Data science approaches make use of large database structures that are made accessible and interlinked with the help of programming languages such as Python.
Combating cybercrime and installing reliable chatbots
Machine learning is also used in the finance sector in cases of suspected credit card fraud. Especially in times of growing cybercrime, real-time fraud prevention is needed to detect suspicious activities in time while ensuring customer satisfaction through fast processing.
Even the dunning process can involve the use of machine learning to find out which customers are best reached, when, in what tone and via which channel. Deriving the best measures can help to maintain the customer relationship despite payment arrears and improve the company's liquidity at the same time.
Artificial intelligence also finds application in the form of chatbots, as these can independently answer a large proportion of customer questions. Instead of having to wait longer for a free employee, prospective customers receive immediate feedback. This promotes satisfaction and relieves the burden on employees. A real win-win situation for both sides.