Artificial intelligence has transformed the day-to-day operations of financial services, and the result has been a rapprochement between machine learning and finance . With this, companies in this sector can resolve situations ranging from risk management to investment forecasting.
“Machine learning is the automation of processes and malta whatsapp data decisions based on past data to identify patterns that may occur,” explains Daniel Bergmann, professor at Saint Paul Business School.
There are several examples of the application of this automation in the financial area. Want to know which ones? Stay with us and enjoy reading!
Machine Learning in Finance: 5 Examples of Its Use
1 – Risk management
Imagine that a financial institution wants to predict which of its customers has a profile that is likely to default on their payment obligations. With machine learning, it can use variables such as age, income, number of dependents, and credit restrictions to characterize the profile of a bad payer. Thus, when a new customer arrives, it is possible to assign a score to them and determine the probability of possible default.
By analyzing a huge amount of data in real time, machine learning provides efficiency in processes that a manager, for example, would not be able to achieve. At the same time, by performing extensive analyses, it allows managers to have more time to focus on more productive tasks, such as customer service.
Another example of application is in extrajudicial collection:
“There are call centers that are very expensive and require a lot of employees to call customers in debt several times. With machine learning, it is possible to create a program to identify patterns in these debtors and identify which ones can be recovered,” suggests Professor Bergmann.
This increases objectivity and reduces the time between the first contact and the final settlement of the debt.