Machine learning in economics

Machine learning technology is a type of artificial intelligence that allows computers to learn on their own, without predefined behavior scenarios. It is able to quickly process huge amounts of unstructured information, and then draw conclusions and predictions based on it.

The more data is provided to such a system for processing, the more accurate the final result will be. For this reason, Machine learning is increasingly used by financial companies whose job is to store terabytes of information about customers, their payments, credit histories, etc.

Machine learning algorithms can be applied to virtually any company in the financial sector. Here are some of the popular real-world applications for them.

Scoring

ML helps to identify the risks of providing loans to different borrowers. Even those who do not yet have a credit history. In such cases, predictions are made on the basis of a person’s personal data, the history of his financial transactions, online activities and similar factors.

Underwriting

Machine learning solutions automate the buyback and placement process. This allows you to make transactions at the best prices and eliminates human errors.

Chat bots

Communication with customers is time-consuming and costly to run call centers. Machine learning can dramatically reduce the workload of operators by using chatbots that study people’s requests and generate personalized responses for them.

Customer retention

ML algorithms can predict a person’s behavior based on their demographics and financial history. This allows companies to learn about the risks of losing customers in a timely manner and design the most appropriate retention offers for them.

Despite the relative “youth” of the technology, it is already being used by large financial companies. Moreover, there are successful examples of its application. Consider the popular facts on the domestic and world market.

MasterCard

In 2016, the MasterCard Safety Net machine learning system detected anomalies, such as the withdrawal of large funds outside the usual region of use for this account. The activity extended to 300 ATMs in 26 countries.

The payment system blocked transactions and sent appropriate notifications to banks.

Later it turned out that these were fraudulent actions that could lead to losses in excess of $ 40 million. However, thanks to the Safety Net machine learning system, the losses were limited to only $ 100 thousand.

Monobank

Using Machine learning technology, the Ukrainian bank Monobank was able to reduce the number of problem borrowers. The percentage of their determination increased to 66%. This allowed the financial institution to increase the number of loans issued, including to people without credit history. As a result, the bank significantly increased the profitability of its products, minimizing the risks of non-return.

Machine learning algorithms open up new opportunities for business development.

They help: automate all possible processes, build a more competent model of interaction with clients, make more balanced management decisions, and much more.