Model risk management
Finance

Why implement machine learning?

Christopher

Machine learning technology is a type of artificial intelligence that allows computers to learn on their own, without pre-set behaviours. It can quickly process huge amounts of unstructured information and then draw conclusions and forecasts based on it.

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

It is important to know how financial companies use ML for their purposes. Machine learning algorithms can find application in virtually any company in the financial sector. Here are some popular options for their use in real business.

Scoring

ML helps to identify the risks of lending to different borrowers. Even those who do not yet have a credit history. In such cases, forecasts are made based on a person’s data, the history of his financial transactions, activities on the Web and similar factors.

Model risk management

Underwriting

Machine learning-based solutions automate the process of repurchasing and placing securities. This allows you to make transactions at the best prices and eliminates errors due to the human factor.

Chatbots

Communicating with customers takes up a lot of time for companies, and maintaining call centres is not cheap. Machine learning can significantly reduce the burden on operators with the help of chatbots that study people’s queries and generate personalized responses for them.

Customer retention

ML algorithms can predict a person’s behaviour based on their demographics and financial history. This allows companies to learn about the risks of losing customers promptly and design the most suitable offers for them to retain.

Machine learning algorithms open up new opportunities for business development. They help: automate all possible processes, build a more competent model of interaction with customers, make more informed management decisions, and much more.