Application scenario
Machine learning is increasingly recognized for its potential to improve the daily business of enterprises. In terms of risk management, machine learning has been regarded as a sharp tool that can not only improve efficiency and production capacity, but also reduce costs. This is mainly because machine learning technology can quickly process and analyze a large number of unstructured data with little human intervention. It can also help banks and financial institutions reduce operational, regulatory and compliance costs, and provide banks with the ability to make accurate credit decisions.
The emergence of non-traditional loan institutions such as payment banks and technology-based non bank financial companies forces more and more traditional banks to adopt machine learning technology and technology-based algorithms. Therefore, they have to upgrade their traditional system and architecture to evaluate the credit status of customers.
At the same time, they also use alternative data sources, such as social media photos and login information, global positioning system (GPS) data, e-commerce and online shopping information, mobile data and bill payment information. With the help of big data, banks can establish a powerful internal model based on artificial intelligence for decision-making.
Therefore, machine learning solutions can provide timely and reliable data for the financial industry to build customer intelligence, successfully implement strategies and reduce losses.
Machine learning based risk management solutions can also be used for model risk management (backtracking and model validation) and stress testing to meet the requirements of global Prudential regulators.
