Earlier, only when a person had good reputation and was well known in the bank, he would be provided with a loan. But that antiquated approach has become an unfair model. Instead, FICO has developed a credit ranking system based on the leading mathematics of the time that is widely used today. The innovation in the banking industry has changed the lending rates and mortgage. The applicants who lacked in credit history could not prove their ability to pay back the loans. To overcome all such issues, ML and AI came into existence in the banking sector. Several ML factors can benefit the lending industry too.
As each user has different goals and risks associated with them, the process of creating a portfolio is an endeavor. Here, AI and machine learning can develop an individualized portfolio. To complete this process, an AI algorithm analyzes information such as the age of a customer, their income, and current assets. Also, AI and machine learning have the potential to accelerate trading at a faster pace, that can be instrumental in conducting efficient and successful financial businesses.
AI and machine learning can ensure a greater level of security by sharing the information in a digital platform. The potential threats that affect the customer information can be detected by its routine check. In addition, the response of initial invasion detection is quicker, and the security at financial institutions can flag the unusual behavior for monitoring. Also, it allows the customers to change the password with their voice or face recognition.
Machine learning has developed ways to conduct risk assessments that could accurately predict credit scores for consumers. It allows the chances of underserved consumers to present themselves with credit profiles. Consumer lenders could gain a competitive advantage over other institutions using traditional credit score because of machine learning. The evolution of this technology has improved the understanding of consumer dynamics and geopolitical movements. It has benefitted small, medium and large banks globally.
