There was a time when the banks were the sole legitimate lending providers but the rapid increase of Internet coverage, smart devices penetration and credit scoring automation made possible to get a fast loan with just a few clicks on your phone. Welcome to the FinTech world built by much more than cryptocurrencies and ICOs. Credit scoring automation is the tool that enabled loan access to the underfinanced population. Matching credit scoring with machine learning, AI and automation, in general, made this process a viable business case.
Credit scoring is the process enabling lending businesses to determine how likely a lender will default its loan. This process is utilizing different data sources and less or more is designed around the business logic of the lending organization. In some markets like USA and UK, there are credit scoring bureaus assigning credit score derived from credit files and history of a particular person. In other markets, the credit scoring is up to the lending institutions, not matter banks or nonbank financial institutions like leasing companies, consumer finance companies, telecommunication companies, etc. Such businesses hold the credit risk and they need a solution like credit scoring. In this blog post, we explain why credit scoring as a service is the most viable option for lending businesses of different size and markets. Let us share with you the three main options in front of a company dealing with such kind of financial risk.
Just think of it! Without credit and lending businesses, we would not be able to buy a house or appliances when we need them. Business would not be able to grow, expand and innovate at the desired pace. Credit is the fuel of any country` economy and this is not a secret. But what is fueling credit and lending businesses? How do they decide if you are eligible for a particular loan or not?
In this blog post, our chief scientist Alexander Efremov PhD. is discussing the application of direct and recurrent artificial neural networks (ANN) in some methodologies for credit risk models.
Inputs of ANN could be the available individuals’ characteristics, like age, income, marital status, credit bureau data, etc. The outputs are the probability the applicants to have a good performance as loan-holders, the individuals’ response to some actions, etc.
FinTech stands for Financial Technology. It is a common term used for companies offering added value or entirely new financial services via technology. Financial business is generating a huge amount of data also known as big data. This is where the point of intersection for Data Analytics and FinTech is located. We also have to keep in mind that FinTech covers a lot of financial business domains like asset management, insurance, lending, transferring money, to name a few.