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.
It is true that lending has been a focus for FinTech over the last decade. As this particular segment is a genuine revenue generator for traditional banks, many competitors from the nonbanking financial sector have been attracted. In order to succeed they should be better than banks, especially having higher interest rates in mind.
Scorecards are the main instrument for assessing risk clients and they have to predict with as much accuracy as possible whether the client is going to pay his debt or default. The truth is that the bank sector relies on a scorecard for a year or two after some fine-tuning. Since the FinTech lending companies client base is more sensitive to economic shifts and downturns they have to be way more flexible and adaptive to the changes. This is where AI-powered credit scoring is coming to help.
Imagine a tool getting better and better with every single use. It is like a car getting faster with every single track round. This is how AI-powered credit scoring works for FinTech companies. Self-learning algorithms are constantly fine-tuning themselves in order to give the optimal cutoff.
Sure, automation can help to streamline the business processes but it also can give FinTech lending companies advantage in terms of their strategy. Not matter if they want to increase their acceptance rate or to reduce defaults by implementing specific business rules, AI-powered credit scoring can easily implement such specifics.
The automated credit scoring FinTech lending companies relying on is better than static scorecards utilized by banks with one more key feature. It can easily implement new data points if data scientist and business analysts decide they are going to improve the credit scoring model performance. This is extremely important on highly competitive markets as online-based lending solutions and it is a competitive advantage at its best.
Last but not least, if banks need a day or two to process your credit inquiry, FinTech lending companies are often able to answer if a customer is eligible within a seconds. Such expeditiousness is a significant business advantage solving problems in a way that static bank credit products just cannot.
Real-time decisions based on the most significant variables extracted out of rich data are the magic behind the automated credit scoring solutions, which made FinTech lending possible.
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