Tag : credit scoring

More certainty for the consumer finance business amid COVID-19

“Only when the tide goes out do you discover who has been swimming naked.”

Warren Buffett

Those who act fast and adapt to turmoil will survive and succeed. In the context of the Coronavirus pandemic and the lending business, this means that financial institutions should have instruments to continuously analyze on a permanent basis what is happening and react immediately if needed.

As experts in credit scoring utilizing artificial intelligence (AI) and machine learning (ML), we have prepared this publication to offer an in-depth understanding of the problems the consumer finance business is currently facing. We focus on counteraction mechanisms, emphasizing the potential of automated decision-making in general. The dramatic change in people’s lives, the social distancing measures and the heterogeneous impact of the crisis on particular segments of the population, especially employment, as well as the need for additional financing led to a shock for the consumer finance business. As a result, some businesses have drastically reduced their operations and switched their credit scoring processes to manual loan approvals. This significantly reduced the speed and efficiency of their lending process as a whole. Some companies even chose to shut down part of their business abroad due to fears of significant deterioration of their loan portfolio and the negative effect of the imposed legal measures on debt repayment and collateral liquidity.

How credit scoring automation made FinTech lending possible

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.

Why credit scoring as a service is the most viable option for consumer finance companies

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.

How AI & predictive analytics fuel credit scoring-as-a-service

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?

Artificial neural networks and credit risk modeling

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.

5 FinTech Applications of Data Analytics

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.