How Fintech’s Use Technology to Create New Credit Scoring Models?

This is due to the fact that the more accurate your credit scoring model is, the better the quality of your bank's lending programs will be. More than any other factor, the extent to which your financial institution researches prospective borrowers will have the largest influence on the size and profitability of your loan book. 

To be effective, your loan selection process must be rapid, accurate, and low-risk. In today's lending market, if you want to stay one step ahead of your competition, you must first comprehend the industry. Small firms' creditworthiness has traditionally been established by credit ratings, which have been used to establish creditworthiness by banks, credit unions, and other non-banking financial institutions. 

These antiquated tactics, on the other hand, exclude a sizable portion of the market. Small business owners with a poor or non-existent credit history may be able to repay the borrowed cash within the time-frame indicated. Despite this, people are routinely denied credit because of the limitations set by traditional credit scoring methodologies.

Enter fintech!

Financial technology companies may play an essential role in this process as an adjunct and a trigger. They create and specify a new credit scoring model from scratch. These innovative new technologies enable financial institutions and lenders to choose the most suitable loan product for the most suitable candidate at the most suitable time. They do this by using a range of indicators to assess a borrower's reliability. 

These models are more precise since they are typically based on real-time or predict data rather than prior performance. As a result, these models are more beneficial to both lenders and potential borrowers. The lending industry as a whole is undergoing a transformation as a result of the rise of new generations of small business lenders and cutting-edge technologies. Because of the perfect mix of real-time financial data and cutting-edge financial technology currently available, lenders can immediately recognize and respond to these new credit scoring models. 

Lenders who employ these models can speed up their underwriting operations and approve loans in less time. They may, however, give personalized solutions, limit credit risk, reduce credit bias, make loans more egalitarian, and expand loan portfolios. Lenders now use a range of data sources to improve the customer experience and assess whether or not to extend or deny credit to a specific client. Some data points were previously impossible to gather due to lengthy underwriting processes, and as a result, they were not used. 

However, because of technology improvements, they are now more suited to aiding in credit decisions and are simpler to find than ever before.

What Is the Difference Between the Old and New Credit Scoring Models?

Despite the fact that both of these models are designed to achieve the same purpose, the criteria utilized to establish a borrower's creditworthiness is what distinguishes them. In the traditional credit score paradigm, a borrower's creditworthiness is frequently determined only by his or her prior credit history.

Credit scoring companies calculate credit scores using a mix of information given by individuals. Payment history, current debts, the number of open accounts, the credit utilization percentage, and the duration of credit history are all considered. These firms also make full use of publicly available information to gain a complete picture of a borrower's financial history.

Credit bureaus compute a score that varies between 300 and 850 points based on the information supplied above, with higher scores being desirable. Commercial banks are free to set their own minimum credit ratings for lending reasons. A score of 800 or more, on the other hand, is considered excellent. Lenders utilize credit rating in the context of risk-based pricing. 

This happens when the size of the loan, the interest rate, and the terms offered to borrowers are influenced by the chance that the loan will be repaid. According to research issued by the Consumer Financial Protection Bureau in 2020: “About 26 million Americans are credit invisible, and 19 million Americans have credit histories that have become stale or are inadequate to generate a score under the most commonly used scoring models," says the Federal Reserve.

Lenders are reluctant of financing to small business owners since the great majority of them have a poor or non-existent credit history.

Widening the scope!

Small businesses with less-than-perfect credit can get working capital by utilizing a new and unique credit scoring methodology established by modern fintech that rates their creditworthiness based on a range of characteristics. A lender, for example, may examine the following facts in determining a borrower's ability to repay the loan amount:

  • Accounting
  • Banking
  • Financial
  • eCommerce
  • Cash flow predictive data
  • Spending habits

In addition, many data sources such as rent, utility, internet, shopping history, property records, and others are being used.

In addition to these elements, some new credit scoring algorithms will incorporate other factors. For example, the education, employment, and social media presence of a borrower are all considered. Lenders can make better-educated lending judgments when they have a thorough perspective of a potential borrower's financial situation. In addition, the risk level is determined using information other than a conventional credit score, which is utilized to compute the risk level.

What value do Fintechs and technology bring to the credit-scoring process?

When it comes to credit rating, financial technology companies are experimenting with the use of non-traditional data to gauge risk. To achieve their goal of developing credit scoring models for the underbanked, this fintech use artificial intelligence, machine learning, advanced analytics, and predictive modeling-based tools to evaluate potential borrowers based on a variety of data points not included in traditional FICO scores and the five credit criteria.

A borrower's ability to start a new enterprise or obtain financing to expand an existing firm purely on the basis of their credit score can put many young and aspiring entrepreneurs in difficult financial positions, which can lead to bankruptcy. Fortunately, fintech firms are leveraging technology developments to move beyond credit scores. They enable lenders to collect, analyze, and use more extensive and real-time data to make better lending choices. 

It can also help small business owners who are unable to secure finance from traditional banking institutions. Lenders may now analyze borrowers' financial health in the past, present, and future using artificial intelligence and machine learning-based technology. They can track and evaluate their financial behaviors, habits, and trends over time, resulting in more intelligent and less demanding credit approvals. 

Some technologically advanced fintech businesses, such as ForwardAI and Validis, as well as Codat, provide financial data APIs that help lenders get financial information from a variety of sources and deliver better services to their clients. For example, utilizing cash flow prediction data in credit scoring can help lenders determine if a firm will continue to generate the same amount of money and make regular payments in the future. 

It will also disclose whether or not a firm has gone out of business or is facing cash flow problems. When it comes to establishing creditworthiness, harnessing real-time financial data is a novel and imaginative answer to a growing problem.


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