Week 6 Case Study 13.1 – Upstart, What is Upstart and how does

Week 6 Case Study

Read “Case 13.1: Upstart” and write an essay that answers the following questions:

1. What is Upstart and how does it differ from traditional lenders in terms of determining creditworthiness?

2. How does Upstart’s use of AI technology increase the accuracy of risk assessments and lead to a higher approval rate for loans?

3. What concerns do consumers have about Upstart’s use of alternative data to determine creditworthiness, and how does the company address those concerns?

4. How has Upstart’s success with AI technology affected the finance sector, and what changes do you predict for the future of lending and credit?

5. How does the use of AI technology in the credit and mortgage industry impact young borrowers with less credit history? 

Requirements:

· There is no specific page requirement for your analysis. Instead, your work will be evaluated based on how thoroughly it addresses each of the questions that have been outlined for you.

· You must utilize proper APA formatting and citations throughout your paper. If you use any supporting evidence from external sources, it is imperative that you provide accurate citations for each reference.

· You must include a minimum of two sources from scholarly articles or business periodicals, aside from the course textbook. 

· Include your best critical thinking and analysis to arrive at your justification.    

 

 

Case 13.1: Upstart

One of the leading companies to implement AI technologies into their company is Upstart, and their innovativeness may drastically change the credit and mortgage industry. The company was founded in 2012 by current CEO Dave Girouard. Upstart is a leading AI lending platform that uses unconventional yet accurate mea- surements in determining creditworthiness.

Upstart is a lending platform that uses artificial intelligence that is designed to improve access to affordable credit to those who might be a certain demographic that traditional ways would have made it harder for these individuals to get a loan approved. Traditional ways are still being used because we are comfortable with looking at the length of someone’s credit history and making the decision if they are qualified to get an amount of money from a bank or a lending company.

The company uses machine learning algorithms along with artificial intelligence to take several factors into consideration when deciding how creditworthy a poten- tial borrower is. It considers components such as one’s specific occupation, employer, education, or current institution where they may still be in school. In broadening the scope of elements that lenders can use, borrowers can be approved for loans that they may not have been approved for under traditional FICO consid- erations. Girouard stated that 45% of Americans have access to bank-quality credit, yet 83% of Americans have never defaulted on a loan. Upstart used this as motiva- tion to help borrowers qualify at a higher rate, which they did successfully. Their model has been proven to help qualify 27% more consumers than standard credit practices. In addition, it has been proven to lower interest rates by an average of 3.57 points. The alternative data that Upstart collects to determine creditworthiness have had some consumers concerned about invasion of privacy. However, the company has made it clear that under fair lending laws and ethical business practices, the information that they gather is strictly confidential and in no way could put consum- ers at risk.

The machine learning algorithms that Upstart is using are always improving. This could mean adding more uncommon variables to an algorithm to better assess the risk to a possible user. Upstart is trying to be able to make machine learning the way to approve loans. In 2019, 67% of the loans that had gotten approved were fully automated through the machine learning underwriting process. As machine learning in this industry gets further training and receives more information, it will be more common to see conventional banks and loan companies adopting these algorithms. To start this new wave of getting more people in business, they need to eliminate any unwanted biases that us as human beings have.

Upstart can increase its accuracy with the risk assessments because the algo- rithms are always evolving and adding new or changing variables. This has helped Upstart face lower default rates at similar approval rates. This also helps Upstart be able to accept more applicants that apply for loans and get accepted at the same loss rate. Due to the success of Upstart and similar companies, I believe it will lead to a huge shift in the finance sector of the business world. The banks that do not see the change from human to AI will become obsolete in a sense because the approval rates are much higher with companies like Upstart. Companies like Upstart are tar- geting the younger generations and allowing them to get an amount of money even though they have the lack of credit history typical lenders require/want and they do not have an absurdly high interest rate that shy people away from getting a loan altogether.

Analysis of this kind was never possible before the introduction of artificial intel- ligence and machine learning. Algorithms that can examine these factors in a matter of seconds have elevated business for Upstart and other companies that plan on utilizing the evolving technologies. As COVID restrictions begin to lift and young adults with low credit scores begin to look for loans for new homes, cars, and other big purchases that their generation will be looking for, Upstart will have much of an advantage over lenders who are still using conventional credit analysis methods. Young borrowers with less credit history know that companies using traditional FICO methods may reject them, whereas through Upstart they have a much better chance of getting approved for that loan. As the competitiveness begins to rise within this industry, it is very likely that every credit and mortgage company will eventually evolve towards using artificial intelligence and machine learning in their business practices.

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