Banks exist to make credit scoring decisions. Key function of banks is to enable individuals and companies to make expenditures before they can afford them. They can not accomplish this key function without becoming a competent estimator of who will pay back and who will default.
Banks always relied on models and experts to make effective credit decisions. Now models are becoming sophisticated enough to replace experts. AI companies are stepping in to provide these models to banks so they can focus on serving their customers.
What value do AI companies bring to credit scoring?
When it comes to credit scoring, banks already hold the most valuable data: repayments. Loan repayment track records are extremely valuable in understanding market dynamics and banks are the only institutions that hold granular data on repayments. They would need to share anonymized data on repayments with AI companies so they can build effective models.
While they lack unique data, AI companies bring model building expertise and AI talent. It is expensive to develop an in-house data science/AI team as a bank since
- Tech enthusiasts find it less enticing to work in industries traditionally not considered tech
- Data science talent is scarce and expensive
- It takes time and effort to gather data other than loan repayment data. However granular data on topics such as the applicant’s income, career, education or expenditures can be quite valuable in credit scoring
Are there regulatory challenges to using diverse sets of data for credit scoring?
Currently, no. At least in the United States. B2C loan provider fintech company Upstart applied to The Consumer Financial Protection Bureau to get an opinion on its use of alternative data in loan applications and received a “no-action letter” allowing the startup to continue its use of alternative data.
We examined top AI companies providing credit scoring models to banks and other financial institutions.
Complete list of vendors providing credit scoring models
|# of employees on Linkedin
This is possibly an incomplete list in an emerging field, feel free to suggest other vendors in comments.
To learn more on AI-powered credit scoring techniques
Below Marc Stein, the founder and CEO of underwrite.ai, is explaining AI techniques that his company uses in credit scoring models. They perform AI-powered analytics derived from genomics and particle physics to provide lenders with non-linear, dynamic models of credit risk that radically outperform traditional.
And if you believe your enterprise could benefit from using a credit scoring model, we have a data-driven list of vendors prepared.
We will help you choose the best solution for your needs:
Cem has been the principal analyst at AIMultiple since 2017. AIMultiple informs hundreds of thousands of businesses (as per similarWeb) including 60% of Fortune 500 every month.
Cem's work has been cited by leading global publications including Business Insider, Forbes, Washington Post, global firms like Deloitte, HPE, NGOs like World Economic Forum and supranational organizations like European Commission. You can see more reputable companies and media that referenced AIMultiple.
Throughout his career, Cem served as a tech consultant, tech buyer and tech entrepreneur. He advised businesses on their enterprise software, automation, cloud, AI / ML and other technology related decisions at McKinsey & Company and Altman Solon for more than a decade. He also published a McKinsey report on digitalization.
He led technology strategy and procurement of a telco while reporting to the CEO. He has also led commercial growth of deep tech company Hypatos that reached a 7 digit annual recurring revenue and a 9 digit valuation from 0 within 2 years. Cem's work in Hypatos was covered by leading technology publications like TechCrunch and Business Insider.
Cem regularly speaks at international technology conferences. He graduated from Bogazici University as a computer engineer and holds an MBA from Columbia Business School.
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