As the video below explains, Lenddo looks at a potential applicants’ entire digital footprint to determine their creditworthiness by having individuals download their app. They claim it looks at over 12,000 variables including social media account use, internet browsing, geolocation data, and other smartphone information. Their machine learning algorithm turns all this data into a credit score, which banks and other lenders can use.
Lenddo claims not to share this personal data with lenders, only the final result of their analysis to protect individuals’ privacy. They claim their system has allowed their partners to approve up to 50 percent more applications.
Recently, FICO, the global credit agency, announced a major partnerships with Lenddo to use their technology as part of FICO’s new FICO score services in India. In addition earlier this year Experian partnered with Lenddo to use their technology in Indonesia and Vietnam.
ZestFinance is another company using machine learning to process alternative data to get information on so-called “thin file borrowers” – those with no or little credit history. As they explain in this video, they provide companies with the tools New Mexico title loans to use data sources to do underwriting.
One of their most significant recent announcements was a strategic investment from Baidu, the leading Chinese internet search provider. ZestFinance will use Baidu search data to develop credit scores for individuals, giving them a massive amount of data for the large Chinese market where traditional credit score systems are mostly lacking.
ZestFinance will be able to make use of Baidu’s search, location, and payment data for individuals. ZestFinance is able to help lenders determine the creditworthiness of Baidu users – even if said users have very little credit history. According to ZestFinance, there are half a billion people in China with no credit history.
In many ways, this approach models some of the “individualized data” use-cases of AI in insurance that we see at firms like Progressive, where data is collected about individual drivers to better predict their risk of accident. It’s not surprising that similar approaches would be used to refine risk predictions for lending as well – and it’s a trend we expect to continue well into the decade ahead.
ZestFinance also recently finished a study with Ford Motor Credit Company . Based on the success of the study, Ford Credit is developing plans to use machine learning in their auto financing.
It is not just startups using machine learning and some alternative sources to better determine an individual’s creditworthiness but the big established players as well. Equifax is one of the three big credit bureaus. Peter Maynard, Senior Vice President of Global Analytics at Equifax, in interview this year claimed their new “neural network improved the predictive ability of the model by up to 15 percent.” Using it to look back at recent decisions, they found that loans which were turned down could have been made safely.
Finding new and better ways to determining the creditworthiness of individuals is one way to increase business and gain customers. Eliminating administrative overhead and delays is a way to maximize the amount of profits for each loan created. For years banks and other lenders have being using computer systems to automate more and more of the loan process, but now some companies are trying to fully automate the process.
Upstart – Full Automation and AI Determined Creditworthiness
One of the most high profile startup companies using AI to determine creditworthiness and streamline the loan process is Upstart . Two of its co-founders are former top Google employees. Dave Girouard was formerly the President of Google Enterprise and Anna M. Counselman led Gmail’s consumer operations.