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China Struggles to Establish Credit Scoring System

China's government has tried for many years to establish a credit system that rivals the U.S. and Europe, but has found it a struggle to get there.

May 9, 2018 By Todd Anderson Leave a Comment

Views: 144

In 2015 China’s Central Bank contracted eight companies, including affiliates of Tencent, Ping An and Alibaba, to help build a credit system that could rival what other developed economies use. The experiment up until today has not fared well as lenders and e-commerce firms continue to use their own proprietary systems to determine risk of the borrower.

One of the main drivers behind the failure is companies were reluctant to share the data they collected. Also, conflicts of interest arose as users could be rewarded by using certain companies to drive their score up.

The People’s Bank of China officially ended the original consortium credit idea earlier this year and created a new company, Baihang Credit Scoring. The new company is structured as a joint venture by using the original eight companies and awarding them each with an eight percent ownership stake. The remaining 36 percent is held by state-owned National Internet Finance Association.

[Read more…]

Filed Under: Peer to Peer Lending Tagged With: Aadhaar, Alibaba, Baihang Credit Scoring, China, credit scoring, NIFA, Ping An, Social Credit, Tencent

Views: 144

How AI & Machine Learning Can Fix the Broken Credit Scoring System

Marc Stein from Underwrite.ai shares his thoughts on fixing the credit scoring system.

July 27, 2017 By admin 1 Comment

Views: 1,400

Editor’s Note: This is a guest post from Marc Stein, CEO at Underwrite.ai and Principal at Artificial Intelligence Capital Management. A longtime entrepreneur and startup CTO, he cofounded the first auction platform for student loans, College Loan Market, a marketplace for equipment leasing, LeaseQ, and ScholarshipWS, the search engine that drives many of the largest college scholarship search sites. He also served as CTO at Y2M Networks, sold to Viacom and the Student Loan Consolidation Program sold to JP Morgan Chase, and as Global CTO for the Giving Group, a UK entity that operates the world’s largest peer to peer charitable fundraising service. In his spare time, he works on applications of artificial intelligence towards problems in cancer diagnosis and genetic biomarker identification.]

I was recently on a panel at Money 20/20 in Copenhagen with the intriguing title “Credit scoring is broken: Striving for fairness and accuracy in a data-rich world”.

The question stuck with me beyond the panel itself. Is credit scoring broken? If so, when did it break? Who broke it? And, most importantly, why is it broken?

In a sense, the credit scoring system exists to minimize risk to lenders by focusing on the lending of money to people who have proven themselves to be low risks. The dominant scoring methodology in developed economies is solely focused on how people repay prior loans, how many loans they’ve already taken, and how many times they’ve applied for credit.

This works to simply exclude those without existing credit.

I recently worked on a study for a large lender that was testing into their decline population. This gave me some insight into the performance of thin and no-file applicants without FICO scores. What was most interesting about the results was that the overall portfolio had a charge-off rate of 10%, with the lowest performing cohort charging off at 22%. The FICO unscored group, who would almost always be denied credit, charged off at 14%.

So, in fact, the applicants with no score were quite profitable if lent to at the higher priced tiers and outperformed the low end of the approved spectrum.

But does this mean that the credit scoring system is broken? In itself, no. Lenders can loan to applicants with FICO scores at or above 700 with a fairly solid understanding of repayment risk. This leads to the extension of government subsidized credit to the lowest risk portion of the credit spectrum.

But this divides the society into three classes of people. Those with access to cheap credit from subsidized sources, those with access to expensive credit from leveraged sources, and those with no access to credit.

The disparity in credit access is baked into the credit scoring method. But why did this happen?

I propose a simple explanation. In order to create models that can be efficiently implemented, the problem of credit risk has been oversimplified. The current methodology in widespread use seeks to model risk using linear models with a small number of inputs. [Read more…]

Filed Under: Peer to Peer Lending Tagged With: artificial intelligence, credit scoring, Machine Learning

Views: 1,400

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ABOUT LENDIT FINTECH NEWS

LendIt Fintech News, Powered by Lend Academy, has been bringing you all the news and information about fintech and online lending since 2010 when it was founded by Peter Renton. We not only have the industry’s most active news site, but also the largest investor forum and the first and most popular podcast.

We are a team of fintech enthusiasts who have been covering the industry for many years. With a deep knowledge of online lending, digital banking, blockchain, artificial intelligence and more our team covers the daily news and writes in-depth editorials.

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