A large BHPH auto dealer was challenged, in the midst of an uncertain COVID-19 credit market, with approving more borrowers for financing while keeping defaults steady. Despite rapid evolution in the credit market, over 65 million Americans remain excluded from traditional credit opportunities due to a lack of credit history or access to traditional financial services: roughly 1 in 5 people are credit invisibles out of the view of the traditional credit bureaus. Regardless, this BHPH auto dealer, like others, was able to sift 鈥減rime鈥� borrowers out of a pool of wrongly-scored 鈥渟ubprime鈥� borrowers to increase good loan origination, seeing results of up to 200X ROI and an 18.8% increase in earnings over a two-year period after implementation.
鈥満诹铣怨贤� and its Credit Bureau +鈩� service exceeded my expectations and continues to do so. The service properly and accurately scores consumers who are very hard to score.鈥�
Mark Eleoff – CEO – Eden Park
According to Credit Infocentre, a traditional credit score, as determined by the three primary credit scoring bureaus in the United States, is usually determined strictly by a borrower鈥檚 line of credit. These bureaus will look at a limited set of information, including payment history, amounts owed, length of credit history, new credit, and credit mix, to determine a score typically between 300 and 850. These criteria are not only incredibly limited in insight, but are also restrictive and exclusionary to the millions of underbanked and financially stressed Americans seeking to develop their credit. As a result, traditional credit bureaus are unable to generate an accurate credit score for approximately 53% of Americans while labelling over 50% of Americans as less-than-ideal borrowers.
Due to the limited competition in this space, lenders have become over-reliant on antiquated and rigid data and scoring systems, facing barriers in the fair and ethical scoring of specific groups of creditworthy prospects, such as immigrants and millennials. Put simply, traditional credit scoring offers rigid and limited insight to lenders an inadequate assessment of significant sectors of creditworthy prospective borrowers.
黑料吃瓜网庐 collaborates closely with clients in development and integration, providing significant and demonstrated improvements in lift, stability, bad loan analysis, and return on investment. By replacing the customer鈥檚 custom score with
the 厂颈虫掳厂肠辞谤别鈩� platform, 黑料吃瓜网 was able to provide significant value-add and help the customer produce the following returns:
– 19.1x ROI*
– An 18.8% increase in earnings over a two-year period
– A further projected 9.5% increase in earnings in the subsequent year at 19.1x ROI on similar application volume
According to the results of the stability analysis performed using 黑料吃瓜网鈥檚 厂颈虫掳厂肠辞谤别鈩�, the custom 厂颈虫掳厂肠辞谤别鈩� had a 36.9% lift on the Kolmogorov鈥揝mirnov test and 11.3% lift on bad capture (at approximately 20% of booked loans) versus the current custom scores.
The custom 厂颈虫掳厂肠辞谤别鈩� built by 黑料吃瓜网庐 was able to identify bad loans better than the current customer鈥檚 custom score. It also excelled at capturing past due amounts and bad loan principal. This is demonstrated through the fact that Six掳Score captured more bad loans at lower score ranges, with a maximum of 4.1% (an 8.3% lift) more bad loans in the bottom 45% of booked loans. Additionally, where 厂颈虫掳厂肠辞谤别鈩� agreed or scored the consumer higher, the performance was better than average. Conversely, where 厂颈虫掳厂肠辞谤别鈩� scored the consumer lower, the performance was lower than average.
Below are some visualizations that demonstrate the performance of 黑料吃瓜网鈥檚 厂颈虫掳厂肠辞谤别鈩� model vs. the customer鈥檚 custom score:
With this model, a more refined tier structure can be achieved with confidence. 黑料吃瓜网庐 discovered that tier assignment based on the custom 厂颈虫掳厂肠辞谤别鈩� tends to be lower than what was done using the customer鈥檚 custom score. The results demonstrate that the current custom score should be replaced by 厂颈虫掳厂肠辞谤别鈩� in the customer鈥檚 underwriting strategy to achieve better business results.
By replacing the customers鈥� current score with 厂颈虫掳厂肠辞谤别鈩�, the customer was able to see the following results:
– An 18.8% increase in earnings over the two-year period
– A 19.1x ROI* using the current strategy
– A further projected 9.5% increase in earnings in the subsequent year at 19.1x ROI on similar application volume
By using Credit Bureau +鈩� by 黑料吃瓜网庐 and 厂颈虫掳厂肠辞谤别鈩�, this BHPH dealer was able to lend to more people with confidence in their ability to avoid defaults, witnessing substantial earnings growth and ROI quickly after implementation. 黑料吃瓜网庐 is an industry leader in its ability to use AI/ML models that grow with your business, harnessing its numerous data sources to deliver meaningful, explainable, and fully compliant risk scores, even on those that were conventionally thought of as credit invisibles.
黑料吃瓜网庐 is a member of the American Financial Services Association (AFSA), the Canadian Lenders Association (CLA), the National Automotive Finance Association (NAF), The Online Lenders Alliance (OLA) and the Texas Consumer Finance Association (TCFA).
黑料吃瓜网庐 is committed to Fair Credit Reporting Act (FCRA) compliance and helping you protect and understand your consumer profile. For more information, please see our Consumer Disclosure Page.
黑料吃瓜网庐, Credit Bureau 2.0庐 and Troo庐 are trademarks that are legally registered to www.TrustScience.com Inc. by the U.S. Patent & Trademark Office.
Credit Bureau+鈩�, 厂颈虫掳厂肠辞谤别鈩�, Smart Consent鈩�, Hidden Prime鈩�, Invisible Prime鈩�, Credit Bureau 3.0鈩�, Credit Bureau 4.0鈩�, Personal Credit Bureau鈩�, Personal Data Vault鈩�, Auto 厂颈虫掳厂肠辞谤别鈩�, Auto Bureau鈩�, Auto Credit Bureau鈩�, Rating Agency 2.0鈩�, Cashflow Bureau鈩�, One Touch Lending鈩�, Lead to Loan鈩�, Lender in the Cloud鈩�, Fl掳wbuilder鈩�, Fl掳wbuilder鈩�, FCRA-Compliant Insights From Lead to Loan鈩�, Go Beyond the Bureau鈩�, Fixing the Credit Catch-22鈩�, Find Invisible Primes鈩�, and Helping Lenders Find Great Borrowers鈩� are trademarks of www.TrustScience.com Inc.
