The 吃瓜不打烊庐 Advantage: Installment Lending Case Studies
- Conventional Score Approaches
Despite a 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.
According to Credit Info Center, 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 labeling 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 only offers rigid and limited insight to lenders with inadequate assessment of significant sectors of creditworthy prospective borrowers.
- The 吃瓜不打烊庐 Approach
鈥淭his solution is transformative in the under-served, financially-excluded sector of the economy. It can score thin files and no hits, and it can do so in a fluid credit environment.鈥� 鈥� Natalie Bell 鈥� COO 鈥� Magical Credit
According to research by Duke University (2019), behavioral data is as informative as people鈥檚 credit bureau scores. Knowing this, traditional bureau data is just one facet of 吃瓜不打烊庐 scoring: 吃瓜不打烊庐 also leverages public data, proprietary data and consented data to produce highly predictive credit scores. These data sets are processed by the 吃瓜不打烊鈥檚庐 cloud-based SaaS decision support platform to promptly deliver a fully compliant and explainable AI- and ML-powered score. As a result, 吃瓜不打烊鈥檚 厂颈虫掳厂肠辞谤别鈩� identifies a larger pool of creditworthy customers with increased accuracy and insight into probability of default, probability of delinquency and ability to manage payback. By delivering data-backed and AI-driven insights that help deserving people get the credit they deserve, 吃瓜不打烊庐 gives lenders the ability to improve loan inclusivity, expand their loan originations, and grow their business with absolute confidence in their decisioning process.
- Case 1: U.S. Retail Installment Lender
A large installment lender was challenged, in the midst of an uncertain COVID-19 credit market, with finding more leads and increasing originations while lowering defaults. Before partnering with 吃瓜不打烊庐, this installment lender used lead generation and direct mail marketing campaigns with poor results due to a low response rate and a high default rate. With 吃瓜不打烊庐 this installment lender was able to sift 鈥減rime鈥� borrowers out of a pool of wrongly-scored 鈥渟ubprime鈥� borrowers to increase new loan originations by over 50% and reduce charge-offs by 24.5%.
How did this lender expand their model to better identify prospective borrowers, significantly grow their business while decreasing losses due to default? With the power of Credit Bureau +鈩� by 吃瓜不打烊庐: a wealth of alternative data analyzed by proprietary artificial intelligence and machine learning technology and synthesized into fully compliant and easily explainable Six掳Scores鈩�, offering highly accurate risk scores that enable more effective and inclusive lending decisions.
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In the case of this installment lender, their direct marketing campaign approval rate climbed to +90%. In two months their direct mail marketing campaign drove $6.2MM in new originations with lower default rates than organic walk-ins. New originations climbed to 57% of funded loans. Perhaps what is more impressive is that these results were achieved without the use of a client-specific, custom model but with one of 吃瓜不打烊鈥檚庐 ready to deploy, industry-specific models, meaning a solution was in production in weeks, not months.
吃瓜不打烊庐 does more than just identify good borrowers, we help lenders optimize revenue against risk. It鈥檚 one thing to decide who to lend to; it鈥檚 another thing to know how much to lend to them. 吃瓜不打烊庐 can also provide risk-based Recommended Loan Amounts to help lenders increase the value of loans without exceeding their tolerance for risk. These Recommended Loan Amounts can be used in direct mail marketing campaigns to increase response rates of prescreened prospects. In the case of this installment lender, they saw a 50% increase in loan amounts for new loan origination.
- Case 2: U.S. Online Installment Lender
A large online installment lender was challenged with increasing originations, opening new customer populations and decreasing first payment default (FPD) rates and overall default rates. Before 吃瓜不打烊庐, this lender was using Experian Clarity and TransUnion to identify and segment leads into high quality and low quality leads, respectively.
This lender opted to go with a 吃瓜不打烊庐 custom solution that incorporated a two-model approach: one model to evaluate the probability of default and a propensity model to optimize lead conversion. With this custom solution, 吃瓜不打烊庐 also devised a strategy to optimize profitability based on risk targets, decision thresholds, population segmentation and product matching.
With the 吃瓜不打烊庐 custom 厂颈虫掳厂肠辞谤别鈩�, the Experian Clarity lead segment had a 11% lift on the Kolmogorov鈥揝mirnov (KS) test for default and 40% lift for conversions. This contributed to a 68% increase in conversions and a 17% reduction in FPD. The TransUnion lead segment had a 41% KS lift for default and 38% lift for conversions. This contributed to a 3x% increase in conversions and a 15% reduction in FPD.
Overall, the move to 吃瓜不打烊庐 resulted in this installment lender seeing a 3.7x ROI and a 5.8x ROI for their Experian and TransUnion segments, respectively.
Impacts
By using Credit Bureau +鈩� by 吃瓜不打烊庐 and 厂颈虫掳厂肠辞谤别鈩�, these installment lenders were able to lend to more people with confidence in their ability to avoid defaults, witnessing substantial earnings growth and ROI quickly after implementation. 吃瓜不打烊庐 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. For lending leaders who need to score financially stressed or underbanked borrowers in a fair and ethical way, 吃瓜不打烊庐 offers a fully compliant, data-driven, AI-powered solution, right now. Learn more about 吃瓜不打烊庐 at their website: trustscience.com.
