Tag: credit scoring

Learn how you can grow your portfolio and minimize risk by leveraging industry-leading alternative credit scoring models. Read more!

A move toward inclusive finance, including incorporating alternative data in credit scoring models, is a crucial step towards promoting financial inclusion.

According to Experian’s State of the Automotive Finance Market Report: Q2 2022, the average new vehicle interest loan rate for consumers with a credit score between 501 and 600, also referred to as subprime, was 9.75%—compared to prime consumers with a credit score between 661 and 780, who had an average new vehicle interest loan rate of 4.03% this quarter.

What goals should you set to make financial inclusion a reality? How can success be quantified? This checklist can help you become more inclusive.

Learn how expanded FCRA-regulated data can be used to gain a more powerful and complete view of consumers' financial situations. Read more.

Credit scores play a major aspect in our lives. However, today's scoring system prevents many individuals from accessing credit. Learn more.

New challenges created by the COVID-19 pandemic have made it imperative for utility providers to adapt strategies and processes that preserve positive customer relationships. At the same time, they must ensure proper individualized customer treatment by using industry-specific risk scores and modeled income options at the time of onboarding As part of our ongoing Q&A perspective series, Shawn Rife, Experian’s Director of Risk Scoring, sat down with us to discuss consumer trends and their potential impact on the onboarding process. Q: Several utility providers use credit scoring to identify which customers are required to pay a deposit. How does the credit scoring process work and do traditional credit scores differ from industry-specific scores? The goal for utility providers is to onboard as many consumers as possible without having to obtain security deposits. The use of traditional credit scoring can be key to maximizing consumer opportunities. To that end, credit can be used even for consumers with little or no past-payment history in order to prove their financial ability to take on utility payments. Q: How can the utilities industry use consumer income information to help identify consumers who are eligible for income assistance programs? Typically, income information is used to promote inclusion and maximize onboarding, rather than to decline/exclude consumers. A key use of income data within the utility space is to identify the eligibility for need-based financial aid programs and provide relief to the consumers who need it most. Q: Many utility providers stop the onboarding process and apply a larger deposit when they do not get a “hit” on a certain customer. Is there additional data available to score these “no hit” customers and turn a deposit into an approval? Yes, various additional data sources that can be leveraged to drive first or second chances that would otherwise be unattainable. These sources include, but are not limited to, alternative payment data, full-file public record information and other forms of consumer-permissioned payment data. Q: Have you noticed any employment trends due to the COVID-19 pandemic? How can those be applied at the time of onboarding? According to Experian’s latest State of the Economy Report, the U.S. labor market continues to have a slow recovery amidst the current COVID-19 crisis, with the unemployment rate at 7.9% in September. While the ongoing effects on unemployment are still unknown, there’s a good chance that several job/employment categories will be disproportionately affected long-term, which could have ramifications on employment rates and earnings. To that end, Experian has developed exclusive capabilities to help utility providers identify impacted consumers and target programs aimed at providing financial assistance. Ultimately, the usage of income and employment/unemployment data should increase in the future as it can be highly predictive of a consumer’s ability to pay For more insight on how to enhance your collection processes and capabilities, watch our Experian Symposium Series event on-demand. Watch now Learn more About our Experts: Shawn Rife, Director of Risk Scoring, Experian Consumer Information Services, North America Shawn manages Experian’s credit risk scoring models while empowering clients to maximize the scope and influence of their lending universe. He leads the implementation of alternative credit data within the lending environment, as well as key product implementation initiatives.

We are excited to announce that Experian has been selected as a Fintech Breakthrough Awards winner in the Consumer Lending Innovation category.

The DMV is embracing technology to improve overall customer experience, and financial institutions can benefit from doing the same.

Later this year, FICO will retire its Score V1, making it mandatory for those lenders still using the old software to find another solution.

Whether its new regulations and enforcement actions from the Consumer Financial Protection Bureau or emerging legislation in Congress, the public policy environment for consumer and commercial credit is dynamic and increasingly complex. If you are interested to learn more about how to navigate an increasingly choppy regulatory environment, consider joining a breakout session at Experian’s Vision 2016 Conference that I will be moderating. I’ll be joined by several experts and practitioners, including: John Bottega, Enterprise Data Management Conor French, Funding Circle Troy Dennis, TD Bank Don Taylor, President, Automated Collection Services During our session, you’ll learn about some of the most trying regulatory issues confronting the consumer and commercial credit ecosystem. Most importantly, the session will look at how to turn potential challenges into opportunities. This includes learning how to incorporate new alternative data sets into credit scoring models while still ensuring compliance with existing fair lending laws. We’ll also take a deep dive into some of the coming changes to debt collection practices as a result of the CFPB’s highly anticipated rulemaking. Finally, the panel will take a close look at the challenges of online marketplace lenders and some of the mounting regulations facing small business lenders. Learn more about Vision 2016 and how to register for the May conference.

Whether it is an online marketplace lender offering to refinance the student loan debt of a recent college graduate or an online small-business lender providing an entrepreneur with a loan when no one else will, there is no doubt innovation in the online lending sector is changing how Americans gain access to credit. This expanding market segment takes great pride in using “next-generation” underwriting and credit scoring risk models. In particular, many online lenders are incorporating noncredit information such as income, education history (i.e., type of degree and college), professional licenses and consumer-supplied information in an effort to strike the right balance between properly assessing credit risk and serving consumers typically shunned by traditional lenders because of a thin credit history. Regulatory concerns The exponential growth of the online lending sector has caught the attention of regulators — such as the U.S. Treasury Department, the Federal Deposit Insurance Corporation, Congress and the California Business Development Office — who are interested in learning more about how online marketplace lenders are assessing the credit risk of consumers and small businesses. At least one official, Antonio Weiss, a counselor to the Treasury secretary, has publicly raised concerns about the use of so-called nontraditional data in the underwriting process, particularly data gleaned from social media accounts. Weiss said that “just because a credit decision is made by an algorithm, doesn’t mean it is fair,” citing the need for lenders to be aware of compliance with fair lending obligations when integrating nontraditional credit data. Innovative and “tried and true” are not mutually exclusive Some have suggested the only way to assuage regulatory concerns and control risk is by using tried-and-true legacy credit risk models. The fact is, however, online marketplace lenders can — and should — continue to push the envelope on innovative underwriting and business models, so long as these models properly gauge credit risk and ensure compliance with fair lending rules. It’s not a simple either-or scenario. Lenders always must ensure their scoring analytics are based upon predictive and accurate data. That’s why lenders historically have relied on credit history, which is based upon data consumers can dispute using their rights under the Fair Credit Reporting Act. Statistically sound and validated scores protect consumers from discrimination and lenders from disparate impact claims under the Equal Credit Opportunity Act. The Office of the Comptroller of the Currency guidance on model risk management is an example of regulators’ focus on holding responsible the entities they oversee for the validation, testing and accuracy of their models. Marketplace lenders who want to push the limit can look to credit scoring models now being used in the marketplace without negatively impacting credit quality or raising fair lending risk. For example, VantageScore® allows for the scoring of 30 million to 35 million more people who currently are unscoreable under legacy credit score models. The VantageScore® credit score does this by using a broader, deeper set of credit file data and more advanced modeling techniques. This allows the VantageScore® credit score model to capture unique consumer behaviors more accurately. In conclusion, online marketplace lenders should continue innovating with their own “secret sauce” and custom decisioning systems that may include a mix of noncredit factors. But they also can stay ahead of the curve by relying on innovative “tried-and-true” credit score models like the VantageScore® credit score model. These models incorporate the best of both worlds by leaning on innovative scoring analytics that are more inclusive, while providing marketplace lenders with assurances the decisioning is both statistically sound and compliant with fair lending laws. VantageScore® is a registered trademark of VantageScore Solutions, LLC.

Every portfolio has a set of delinquent customers who do not make their payments on time. Truth. Every lender wants to collect on those payments. Truth. But will you really ever be able to recover all of those delinquent funds? Sadly, no. Still, financial institutions often treat all delinquent customers equally, working the account the same and assuming eventually they’ll get their funds. The sentiment to recover is good, but a lot of collection resources are wasted on customers who are difficult or impossible to recover. The good news? There is a better way. Predictive analytics can help optimize the allocation of collection resources by identifying the most effective accounts to prioritize to your best collectors, do not contact and proceed to legal actions to significantly increase the recovery of dollars, and at the same time reduce collection costs. I had the opportunity to recently present at the annual Debt Buyer Association’s International Conference and chat with my peers about this very topic. We asked the room, “How many of you are using scoring to determine how to work your collection accounts?” The response was 50/50, revealing many of these well-intentioned collectors are working themselves too hard, and likely not getting the desired returns. Before you dive into your collections work, you need to respond to two questions: Which accounts am I going to work first? How am I going to work those accounts? This is where scoring enters the scene. A scoring model is a statistical algorithm that assigns a numerical expression based on known information to predict an unknown future outcome. You can then use segmentation to group individuals with others that show the same behavior characteristics and rank order groups for collection strategies. In short, you allow the score to dictate the collection efforts and slope your expenses based on the propensity and expected amount of the consumer to pay. This will inform you on: What type, if any, skip trace tactic you should use? If you should purchase additional data? What intensity you should work the account? With scoring, you will see different performances on different debts. If you have 100 accounts you are collecting on, you’ll then want to find the accounts where you will have the greatest likelihood to collect, and collect the most dollars. I like to say, “You can’t get blood from a stone.” Well the same holds true for certain accounts in your collections pile. Try all you like, but you’ll never recoup those dollars, or the dollars you do recoup will be minimal. With a scoring strategy, you can establish your “hit list” and find the most attractive accounts to collect on, and also match your most profitable accounts with your best collectors. My message to anyone managing a collections portfolio can be summed up in three key messages. You need to use scoring in your business to optimize resources and increase profits. The better data that goes into your model will net you better performance results. Get a compliance infrastructure in place so you can ensure you are collecting the right way and stay out of trouble. The beauty of scores is they tell you what to do. It will help you best match resources to the most profitable accounts, and work smarter, not harder. That’s the power of scoring.

By: Teri Tassara In my blog last month, I covered the importance of using quality credit attributes to gain greater accuracy in risk models.  Credit attributes are also powerful in strengthening the decision process by providing granular views on consumers based on unique behavior characteristics.  Effective uses include segmentation, overlay to scores and policy definition – across the entire customer lifecycle, from prospecting to collections and recovery. Overlay to scores – Credit attributes can be used to effectively segment generic scores to arrive at refined “Yes” or “No” decisions.  In essence, this is customization without the added time and expense of custom model development.  By overlaying attributes to scores, you can further segment the scored population to achieve appreciable lift over and above the use of a score alone. Segmentation – Once you made your “Yes” or “No” decision based on a specific score or within a score range, credit attributes can be used to tailor your final decision based on the “who”, “what” and “why”.  For instance, you have two consumers with the same score. Credit attributes will tell you that Consumer A has a total credit limit of $25K and a BTL of 8%; Consumer B has a total credit limit of $15K, but a BTL of 25%.   This insight will allow you to determine the best offer for each consumer. Policy definition - Policy rules can be applied first to get the desirable universe.  For example, an auto lender may have a strict policy against giving credit to anyone with a repossession in the past, regardless of the consumer’s current risk score. High quality attributes can play a significant role in the overall decision making process, and its expansive usage across the customer lifecycle adds greater flexibility which translates to faster speed to market.  In today’s dynamic market, credit attributes that are continuously aligned with market trends and purposed across various analytical are essential to delivering better decisions.  

As a scoring manager, this question has always stumped me because there was never a clear answer. It simply meant less than prime – but how much less? What does the term actually mean? How do you quantify something so subjective? Do you assign it a credit score? Which one? There were definitely more questions than answers. But a new proposed ruling from the FDIC could change all that – at least when it comes to large bank pricing assessments. The proposed ruling does a couple of things to bring clarity to the murky waters of the subprime definition. First, it replaces the term “subprime” with “high-risk consumer loans”. Then they go one better: they quantify high-risk as having a 20% probability of default or higher. Finally, something we can calculate! The arbitrary 3-digit credit score that has been used in the past to define the line between prime and subprime has several flaws. First of all, if a subprime loan is defined as having any particular credit score, it has to be for a specific version of a specific model at a specific time. That’s because the default rates associated to any given score is relative to the model used to calculate it. There are hundreds of custom-build and generic scoring models in use by lenders today – does that single score represent the same level of risk to all of them? Absolutely not. And even if all risk models were calibrated exactly the same, just assigning credit risk a number has no real meaning over time. We all know what scores shift, that consumer credit behavior is not the same today as it was just 6 years ago. In 2006, if a score of X represented a 15% likelihood of default, that same score today could represent 20% or more. It is far better to align a definition of risk with its probability of default to begin with! While it only currently applies to the large bank pricing assessments with the FDIC, this proposed ruling is a great step in the right direction. As this new approach catches on, we may see it start to move into other polices and adopted by various organizations as they assess risk throughout the lending cycle.