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Small SUVs became the most financed vehicle segment in Q3 2020, making up 26.01% of all financed vehicles during the quarter.

Previously, we discussed the risks of account takeover and how a Defense in Depth strategy can protect your business. Before implementation it’s important to understand the financial benefits of the strategy. There are a few key steps to assessing and quantifying the value of Defense in Depth. Transaction risk assessment: This requires taking inventory of all possible transactions. Session-level risk analysis: With the transactions categorized by risk level, the next step is to review session history based on the highest risk activity within the session. Quantify the cost of a challenge: There are multiple costs associated with challenging a user using step-up authentication. Consider both direct and indirect costs – failure rate, contact center operational cost, and attrition rate following failed challenges (consider lifetime value of account) Quantify the expected challenge rate: This can be done by comparing the Defense in Depth approach to a traditional approach. Below is a calculator that will help determine the cost of the reduced challenges associated with a Defense in Depth strategy versus a traditional strategy. initIframe('5f039d2e4c508b1b0aafa4bd'); In addition to the quantitative benefits, it is important to consider some of the qualitative benefits of this approach: Challenging at moments that matter: Customers appreciate and expect protection in online banking, especially when moving money externally or updating contact information. This is a great way to achieve both convenience and security. Improved fraud management: By staging the risk decision at the transaction level, the business can balance the type of challenge with the transaction risk. There are incremental cost considerations to include in the business case as well. For instance, there is an increase in transaction calls for a risk assessment at the medium/high risk transactions – about 10% in the example above. Generally, the increased transaction cost is more than offset by the reduction in cost of challenges alone. A Defense in Depth strategy can help businesses manage fraud risk and prevent account takeover in online banking without sacrificing user experience. If you are interested in assistance with building your business case and understanding the strategies to implement a successful Defense in Depth strategy, contact us today. Contact us 1Identity Fraud in the Digital Age, Javelin Strategy & Research, September 2020

Preventing account takeover (ATO) fraud is paramount in today’s increasingly digital world. In this two-part series, we’ll explore the benefits and considerations of a Defense in Depth strategy for stopping ATO. The challenges with preventing account takeover Historically, managing fraud and identity risk in online banking has been a trade-off between customer experience and the effectiveness of fraud controls. The basic control structure relies on a lock on the front door of online banking front door—login—as the primary authentication control to defend against ATO. Within this structure, there are two choices. The first is tightening the lock, which equals a higher rate of step-up authentication challenges and lower fraud losses. The second is loosening the lock, which results in a lower challenge rate and higher fraud loses. Businesses can layer in more controls to reduce the false positives, but that only allows marginal efficiency increases and usually represents a significant expense in both time and budget to add in new controls. Now is the perfect time for businesses reassess their online banking authentication strategy for a multitude of reasons: ATO is on the rise: According to Javelin Strategy & Research, ATO increased 72% in 2019.1 Users’ identities and credentials are at more risk than ever before: Spear phishing and data breaches are now a fact of life leading to reduced effectiveness of traditional authentication controls. Online banking enrollments are on the rise: According to BioCatch, in the months following initial shelter-in-place orders across the country, banks have seen a massive spike in first time online banking access. Users expect security in online banking: Half of consumers continue to cite security as the most important factor in their online experience. Businesses who reassess the control structure for their online banking will increase the effectiveness of their tools and reduce the number of customers challenged at the same time – giving them Defense in Depth. What is Defense in Depth? Defense in Depth refers to a strategy in which a series of defense mechanisms are layered in order to protect data and information. The basic assumptions underlying the value of a Defense in Depth strategy are: Different types of transactions within online banking have different levels of inherent risk (e.g., external money movement is considerably higher risk compared to viewing recent credit card transactions) At login, the overall transaction risk associated with the session risk is unknown The risk associated with online banking is concentrated in relatively small populations – the vast majority of digital transactions are low risk This is the Pareto principle at play – i.e., about 80% of online banking risk is concentrated within about 20% of sessions. Experian research shows that risk is even more concentrated – closer to >90% of the risk is concentrated in <10% of transactions. This is relatively intuitive, as the most common activities within online banking consist of users checking their balance or reviewing recent transactions. It is much less common for customers to engage in higher risk transaction. The challenge is that businesses cannot know the session risk at the time of challenge, thus their efficiency is destined to be sub-optimal. The benefits of Defense in Depth A Defense in Depth strategy can really change the economics of an online banking security program. Adopting a strategy that continuously assesses the overall session risk as a user navigates through their session allows more efficient risk decisions at moments that matter most to the user. With that increased efficiency, businesses are better set up to prevent fraud without frustrating legitimate users. Defense in Depth allows businesses to intelligently layer security protocols to protect against vulnerability – helping to prevent theft and reputational losses and minimize end-user frustration. In addition to these benefits, a continuous risk-based approach can have lower overall operational costs than a traditional security approach. The second part of this series will explore the cost considerations associated with the Defense in Depth strategy explored above. In the meantime, feel free to reach out to discuss options. Contact us 1Identity Fraud in the Digital Age, Javelin Strategy & Research, September 2020

COVID-19 is not only shifting the way we work, live and think, but it is also reframing the conversation behind which metrics successful companies focus on. Having worked in marketing for various lenders, origination and funding milestones were prevalent in their marketing. However, during this unique time in mortgage when most lenders are shattering previous origination records, focus is now drawn to new performance indicators. Providing a seamless digital process A recent McKinsey survey determined that consumer and business digital adoption vaulted five years forward in a matter of eight weeks at the beginning of the pandemic. And while this is generally true for business, many mortgage lenders may not have had the time or resources to update and modernize their processes due to massive origination volumes. When volume is good, companies wait to update their technology – either due to an “if it isn’t breaking why fix it” mentality, or, in the case of unmanageable volume, lenders can’t fathom disrupting their processes. Lenders that proactively streamlined technology and focused on digital adoption before the pandemic are leveraging and benefitting from the current mortgage environment. For lenders that did not digitize in time, the high-volume environment highlights their inefficiencies and unscalable processes. Providing meaningful customer experiences Forward-thinking, resilient mortgage lenders are also tracking how effectively they can provide meaningful customer experiences, for both their borrowers as well as their internal customers – their employees. For borrowers, it could come in the form of enjoying a seamless mortgage experience, being proactively kept abreast of their loan status, and the ability to interact and communicate with the lender in a manner that works best for their style. For employees of the company, this can come from feeling valued and listened to, with relevant and useful communications and resources to rely on during these uncertain times. It also comes in the form of providing the right resources for employees to perform at a high level during these times when it matters the most and working efficiently without sacrificing quality. Investing in technology and your greatest asset, your employees, is the answer to how mortgage lenders can achieve these metrics which will help them stand out among their competition. As the refi heyday starts to show signs of impermanence, these differentiators will become more important than ever – and all lenders should be taking a proactive look now at how they can bridge their digital gaps. Mortgage lenders are coming out of 2020 with strong earnings and should look to allocate a part of these earnings towards ‘future-proofing’ through scalable technology that will ultimately reduce costs and continue to bring in qualified volume. Join Experian Mortgage in accelerating the mortgage evolution and learn how we can help bridge your technology gaps. Learn More

No two customers are the same. That’s why it’s important to go beyond the traditional credit score for a closer look at each consumer’s individual circumstance and create personalized response plans. Learn more about some of the many different customers you’ll encounter and download our guide to get recommendations for every stage of the lifecycle. Get the Guide

According to Experian’s Q3 2020 State of the Automotive Finance Market report, 26.20% of all new vehicles are leased compared to 30.27% last year.

It’s clear that the digital transformation we experienced this year is here to stay. While there are many positives associated with this transformation – innovation, new ways to work, and greater online connectedness – it’s important that we review the risks associated with these trends as well.   In late 2019 and throughout 2020, Experian surveyed consumers and businesses. We asked about online habits, expectations for information security and plans for future spending. Unsurprisingly, about half of consumers think they’ll continue to spend more online in the coming year. Those same consumers now have a higher expectation for their online experience than before the onset of COVID-19.   Hand-in-hand with the online activity trends come increased risks associated with identity theft and fraud as criminals find new chances to steal information. In response to both of these trends, businesses and consumers want a balance between security and convenience.   Our latest trends report dives into the new opportunities 2020 has created for fraud, and the opportunities to prevent identity theft or manipulation and the associated losses while building stronger relationships.   Download the full North America Trends Report for a look into North American trends over the last year and to learn how fraud prevention and positive customer relationships are actually two sides of the same coin. North America Trends Report

While things aren't quite back to normal in Q3 2020, there were a number of positive trends that demonstrates the automotive industry's resilience.

Leveraging data to eliminate wasted ad spend will set your dealership up for success in the new year.

Experian recently announced the new members named to its Fintech Advisory Board. The board and its members provide Experian with valuable insights and key perspectives into the unique and quickly evolving needs of the fintech industry. “For years Experian has been committed to partnering with innovators in the fintech industry to bring better opportunities to businesses and consumers alike,” said Experian North American CEO Craig Boundy. “We appreciate the thought leadership we get from our Fintech Advisory Board members and the challenge and the push that comes along with it,” he said. The board met virtually last month, welcoming representatives from across the fintech ecosystem representing payments, personal and secured loan lenders, credit card issuers, investors and others. “This was my first board meeting with Experian, and I’m very pleased to see the investment Experian has put into being the best of the three major bureaus in having the best technology to enable us to turnaround our models more quickly, and better data and alternative data sources like Boost,” said one of the new executives appointed to the board. “We are delighted to gather this group of innovators together to ensure we are consistently meeting the needs of our fintech partners,” said Experian Vice President Jon Bailey, who oversees the fintech vertical.  “Now more than ever it’s important that we work alongside them in shaping the industry and helping them meet their goals for the future,” he said. Experian’s fintech vertical provides leading-edge solutions and data across the credit lifecycle specifically designed to impact Fintech and marketplace lending companies and their customers. For more information on Experian’s fintech services or the advisory board, click here.

When we think about vehicle history, we tend to imagine two audiences: dealers and consumers. After all, identifying any potential hidden defects could have a significant impact on a used car buying decision; vehicle history reports are an invaluable part of the process. But it’s not just dealers and consumers who can benefit. It takes three things to sell a vehicle: the car (dealers), the consumer and credit; we’ve covered the first two, so let’s focus on the third. Lenders take a plethora of information into consideration when making automotive lending decisions, including a borrower’s credit score, payment history and utilization rate. But these data points only reflect the risk associated with the borrower; there’s also inherent risk with the vehicle itself. I recently participated in a virtual workshop, The Risky Side of the Road, during Used Car Week 2020, where we discussed the value of leveraging vehicle history information to minimize risk with lending decisions. Extending a loan to a borrower hoping to purchase a used vehicle with unidentified defects exposes the lender to unnecessary risk; hidden damage and maintenance costs could impact a borrower’s ability to repay the loan. To minimize portfolio risk, we recommend lenders leverage vehicle history reports, such as AutoCheck, before making a lending decision. Hidden Damage Significantly Impacts Vehicle Value Let’s consider the universe of used vehicles that could potentially be sold and financed. According to Experian’s Q2 2020 Market Trends Review, there are more than 280 million vehicles on the road. And our research indicates that four out of 10 of the cars and light duty trucks on the road have been in at least one accident, and around 20% of vehicles have been in multiple accidents. What does this mean for a vehicle’s value? Even if a vehicle has been completely restored and repaired, the value of the vehicle diminishes. According to a recent Mitchell Industry Trends Physical Damage Report, in Q2 2019, the average diminished value for a vehicle involved in an accident was $3,151; and this doesn’t include the fiscal impact of other hidden defects, such as flood damage. And the loss in value trickles down to the consumer and lender. For instance, if a lender unknowingly extends a $10,000 loan to a consumer who purchases a used vehicle that was involved in an accident, the actual value of the vehicle may be around $7,000. If the consumer decides to sell the vehicle before paying off the loan, it is very likely they will be up-side down. If the consumer falls behind on payments and the vehicle is repossessed, it will be difficult for the lender to recoup any losses at auction. But that’s where vehicle history reports come into play. Tools, such as AutoCheck vehicle history reports, inform lenders about reported accidents and recall information, among other insights. In addition, the AutoCheck Score, enables users to compare a vehicle with vehicles of similar class and age and assess the likelihood it will be on the road in five years. The AutoCheck Score can also help gauge the value and drivability of a repossessed vehicle.  For example, according to Experian’s similarly titled white paper, The Risky Side of the Road, we found that the percentage of repossessed vehicles that were drivable was higher for vehicles assured by AutoCheck vehicle history reports (86.16%) versus those that were not assured (80.75%). Additionally, we found that repossessed vehicles that were drivable tend to have higher AutoCheck Score range. And unsurprisingly, vehicles that are drivable tend to perform better at auction, meaning a better return on investment for the lender. During these uncertain times, it is important for lenders to more precisely gauge the level of risk they take on. The more information lenders have about the used vehicles they are financing, the better positioned they will be to offer loan terms that minimize portfolio risk, while better meeting consumer needs. To view Experian’s white paper, The Risky Side of the Road, click here.

Financial services companies have long struggled to make inclusive decisions for small businesses and for low- and moderate-income consumers. One key reason: to make accurate predictions of the financial risks associated with those customers’ accounts requires lenders to rely on a wider variety of data than a credit score alone. To accurately assess risk, expanded Fair Credit Reporting Act regulated data is helpful – including rental data, trended data, enhanced public records, alternative financial services data and more. This expanded FCRA data is one key to financial inclusion. Without that data, lenders risk rejecting potentially profitable customers, including so-called credit invisibles and thin file consumers. In fact, The Federal Reserve, along with four important financial services regulators, highlighted the consumer benefits of alternative data in their December 2019 interagency statement. That statement also highlighted the increased importance of managing compliance when firms use alternative data in credit underwriting. With hundreds of data sources available to help with important tasks such as verifying identity, checking credit, and assessing the value of automotive and real-estate collateral, why have some lenders been slow to use the most appropriate data attributes when making credit decisions? One reason is a matter of IT Architecture; another is priorities. Changing a business process to take advantage of new data requirements can be prohibitively lengthy and costly – ­in terms of both analytical and IT resources. This is especially true for older systems—which were seldom adapted to use Application Programming Interfaces (APIs) supporting modern data structures such as JSON. Furthermore, data access to older systems can require specific types of system connectivity such as VPNs or leased lines. Latency is important in this type of application: some of these tasks have to be done instantly in a digital-first or digital-only lending environment. So is time to market: lenders deploying analytics processes cannot wait for overtaxed IT teams to complete lengthy projects. Lenders’ analytics and IT teams have long known they need to be more agile and efficient, faster to market, and increasingly secure. Their answer, largely, has been a slow but steady migration of their systems to the cloud. A 2019 McKinsey survey revealed that CIOs were modernizing their infrastructures primarily to achieve four goals: agility and time to market, quality and reliability, cost, and security. There are other benefits as well. But if the business case for a cloud strategy was somewhat clear to IT and analytics leaders, it became crystal clear to the rest of the business in 2020. As companies shifted to at-home work using cloud-based collaboration tools, especially videoconferencing services, most companies conquered what was perhaps the final barrier to entry—the fear that the issues of data privacy and security were somehow more insurmountable with virtual machines, containers, and microservices than with on-premise infrastructure. Last quarter, the leading cloud providers ­Amazon Web Services, Google Cloud Platform, and Microsoft Azure ­reported incredible annual revenue growth: 29%, 45%, and 48% respectively. COVID-19 has proven to be the catalyst that greatly sped up the transition to cloud technologies. The jump to the cloud means that lenders are suddenly more capable than ever at making analytically sound – and therefore more financially inclusive ­decisions. The key to analytical decision-making is to use the right data and to make the most appropriate calculations (called attributes) as part of a business strategy or a mathematical model. With Experian programs such as Attribute Toolbox now available in the cloud, calculating those all-important attributes is as simple for the IT department as coding an API call. Lenders will soon be able just as easily to retrieve and process raw data from over 100 data sources, to recognize their native formats and to extract the desired information quickly enough for real-time and batch decisioning. The pandemic has brought economic distress to millions of Americans—it is unlike anything in our lifetimes. The growth of cloud computing promises to enable these consumers to obtain additional products as well as more favorable pricing and terms. It’s ironic that COVID has accelerated the adoption of the very technologies that will expand access to credit for many people who cannot currently access it from mainstream financial firms. To learn more about our Attribute Toolbox, click here. 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.

The global pandemic has created major shifts in the ways companies operate and innovate. For many organizations, a heavy reliance on cloud applications and cloud services has become the new normal, with cloud applications being praised as “an unsung hero” for accommodating a world in crisis, as stated in an article from the Channel Company. However, cloud computing isn’t just for consumers and employees working from home. In the last few years, cloud computing has changed the way organizations and businesses operate. Cloud-based solutions offer the flexibility, reduced operational costs and fast deployment that can transform the ways traditional companies operate. In fact, migrating services and software to the cloud has become one of the next steps to a successful digital transformation. What is cloud computing? Simply put – it’s the ability to run applications or software from remote servers, hosted by external providers, also known as infrastructure-as-a-service (IaaS). Data collected from cloud computing is stored online and is accessed via the Internet. According to a study by CommVault, more than 93% of business leaders say that they are moving at least some of their processes to the cloud, and a majority are already cloud-only or plan to completely migrate. In a recent Forrester blog titled ‘Troubled Times Test Traditional Tech Titans,’ Glenn O’Donnell, Vice President, Research Director at Forrester highlights that “as we saw in prior economic crises, the developments that carried business through the crisis remained in place. As many companies shift their infrastructure to cloud services through this pandemic, those migrated systems will almost certainly remain in the cloud.” In short, cloud computing is the new wave – now more than ever during a crisis. But what are the benefits of moving to the cloud? Flexibility Cloud computing offers the flexibility that companies need to adjust to fluctuating business environments. During periods of unexpected growth or slow growth, companies can expand to add or remove storage space, applications, or features and scale as needed. Businesses will only have to pay for the resources that they need. In a pandemic, having this flexibility and easy access is the key to adjusting to volatile market conditions. Reduced operational costs Companies (big or small) that want to reduce costs from running a data center will find that moving to the cloud is extremely cost-effective. Cloud computing eliminates the high cost of hardware, IT resources and maintaining internal and on-premise data systems. Cloud-based solutions can also help organizations modernize their IT infrastructures and automate their processes. By migrating to the cloud, companies will be able to save substantial capital costs and see a higher return on investment – while maintaining efficiency. Faster deployment With the cloud, companies get the ability to deploy and launch programs and applications quickly and seamlessly. Programs can be deployed in days as opposed to weeks – so that businesses can operate faster and more efficiently than ever. During a pandemic, faster deployment speeds can help organizations accommodate, make updates to software and pivot quickly to changing market conditions. Flexible, scalable, and cost-effective solutions will be the keys to thriving during and after a pandemic. That’s why we’ve enhanced a variety of our solutions to be cloud-based – to help your organization adapt to today’s changing customer needs. Solutions like our Attribute Toolbox are now officially on the cloud, to help your organizations make better, faster, and more effective decisions. Learn More

Intuitively we all know that people with higher credit risk scores tend to get more favorable loan terms. Since a higher credit risk score corresponds to lower chance of delinquency, a lender can grant: a higher credit line, a more favorable APR or a mix of those and other loan terms. Some people might wonder if there is a way to quantify the relationship between a credit risk score and the loan terms in a more mathematically rigorous way. For example, what is an appropriate credit limit for a given score band? Early in my career I worked a lot with mathematical optimization. This optimization used a software product called Marketswitch (later purchased by Experian). One caveat of optimization is in order to choose an optimal decision you must first simulate all possible decisions. Basically, one decision cannot be deemed better than another if the consequences of those decisions are unknown. So how does this relate to credit risk scores? Credit scores are designed to give lenders an overall view of a borrower’s credit worthiness. For example, a generic risk score might be calibrated to perform across: personal loans, credit cards, auto loans, real estate, etc. Per lending category, the developer of the credit risk score will provide an “odds chart;” that is, how many good outcomes can you expect per bad outcome. Here is an odds chart for VantageScore® 3 (overall - demi-decile). Score Range How Many Goods for 1 Bad 823-850 932.3 815-823 609.0 808-815 487.6 799-808 386.1 789-799 272.5 777-789 228.1 763-777 156.1 750-763 115.6 737-750 85.5 723-737 60.3 709-723 45.1 693-709 33.0 678-693 24.3 662-678 18.3 648-662 14.1 631-648 10.8 608-631 7.9 581-608 5.5 542-581 3.5 300-542 1.5 Per the above chart, there will be 932.3 good accounts for every one “bad” (delinquent) account in the score range of 823-850. Now, it’s a simple calculation to turn that into a bad rate (i.e. what percentage of accounts in this band will go bad). So, if there are 932.3 good accounts for every one bad account, we have (1 expected bad)/(1 expected bad + 932.3 expected good accounts) = 1/(1+932.3) = 0.1071%. So, in the credit risk band of 823-850 an account has a 0.1071% chance of going bad. It’s very simple to apply the same formula to the other risk bands as seen in the table below. Score Range How Many Goods for 1 Bad Bad Rate 823-850 932.3 0.1071% 815-823 609.0 0.1639% 808-815 487.6 0.2047% 799-808 386.1 0.2583% 789-799 272.5 0.3656% 777-789 228.1 0.4365% 763-777 156.1 0.6365% 750-763 115.6 0.8576% 737-750 85.5 1.1561% 723-737 60.3 1.6313% 709-723 45.1 2.1692% 693-709 33.0 2.9412% 678-693 24.3 3.9526% 662-678 18.3 5.1813% 648-662 14.1 6.6225% 631-648 10.8 8.4746% 608-631 7.9 11.2360% 581-608 5.5 15.3846% 542-581 3.5 22.2222% 300-542 1.5 40.0000%   Now that we have a bad percentage per risk score band, we can define dollars at risk per risk score band as: bad rate * loan amount = dollars at risk. For example, if the loan amount in the 823-850 band is set as $10,000 you would have 0.1071% * $10,000 = $10.71 at risk from a probability standpoint. So, to have constant dollars at risk, set credit limits per band so that in all cases there is $10.71 at risk per band as indicated below. Score Range How Many Goods for 1 Bad Bad Rate Loan Amount $ at Risk 823-850 932.3 0.1071%  $   10,000.00  $   10.71 815-823 609.0 0.1639%  $     6,535.95  $   10.71 808-815 487.6 0.2047%  $     5,235.19  $   10.71 799-808 386.1 0.2583%  $     4,147.65  $   10.71 789-799 272.5 0.3656%  $     2,930.46  $   10.71 777-789 228.1 0.4365%  $     2,454.73  $   10.71 763-777 156.1 0.6365%  $     1,683.27  $   10.71 750-763 115.6 0.8576%  $     1,249.33  $   10.71 737-750 85.5 1.1561%  $        926.82  $   10.71 723-737 60.3 1.6313%  $        656.81  $   10.71 709-723 45.1 2.1692%  $        493.95  $   10.71 693-709 33.0 2.9412%  $        364.30  $   10.71 678-693 24.3 3.9526%  $        271.08  $   10.71 662-678 18.3 5.1813%  $        206.79  $   10.71 648-662 14.1 6.6225%  $        161.79  $   10.71 631-648 10.8 8.4746%  $        126.43  $   10.71 608-631 7.9 11.2360%  $          95.36  $   10.71 581-608 5.5 15.3846%  $          69.65  $   10.71 542-581 3.5 22.2222%  $          48.22  $   10.71 300-542 1.5 40.0000%  $          26.79  $   10.71   In this manner, the output is now set credit limits per band so that we have achieved constant dollars at risk across bands. Now in practice it’s unlikely that a lender will grant $1,683.27 for the 763 to 777 credit score band but this exercise illustrates how the numbers are generated. More likely, a lender will use steps of $100 or something similar to make the credit limits seem more logical to borrowers. What I like about this constant dollars at risk approach is that we aren’t really favoring any particular credit score band. Credit limits are simply set in a manner that sets dollars at risk consistently across bands. One final thought on this: Actual observations of delinquencies (not just predicted by the scores odds table) could be gathered and used to generate a new odds tables per score band. From there, the new delinquency rate could be generated based on actuals. Though, if this is done, the duration of the sample must be long enough and comprehensive enough to include both good and bad observations so that the delinquency calculation is robust as small changes in observations can affect the final results. Since the real world does not always meet our expectations, it might also be necessary to “smooth” the odds-chart so that its looks appropriate.