Data & Analytics
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.
A few weeks ago, I shared the first in a series of articles about understanding the different types of fraud and how to solve for them. In that article, I likened the fraud problem to baking. Continuing that theme, I’m going to explore first-party fraud by comparing it to biting into a cookie you think is chocolate chip, only to find that it’s filled with raisins. The raisins in the cookie were hiding in plain sight, indistinguishable from chocolate chips without a closer look, much like first-party fraudsters. What is first-party fraud? First-party fraud refers to instances when an individual makes a promise of future repayments in exchange for goods or services without the intent to repay. The fraudster might accomplish this by applying for a loan or credit card they won’t pay back, or misrepresenting their financial situation to get a more favorable rate. First-party fraud sometimes presents via “mules” or consumers who are persuaded to use their own information to obtain credit or merchandise on behalf of a larger fraud ring. This type of fraud has become especially prevalent in 2020 due to the increases in online activity for both work and purchasing. Mule activity has increased by 41% in 2020 in comparison to attack rates prior to the pandemic. – Julie Conroy, Research Director, Aite Group First-party fraud is often miscategorized as credit loss and written off as bad debt, which causes problems when businesses later try to determine how much they’ve lost to fraud versus credit risk, and then make future lending decisions. How does first-party fraud impact me? Firstly, there are often substantial losses associated with first-party fraud. According to Payments Journal, 60% of financial institutions report first-party fraud as the prominent source of fraud losses. The ranks of those who commission the attacks, as well as the mules who provide logistical support, will continue to increase at the same pace, if not more quickly, as economic conditions remain suppressed. The result will be an increase in the volume of attacks in general but with a particular emphasis on the kinds of fraud that typically accompany prolonged recessions, most notably first-party fraud.1 – Trace Fooshee, Senior Analyst, Aite Group An imperfect first-party fraud solution can also strain relationships with good customers and hinder growth. When lenders have to interpret actions and behavior to assess customers, there’s a lot of room for error and losses. Those same losses hinder growth when, as mentioned before, businesses anticipate credit losses that aren’t actually credit losses. This type of fraud isn’t a single-time event, and it doesn’t occur at just one point in the customer lifecycle. It occurs when good customers develop fraudulent intent, when new applicants who have positive history with other lenders have recently changed circumstances, or when seemingly good applicants have manipulated their identities to mask previous defaults. Finally, first-party fraud impacts how your organization categorizes and manages risk – and that’s something that touches every department. Solving the first-party fraud problem Preventing first-party fraud requires a change in how we think about the fraud problem. It starts with the ability to separate first- and third-party fraud to treat them differently. Because first-party fraud doesn’t have a victim, you can’t work with the person whose information was stolen to confirm the fraud. Instead, you’ll have to work implement a consistent monitoring system and make a determination internally when fraud is suspected. As we’ve already discussed, the fraud problem is complex. However with a partner like Experian, you have the tools required to perform a closer examination and the ability to differentiate between the types of fraud so you can determine the best course of action moving forward. In the coming weeks, I’ll continue my exploration of this topic with a dive into synthetic identity and account takeover fraud, and how a layered fraud management strategy can help you minimize customer friction to improve and deepen your relationships while preventing fraud. Contact us if you’d like to learn more about how Experian is using our identity expertise, data, and analytics to detect and prevent all types of fraud. Contact us 1Key Trends Driving Fraud Transformation in 2021 and Beyond, December 2020
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.
Fraud – it’s a word that comes up in conversations across every industry. While there’s a general awareness that fraud is on the rise and is constantly evolving, for many the full impact of fraud is misunderstood and underestimated. At the heart of this challenge is the tendency to lump different types of fraud together into one big problem, and then look for a single solution that addresses it. It’s as if we’re trying to figure out how to un-bake a terrible cake instead of thinking about the ingredients and the process needed to put them together in the first place. This is the first of a series of articles in which we’ll look at some of the key ingredients that create different types of fraud, including first party, third party, synthetic identity, and account takeover. We’ll talk about why they’re unique and why we need to approach each one differently. At the end of the series, we’ll get a result that’s easier to digest. I had second thoughts about the cake metaphor, but in truth it really works. Creating a good fraud management process is a lot like baking. We need to know the ingredients and some tried-and-true methods to get the best result. With that foundation in place, we can look for ways to improve the outcome every time. Let’s start with a look at the best known type of fraud, third party. What is third-party fraud? Third-party fraud – generally known as identity theft – occurs when a malicious actor uses another person’s identifying information to open new accounts without the knowledge of the individual whose information is being used. This type of fraud is unique from first party or synthetic identity fraud because it involves an identifiable victim that’s willing to collaborate in the investigation and resolution, for the simple reason that they don’t want to be responsible for the obligation made under their name. Third-party fraud is often the only type of activity that’s classified as fraud by financial institutions. The presence of an identifiable victim creates a high level of certainty that fraud has indeed occurred. That certainty enables financial institutions to properly categorize the losses. Since there is a victim associated with it, third party fraud tends to have a shorter lifespan than other types. When victims become aware of what’s happening, they generally take steps to protect themselves and intervene where they know their identity has been potentially misused. As a result, the timeline for third-party fraud is shorter, with fraudsters acting quickly to maximize the funds they’re able to amass before busting out. How does third-party fraud impact me? As the digital transformation continues, more and more personally identifiable information (PII) is available on the dark web due to data breaches and phishing scams. Given that half of consumers anticipate increasing their online spending in the coming year, we anticipate that the amount of PII readily available to criminals will only continue to grow. All of this will lead to identity theft and increase the risk of third-party fraud. Third-party fraud has been on businesses\' radar throughout 2020, with account takeover and account opening fraud representing high opportunities for risk. While we don’t yet know the full financial impact of COVID-19, it’s clear that it has created both opportunity—increased online presence and interaction—and need—in the form of financial distress for businesses and consumers—when it comes to third-party fraud. Solving the third-party fraud problem We’ve examined one part of the fraud problem, and it is a complex one. With Experian as your partner, solving for it isn’t. Continuing my cake metaphor, by following the right steps and including the right ingredients, businesses can detect and prevent fraud. Preventing third-party fraud involves two distinct steps. Analytics: Driven by extensive data that captures the ways in which people present their identity—plus artificial intelligence and machine learning—good analytics can detect inconsistencies, and patterns of usage that are out of character for the person, or similar to past instances of known fraud. Verification: The advantage of dealing with third-party fraud is the availability of a victim that will confirm when fraud is happening. The verification step refers to the process of making contact with the identity owner to obtain that confirmation. It does require some thought and discipline to make sure that the contact information used leads to the identity owner—and not to the fraudster. Over the coming weeks, I’ll be exploring first-party fraud, synthetic identity fraud, and account takeover fraud and how a layered fraud management strategy can help keep your business and customers safe from all types. Let us know if you’d like to learn more about how Experian is using our identity expertise, data, and analytics to detect and prevent fraud. Contact us
Enterprise Security Magazine recently named Experian a Top 10 Fraud and Breach Protection Solutions Provider for 2020. Accelerating trends in the digital economy--stemming from stay-at-home orders and rapid increases in e-commerce and government funding--have created an attractive environment for fraudsters. At the same time, there’s been an uptick in the amount of personally identifiable information (PII) available on the dark web. This combination makes innovative fraud and breach solutions more crucial than ever. Enterprise Security Magazine met with Kathleen Peters, Experian’s Chief Innovation Officer, and Michael Bruemmer, Vice President of Global Data Breach and Consumer Protection, to discuss COVID-19 digital trends, the need for robust fraud protection, and how Experian’s end-to-end breach protection services help businesses protect consumers from fraud. According to the magazine, “With Experian’s best in class analytics, clients can rapidly respond to ever-changing environments by utilizing offerings such as CrossCore® and Sure ProfileTM to identify and prevent fraud.” In addition to our commitment to develop new products to combat the rising threat of fraud, Experian is focused on helping businesses minimize the consequences of a data breach. The magazine noted that, “To serve as a one-stop-shop for data breach protection, Experian offers a wide range of auxiliary services such as incident management, data breach notification, identity protection, and call center support.” We are continuously working to create and integrate innovative and robust solutions to prevent and manage different types of data breaches and fraud. Read the full article Contact us
The shift created by the COVID-19 pandemic is still being realized. One thing that we know for sure is that North American consumers’ expectations continue to rise, with a focus on online security and their digital experience. In mid-September of this year, Experian surveyed 3,000 consumers and 900 businesses worldwide—with 300 consumers and 90 businesses in the U.S.—to explore the shifts in consumer behavior and business strategy pre- and post-COVID-19. More than half of consumers surveyed continue to expect more security steps when online, including more visible security measures in place on websites and more knowledge about how their data is being protected and stored. However, those same consumers aren’t willing to wait more than 60 seconds to complete an online transaction making it more important than ever to align your security and experience strategies. While U.S. consumers are optimistic about the economy’s recovery, they are still dealing with financial challenges and their behaviors have changed. Future business plans should take into account consumers’: High expectations of their online experience Increases in online spending Difficulty paying bills Reduction in discretionary spending Moving forward, businesses are focusing on use of AI, online security, and digital engagement. They are emphasizing revenue generation while looking into the future of online security. Nearly 70% of businesses also plan to increase their fraud management budgets in the next 6 months. Download the full North America Insights Report to get all of the insights into North American business and consumer needs and priorities and keep visiting the Insights blog in the coming weeks for a look at how trends have changed from early in the pandemic. North America Insights Report Global Insights Report
In late September, California announced a new requirement for the sale of all new passenger vehicles to be zero-emission by 2035. While that’s still 15 years away, the executive order created quite a buzz in the automotive industry, reigniting conversations about electric vehicles (EVs) and the current market penetration of the most common zero-emission vehicles. With that in mind, we wanted to take a closer look at the state of EVs—across the country and more specifically, in California—to better understand the EV market and how it’s grown over the past few years. As of Q2 2020, electric vehicles comprised just 0.312% of vehicles in operation (VIO). While EV market share seems small, there has been significant growth since Q2 2015, when they only held 0.0678% of the VIO market—meaning the growth of EVs has more than tripled (3.6x) in the last five years. But even still, other segments, such as CUVs have seen faster growth in the same time period (10% market share in Q2 2020 compared to 6.2% in Q2 2015). California sees faster EV adoption California has seen growth in EV adoption in the last decade, but it has grown exponentially in the last five years. EVs comprised 1.79% of new vehicle registrations 2015, and the percentage grew to 5.32% as of Q2 2020. Much of the growth occurred between 2017 and 2018, when market share jumped from 2.62% to 5.04% year-over-year, with the introduction of the more cost-effective Tesla Model 3. Even with that growth, California new vehicle purchases have a long way to grow to move up to 100% EV. With the popularity of the Model 3, it’s somewhat unsurprising, Tesla holds the lion’s share of the EV market in California, making up 61.9% of EVs on the road within VIO, and nationally at 64.8% share. That could potentially change down the road though. Over the next two years, numerous manufacturers have plans to introduce electric versions of popular models or introduce new EV models altogether. This not only creates competition but could also help continue to drive down vehicle cost, making EVs a more viable option for consumers. Examining costs and other factors Cost is one of the key considerations that industry experts have routinely brought up over the years as a barrier to EV adoption. While some say that maintenance and fuel are cheaper in the long run, purchasing an EV today is typically a more expensive option at the dealership. The average loan amount for an EV in California in 2019 was $40,964, compared to an average vehicle loan amount of $32,373. That said, as EV adoption has seen exponential growth in the last five years, the average price has reduced. The average loan amount for an EV in 2016 was $78,646, dropping more than $35,000 in just five years as the technology continued to mature and vehicle costs lowered. Additionally, tax incentives, particularly in California, have also helped reduce affordability concerns. Though today’s tax incentives may not be in place through 2035, California will likely need to evaluate if economic incentives are required along the way to achieving the zero-emission vehicle deadline. However, even as costs lower, there are additional challenges to be overcome. For instance, infrastructure continues to be a barrier to adoption. In a 2019 AAA study, concern over being able to find a place to charge is the top reason listed as to why respondents are unlikely to purchase an EV in the future. In addition, according to Statisa, in March 2020, the U.S. had almost 25,000 charging stations for plug-in electric vehicles, and approximately 78,500 charging outlets. Of those charging stations, nearly 30% are in California. But with continued growth of EV sales, there will be a critical need for continued infrastructure nationwide—not just in California. In addition to these considerations, many impacts of the new mandate remain unknown. California will have to navigate increased electricity demand—especially during peak hours—and increases in battery scrappage, as EVs wear out. Gas stations will need to manage a loss of revenue, while changes in fuel taxes are likely, and vehicle technicians will require new training. If increased adoption of zero emission vehicles is California’s long-term goal, this could also impact the popularity of used vehicles, which could leave dealers looking for alternative locations to sell their gasoline-powered inventory. Looking toward the future of EVs Realistically, with 15 years until the mandate takes effect, the California mandate won’t have much of an immediate effect on the industry. But it does highlight key considerations for automakers and the aftermarket moving forward. To achieve better adoption rates, automakers need to understand the barriers to success and how they impact consumer behavior. An example of this is how California has seen higher EV adoption rates as the availability of plug-in stations has increased. Continuing to find ways to lower costs and focusing on savings over the lifetime of the vehicle is will help consumers see the value of an EV. At the end of the day, automakers play a large role in moving the country toward EV adoption, so having a clear understanding of the trends can help refine strategies as we move toward an electrified future.
In what has been an unprecedented year, marked by a global pandemic and a number of economic and personal challenges for both businesses and consumers, Americans are maintaining healthy credit profiles during the COVID-19 pandemic. Experian released the 11th annual State of Credit report, which provides a comprehensive look at the credit performance of consumers across America by highlighting consumer credit scores and borrowing behaviors. This year’s report provided an extended view into how consumers are managing and repaying their debts; showing most Americans are practicing responsible credit management by reducing utilization rates, credit card balances and late payments. Even in light of the pandemic, data on American consumers across all generations shows responsible credit management including reduced utilization rates, credit card balances and late payments. “While it’s difficult to predict when the economy will return to pre-pandemic levels, we are seeing promising signs of responsible credit management, especially among younger consumers,” said Alex Lintner, group president Experian Consumer Information Services. Highlights of Experian’s State of Credit report: 2020 State of Credit Report 2019 2020 Average VantageScore[1,2] 682 688 Average number of credit cards 3.07 3.0 Average credit card balance $6,629 $5,897 Average revolving utilization rate 30% 26% Average number of retail credit cards 2.51 2.42 Average retail credit card balance $1,942 $2,044 Average nonmortgage debt [3] $25,386 $25,483 Average mortgage debt $213,599 $215,655 Average 30 - 59 days past due delinquency rates 3.9% 2.4% Average 60 - 89 days past due delinquency rates 1.9% 1.3% Average 90 - 180 days past due delinquency rates 6.8% 3.8% Though not the same, some consumers are experiencing a second economic downturn. The economic fallout stemming from COVID-19 coming after the Great Recession of 2009, which took place in the not too distant past. Silent, Boomer, Gen X and Gen Z Americans are managing responsible credit utilization rates and holding credit cards below the recommended maximum. Are the older generations more credit responsible? Average VantageScore follows rank order from oldest to youngest – though contributed to by length of time possessing credit, number of lines of credit, and other factors that drive credit score – with the Silent Generation having the highest score (729), then Boomers (716), followed by Gen X (676), Gen Y (658) and Gen Z (654). Gen X consumers have the highest average credit card balance at $7,718 and utilization at 32%, while Gen Z has the lowest average credit card balance at $2,197 and the Silent Generation has the lowest utilization at 13%. Year over year data shows positive results driven by younger borrowers. While average utilization rates dropped for every generation, the most significant decreases were seen in Gen Z borrowers who saw a 6 percent reduction in their use of available credit, followed by Millennials who saw a 5% decrease year-over-year. While Gen Z and Gen Y are carrying more credit cards than they were in 2020, their credit card balances decreased year-over-year. These factors fueled a 13-point increase in average credit scores for Gen Z and an 11-point increase for Millennials. When spliced by state, the data Minnesota had the highest credit score, while Mississippi had the lowest credit score. While the future is still uncertain, perhaps consumers can find comfort in knowing there is much they can do to improve their financial health – including their credit scores – and that there are numerous resources for them to access during these unprecedented times. “As the consumer’s bureau, we are committed to informing, guiding, and protecting consumers. Educating Americans about the factors included in their credit profile and how to manage these responsibly is of critical importance, especially on the road to economic recovery from the COVID-19 pandemic,” said Lintner. In an effort to encourage consumers to regularly monitor and understand the information in their credit reports, Experian joined forces with the other U.S. credit reporting agencies, to offer free weekly credit reports to all Americans through April 2021 via AnnualCreditReport.com. In addition to the free weekly credit report at AnnualCreditReport.com, Experian also offers consumers free access to their credit report and ongoing credit monitoring at Experian.com. Additional credit education resources and tools Experian’s #CreditChat: Hosted by @Experian on Twitter with financial experts every Wednesday at 3 p.m. Eastern time The Ask Experian blog: Find answers to common questions, advice and education about credit Experian Boost: Add positive telecom and utility payments to your Experian credit report for an opportunity to improve your credit scores experian.com/consumer-education-content/ experian.com/coronavirus 1VantageScore is a registered trademark of VantageScore Solutions, LLC. 2VantageScore range is 300 to 850.
As industry experts are still unsure when the economy will fully recover, re-entry into marketing preapproved credit offers seems like a far-off proposal. However, several of the top credit card issuers are already mailing prescreen offers, with many other lenders following suit. When the time comes for organizations to resume, or even expand this type of targeting, odds are that the marketing budget will be tighter than in the past. To make the most of the limited available marketing spend, lenders will need to be more prescriptive with their selection process to increase response rates on fewer delivered offers. Choosing the best candidates to receive these offers, from a credit risk perspective, will be critical. With delinquencies being suppressed due to CARES Act reporting guidelines, identifying consumers with the ability to repay will require additional assessment of recent credit behavior metrics, such as actual payment amounts and balance migration. Along with the presence of explicit indicators of accommodated trades (trades affected by natural disaster, trades with a balance but no scheduled payment amount) on a prospect’s credit file, their recent trends in payments and balance shifts can be integral in determining whether a prospect has been adversely impacted by today’s economic environment. Once risk criteria have been developed using a mix of bureau scores (like VantageScore®), traditional credit attributes and trended attributes measuring recent activity, additional targeting will be critical for selecting a population that’s most likely to open the relevant trade type. For credit cards and personal installment loans, response performance can be greatly improved by aligning product offers with prospects based on their propensity to revolve, pay in full each month or consolidate balances. Additionally, the process to select final prospects should integrate a propensity to open/respond assessment for the specific offering. While many lenders have custom models developed on previous internal response performance, off-the-shelf propensity to open models are also available to provide an assessment of a prospect’s likelihood to open a particular type of trade in the coming months. These models can act as a fast-start for lenders that intend to develop internal custom models, but don’t have the response performance within a particular product/geography/risk profile. They are also commonly used as a long-term solution for lenders without an internal model development team or budget for an outsourced model. Prescreen selection best practices Identify geography and traditional credit risk assessment of the prospect universe. Overlay attributes measuring accommodated trades and recent payment/balance trends to identify prospects with indications of ability to pay. Segment the prospect universe by recent credit usage to determine products that would resonate. Make final selections using propensity to open model scores to increase response rates by only making offers to consumers who are likely looking for new credit offers. While the best practices listed above don’t represent a risk-free approach in these uncertain times, they do provide a framework for identifying prospects with mitigated repayment risk and insights into the appropriate credit offer to make and when to make it. Learn about in the market models Learn about trended attributes VantageScore is a registered trademark of VantageScore Solutions, LLC.
Profitability analysis is one of the most powerful analytics tools in business and strategy development. Yet it’s underrated, deemed too complicated and often ignored. A chief lending officer may state that the goal of strategy development is to increase approvals or to reduce losses. Each one of these goals has an impact generally inversely on each other. That impact may be consequential, and evaluating the effects requires deeper thought and discipline. I propose that the benefits of a profitability analysis in strategy development are worth the additional effort, time and cost. Profitability analysis provides a disciplined framework for making business decisions. For financial companies, a simple profit and loss (P&L) statement will identify interest income, subtract losses and arrive at a risk-adjusted yield. A more robust P&L statement will include interest expense, loss reserves, recovery, fees and other income, operating expenses, other cost per account, and net income. Whether simplified or fully loaded, a P&L analysis used in strategy development must provide a clear and informative representation of key performance metrics and risks. The most important benefit of a profitability analysis is its inherent ability to quantify the trade-offs between risk and rewards. In the P&L terminology, we mean the trade-off between expenses and revenue or losses and interest income. Understanding trade-offs allows companies to make informed decisions and explore serious alternatives. The net income is a concise and elegant metric that captures the impact of various and sometimes competing business objectives. Consider different divisions within a financial organization. Each division has its own specific and measurable objective. Marketing’s goal is to increase loan approvals while Risk is tasked with managing losses. Operations looks to improve efficiencies while IT aims to provide stable, reliable and accurate systems infrastructure. Legal and Compliance ensure regulatory compliance across the entire organization. Each division working to achieve its objectives creates externalities — each division’s actions may not fully incorporate costs imposed on other divisions. For example, targeting highly responsive consumers for a loan product achieves higher loan approvals and may in turn lead to higher credit risk losses. A P&L analysis imposes the discipline for each division to internalize costs and lead to a favorable and efficient outcome for the organization. The challenge with profitability analysis in strategy development is how to develop a good P&L statement. We look to historical data to define assumptions and calibrate inputs to the P&L. There will be uncertainty and concerns regarding the reliability and quality of such data. Organizations don’t regularly conduct test and control experiments or champion and challenger strategies that provide actual performance information on specific areas of studies. Though imperfect, historical data provides a starting foundation for profitability analysis. We augment historical data with predictive credit attributes, industry experience and understanding consumer behavior and incentives. For example, to estimate interest income we may utilize estimated interest rates combined with balance propensity behavior, such as a balance revolver or transactor. To estimate losses on declined population that may be considered for approval, we infer on-us performance using off-us performance with other lenders. Defining assumptions is tedious, hard work and full of uncertainty. This exercise once again imposes the discipline required of organizations to know in detail the characteristics of their products and businesses that make them relevant to consumers. We generate P&L simulations using a set of assumptions, acknowledge the data limitations and evaluate recommendations. A profitability analysis is useful in both times of economic expansion and contraction. A P&L analysis is valuable when evaluating strategies across the customer life cycle. Remember, we live in a world of trade-offs and choices are inevitable. In the prospecting and acquisition life cycle, a P&L analysis provides insights on approval expansion and the consequences of higher credit losses. Alternatively, tighter lending criteria will have a direct impact on balance growth and interest income with lower losses. In account management, a P&L analysis provides estimates on expanded account authorization limits and the effect on activation and usage. In collections, a P&L analysis provides valuation on recoveries and operational costs. These various assessments are quantified in the P&L and allows the organization to identify other mechanisms such as marketing campaigns, customer services or technology investments in support of the organization’s goals and mission. Organizations face a full spectrum of opportunities and risks. We propose a profitability analysis to evaluate business trade-offs, navigate the marketplace, and continue to provide relevant financial products and services to consumers and businesses. Learn more
Consumers are taking advantage of new car incentives, low interest rates and longer-term loans in order to ensure that their vehicle purchase is manageable.
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