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.

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 the wake of unprecedented unemployment fraud since the start of COVID-19, Experian announced it was selected as the exclusive partner for identity and fraud verification for the Unemployment Insurance (UI) Integrity Center’s centralized Identity Verification (IDV) capability. IDV is available to state agencies at no cost through UI Integrity Center, which is operated by the National Association of Workforce Agencies (NASWA) in partnership with the U.S. Department of Labor.   With the Federal Bureau of Investigations (FBI) reporting a spike in fraudulent unemployment insurance claims complaints related to COVID-19, it’s more important than ever for state agencies to use innovative solutions to verify identities that are applying for unemployment insurance to protect consumers. If improper unemployment insurance payments are made to fraudsters, the efforts of the CARES Act could be largely wasted.   The IDV capability leverages Experian’s Precise IDTM to provide a centralized identity verification and proofing solution. Precise ID combines identity analytics with advanced fraud risk models to distinguish various types of fraud, which can help state agencies maximize time and resources. When state agencies submit claims, the IDV solution will return ID theft scoring and associated cause codes, enabling them to assess whether a claim may be fraudulent.   “Due to the COVID-19 health crisis, unemployment is high, with over roughly 60 million Americans filing for unemployment since March,” said Robert Boxberger, president of Experian’s Decision Analytics in North America. “At Experian, we’re proud to have a strong culture dedicated to continuous innovation that helps protect consumers’ financial health. We’re taking that same consumer focus and helping make the unemployment insurance application process more efficient and safer for constituents.”   The Integrity Data Hub (IDH) is a robust, multi-state data system that contains a continuously expanding set of sources to provide advanced cross-matching and analytic capabilities to states. It is designed to be easily implemented by any state Unemployment Insurance agency, regardless of claim volume, technology, or access to internal resources. The IDH was designed and built using the latest National Institute of Standards and Technology IT security standards, including the use of asymmetric encryption and other techniques to ensure the security of sensitive data.   “We’re excited to partner with Experian and utilize its Precise ID solution to assist states in mitigating fraud during these unprecedented times,” said Scott Sanders, NASWA Executive Director. “States are finding this to be a very valuable tool and we are pleased that we can offer this solution to states through our partnership with the U.S. Department of Labor.” Read Press Release Learn More About Precise ID

No one can deny that the mortgage and real estate industries have been uniquely affected by COVID-19. Social distancing mandates have hindered open house formats and schedules. Meanwhile, historically low-interest rates, pent-up demand and low housing inventory created a frenzied sellers’ market with multiple offers, usually over-asking. Added to this are the increased scrutiny of how much borrowers will qualify and get approved for with tightened investor guidelines, and the need to verify continued employment to ensure a buyer maintains qualifying status through closing. As someone who’s spent more than 15 years in the industry and worked on all sides of the transaction (as a realtor and for direct lenders), I’ve lived through the efforts to revamp and digitize the process. However, it wasn’t until recently that I purchased my first home and experienced the mortgage process as a consumer. And it was clear that, for most lenders, the pandemic has only served to shine a light on a still somewhat fragmented mortgage process and clunky consumer experience. Here are three key components missing from a truly modernized mortgage experience: Operational efficiency Knowing that the industry had made moves toward a digital mortgage process, I hoped for a more streamlined and seamless flow of documents, loan deliverables and communication with the lender. However, the process I experienced was more manual than expected and disjointed at times. Looking at a purchase transaction from end to end, there are at least nine parties involved: buyer, seller, realtors, lender, home inspectors/inspection vendors, appraiser, escrow company and notary. With all those touchpoints in play, it takes a concerted effort between all parties and no unforeseen issues for a loan to be originated faster than 30 days. Meanwhile, the opposite has been happening, with the average time to close a loan increasing to 49 days since the beginning of the pandemic, per Ellie Mae’s Origination Insights Report. Faster access to fresher data can reduce the time to originate a mortgage. This saves resource hours for the lender, which equates to savings that can ultimately be passed down to the borrower. Digital adoption There are parts of the mortgage process that have been digitized, yes. However, the mortgage process still has points void of digital connectivity for it to truly be called an end-to-end digital process. The borrower is still required to track down various documents from different sources and the paperwork process still feels very “manual.” Printing, signing and scanning documents back to the lender to underwrite the loan add to the manual nature of the process. Unless the borrower always has all documents digitally organized, requirements like obtaining your W-2’s and paystubs, and continuously providing bank and brokerage statements to the lender, make for an awkward process. Modernizing the mortgage end-to-end with the right kind of data and technology reduces the number of manual processes and translates into lower costs to produce a mortgage. Turn times are being pushed out when the opposite could be happening. A streamlined, modernized approach between the lender and consumer not only saves time and money for both parties, it ultimately enables the lender to add value by providing a better consumer experience. Transparency Digital adoption and better digital end-to-end process are not the only keys to a better consumer experience; transparency is another integral part of modernizing the mortgage process. More transparency for the borrower starts with a true understanding of the amount for which one can qualify. This means when the loan is in underwriting, there needs to be a better understanding of the loan status and the ability to better anticipate and be proactive about loan conditions. Additionally, the lender can profit from gaining more transparency and visibility into a borrower’s income streams and assets for a more efficient and holistic picture of their ability to pay upfront. This allows for a more streamlined process and enables the lender to close efficiently without sacrificing quality underwriting. A multitude of factors have come into play since the beginning of the pandemic – social distancing mandates have led to breakdowns in a traditionally face-to-face process of obtaining a mortgage, highlighting areas for improvement. Can it be done faster, more seamlessly? Absolutely. In ideal situations, mortgage originators can consistently close in 30 days or less. Creating operational efficiencies through faster, fresher data can be the key for a lender to more accurately assess a borrower’s ability to pay upfront. At the same time, a digital-first approach enhances the consumer experience so they can have a frictionless, transparent mortgage process. With technology, better data, and the right kind of innovation, there can be a truly end-to-end digital process and a more informed consumer. Learn more

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.