Models & Scores
At some point a lender may need to issue an RFI or an RFP for a credit decisioning system. In this latest installment of “working with vendors” let’s dive into some best practices for writing RFIs and RFPs that will help you more quickly and efficiently understand the capabilities of a vendor. First, have one person (or at most a very small group) review the document before it goes out to vendors. Too often these kinds of documents seem like they’re just cut and pasted together without any concern if they paint a coherent picture. If it’s worth the time to write an RFI/RFP, then it’s worth the time to get it right so that the vendor responses make sense. If your document paints an inconsistent picture, a vendor may not know what products will best serve your requirements. In turn, precious time will be wasted in discussions around what’s being proposed. Here are some things to make clear in the document: For what part of the credit life cycle does this RFI/RFP apply (prospecting, origination, account management or collections)? If the request covers more than one part of the life cycle, make clear which questions apply to which part of the life cycle. Do you need a system that processes in batch or real-time requests (or both)? For example, a credit card account management solution can process accounts in batch (for proactive line management), in real time (for reactive requests) or possibly even both. Let the vendor know what it is you’re trying to do, as there may be different systems involved in processing these requests. Do you want this system hosted at the vendor, a third party (like AWS, Azure, etc.) or installed on premises? If you have a preference, let the vendor know. If you have no preference, ask the vendor what they can support. In general, consider playing down or skip detailed pricing questions. There’s nothing wrong with asking for a price range. For credit decisioning systems, detailed pricing is difficult for the vendor since there are often high levels of unknown customization to do. A better question might be, “What things will the vendor have to know in order to accurately price the solution? What are the logical next steps to get more accurate pricing? What’s the typical range of pricing in a solution such as this and what drives that range?” Will you be acting as an aggregator? Sometimes systems are created as front ends to several lenders. For example, a client may want to create a website where a borrower can “shop” among several lenders. This is certainly doable but carries with it a whole host of legal, compliance, business and technical questions. In my opinion, I’d skip the RFI/RFP in this situation and have a robust sit down directly with the vendors. This option will likely be far more productive. Ask more open-ended questions. “How does the solution perform task X?” as opposed to, “Do you support Y?” Often, there’s more than one way to accomplish a task. Asking more open-ended questions will yield a more comprehensive answer from the vendor rather than a simple yes or no response. It also gives you the opportunity to learn about the latest decisioning techniques. Be careful that you have not copied old RFP questions that are no longer relevant. I’ve had clients ask if we support Bernoulli Boxes (a mid-80s kind of floppy disk), or whether we support OS/2, etc. I’ve even had questions about supporting a particular printer. These kinds of questions are centered on the support of the operating system and not a particular vendor’s credit decisioning software. Instead of asking yes/no technology questions, ask for a typical sample architecture. Ask what kinds of APIs are supported (REST, SOAP/XML, etc.). Ask about the solution’s capabilities to call third-party systems (both internal and external). Ask fewer, but more in-depth questions. If the solution needs screens, be clear which screens you’re talking about. Do you need screens to make rule adjustments or configuration changes? Do you need screens for manual review or some sort of case management? Do you need consumer-facing screens where borrowers can type in their application data? If you need screens, be clear on the task the screens should perform. If you have particular concerns, ask them in an open-ended way. For example, “The solution will have to exchange file-based data with a mainframe. How can your solution best satisfy this requirement?” In general, state your requirement not the technology to use. A preamble or brief executive summary is useful to get the big picture across before the vendor delves into any questions. A paragraph or two can go a long way to help the vendor better assess your requirements and provide more meaningful answers to you. This works well because it’s easier to give the big picture in a few paragraphs as opposed to sprinkled around in multiple questions. To summarize, be clear on your requirements and provide a more open-ended format for the vendor to respond. This will save both you and the vendor a lot of time. In section three, I’ll cover evaluating vendors.
Perhaps your loan origination system (LOS) doesn’t have the flexibility that you require. Perhaps the rules editor can’t segment variables in the manner that you need. Perhaps your account management system can’t leverage the right data to make decisions. Or perhaps your existing system is getting sunset. These are just some of the many reasons a company may want to investigate the marketplace for new credit decisioning software. But RFIs and RFPs aren’t the only way to find new decisioning software. After working in credit services decisioning for over 20 years — and seeing hundreds of RFPs and presenting thousands of solutions and proposed architectures — I’ve formed a few opinions about how I would go about things if I were in the customer’s seat and have broken that into a three-part series. Part 1 will cover everything up to issuing an RFI or RFP. Part 2 will discuss writing an RFP or RFI. Part 3 will cover evaluating vendors. Let’s go. If you’re looking to buy new decisioning software, your first inclination might be to issue an RFI or an RFP. However, that may not be the best idea. Here’s an issue that I frequently see. Vendors are constantly evolving their products. How a product did feature X two years ago might be completely different now. The terminology that the industry uses might have changed, and new capabilities (like machine learning) might have come about and changed whole sets of functionalities. The first decision point is to ask yourself a question, “Do I know exactly what I want or am I trying to generally learn what is out there?” An RFI or RFP isn’t always the greatest way to exchange information about a product. From a vendor’s standpoint, a feature-rich, complex system has to be reduced down to a few text answers or (worst yet) a series of yes or no answers. It all boils down to nuance. On many occasions, I’ve faced a dilemma when answering an RFP question, “This question is unclear; if the customer means X, the answer is yes; if they mean Y, the answer is no.” If I were in a room with the customer, I could ask them the question, they could provide clarification and I could then provide the accurate answer. There would be more opportunity to have a back and forth, “Oh when you said X, this is what you meant ….” All of that back and forth is lost with an RFI or RFP, or at least delayed until the (hopefully selected) vendor gets a chance to present in front of a live audience. Also, consider that vendors are eager to educate you about their product. They know exactly how the product works and they’re happy to answer your questions. It’s perfectly reasonable to go to a vendor with prewritten questions and thoughts and to pose those questions during a call or demonstration with the vendor. Nothing would prevent a customer from using the same questions for each vendor and evaluating them based on their answers. All of this can be done without issuing an RFI or RFP. In conclusion, I’d offer the following points to think about before issuing an RFI or RFP: A customer can provide questions that they want answered during a demonstration of a credit decisioning product. These same questions can be used to provide an initial assessment of several vendors. A customer’s understanding of a vendor’s capabilities is likely 10x faster and deeper with an interactive session versus reading the answers in a questionnaire. Nuanced and follow-up questions can be asked to gather a complete understanding. Alternative solutions can be explored. This exercise doesn’t have to replace an RFP but instead can better inform the customer about the questions they need answered in order to issue an RFP. Don’t be afraid to talk to a vendor, even if you’re not sure what you want in a new product. In fact, talk to several vendors. More than likely, you’ll learn a lot more via a discussion than you will via an RFI questionnaire. What’s good about an RFI or RFP is coming in with prepared questions. That way, you can judge each vendor using the same criteria but, if possible, get the answers to those questions via an interactive session with the vendors. Next: How to write an effective RFP or RFI.
With 2020 firmly behind us and multiple COVID-19 vaccines being dispersed across the globe, many of us are entering 2021 with a bit of, dare we say it, optimism. But with consumer spending and consumer confidence dipping at the end of the year, along with an inversely proportional spike in coronavirus cases, it’s apparent there’s still some uncertainty to come. This leaves businesses and consumers alike, along with fintechs and their peer financial institutions, wondering when the world’s largest economy will truly rebound. But based on the most recent numbers available from Experian, fintechs have many reasons to be bullish. In this 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 and responsible spending habits. While growth expectedly slowed towards the end of the year, Q4 of 2020 saw solid job gains in the US labor market, with 883,000 jobs added through November and the US unemployment rate falling to 6.7%. Promisingly, one of the sectors hit hardest by the pandemic, the leisure and hospitality industry added back the most jobs of all sectors in October: 271,000. Additionally, US home sales hit a 14-year high fueled by record low mortgage rates. And finally, consumer sentiment rose to the highest level (81.4%) since March 2020. Not only are these promising signs of continued recovery, they illustrate there are ample market opportunities now for fintechs and other financial institutions. “It’s been encouraging to see many of our fintech partners getting back to their pre-COVID marketing levels,” said Experian Account Executive for Fintech Neil Conway. “Perhaps more promising, these fintechs are telling me that not only are response rates up but so is the credit quality of those applicants,” he said. More plainly, if your company isn’t in the market now, you’re missing out. Here are the four steps fintechs should take to reenter the lending marketing intelligently, while mitigating as much risk as possible. Re-do Your Portfolio Review Periodic portfolio reviews are standard practice for financial institutions. But the health crisis has posted unique challenges that necessitate increased focus on the health and performance of your credit portfolio. If you haven’t done so already, doing an analysis of your current lending portfolio is imperative to ensure you are minimizing risk and maximizing profitability. It’s important to understand if your portfolio is overexposed to customers in a particularly hard-hit industry, i.e. entertainment, or bars and restaurants. At the account level there may be opportunities to reevaluate customers based on a different risk appetite or credit criteria and a portfolio review will help identify which of your customers could benefit from second chance opportunities they may not have otherwise been able to receive. Retool Your Data, Analytics and Models As the pandemic has raged on, fintechs have realized many of the traditional data inputs that informed credit models and underwriting may not be giving the complete picture of a consumer. Essentially, a 720 in June 2020 may not mean the same as it does today and forbearance periods have made payment history and delinquency less predictive of future ability to pay. To stay competitive, fintechs must make sure they have access to the freshest, most predictive data. This means adding alternative data and attributes to your data-driven decisioning strategies as much as possible. Alternative data, like income and employment data, works to enhance your ability to see a consumer’s entire credit portfolio, which gives lenders the confidence to continue to lend – as well as the ability to track and monitor a consumer’s historical performance (which is a good indicator of whether or not a consumer has both the intention and ability to repay a loan). Re-Model Your Lending Criteria One of the many things the global health crisis has affirmed is the ongoing need for the freshest, most predictable data inputs. But even with the right data, analytics can still be tedious, prolonging deployment when time is of the essence. Traditional models are too slow to develop and deploy, and they underperform during sudden economic upheavals. To stay ahead in times recovery or growth, fintechs need high-quality analytics models, running on large and varied data sets that they can deploy quickly and decisively. Unlike many banks and traditional financial institutions, fintechs are positioned to nimbly take advantage of market opportunities. Once your models are performing well, they should be deployed into the market to actualize on credit-worthy current and future borrowers. Advertising/Prescreening for Intentional Acquisition As fintechs look to re-enter the market or ramp up their prescreen volumes to pre-COVID levels, it’s imperative to reach the right prospects, with the right offer, based on where and how they’re browsing. More consumers than ever are relying on their phones for browsing and mobile banking, but aligning messaging and offers across devices and platforms is still important. Here’s where data-driven advertising becomes imperative to create a more relevant experience for consumers, while protecting privacy. As 2021 rolls forward, there will be ample chance for fintechs to capitalize on new market opportunities. Through up-to-date analysis of your portfolio, ensuring you have the freshest, predictive data, adjusting your lending criteria and tweaking your approach to advertising and prescreen, you can be ready for the opportunities brought on by the economic recovery. How is your fintech gearing up to re-enter the market? Learn more
Credit cards are the most widely available credit products offered to millions of consumers today. For many consumers, owning a credit card is a relatively simple step toward establishing credit history and obtaining access to other lending products later in life. For credit unions, offering a credit card to members expands and enriches the credit relationship. In today’s environment, some credit unions don’t view credit cards as an integral part of their member service. I propose that the benefits of credit cards in a credit union portfolio are impactful, meaningful and fully align to member outreach and community service. A high-level review of risk-adjusted yields across three of the most common retail products offered by credit unions show that credit cards can be very profitable. The average APR of credit cards as of Q3 2020 is just slightly below personal loans. While charge-offs as a percentage of balances are more than double of personal loans, the estimated risk-adjusted yield is still elevated and is 1.8 times higher than auto loan and leases. See Table 1. Table 1. Estimated average risk-adjusted yield for auto loan and lease, personal loan, and credit card for credit unions Auto loan and lease Personal loan Credit card Average APR 5.21% 12.05% 11.26% Charge-offs as % of balances (annualized) 0.28% 0.89% 1.98% Risk-adjusted yield 4.93% 11.16% 9.28% Notes: Average APR of auto loans and leases, personal loans, and charge-off information as of Q3 2020 was extracted from Experian-Oliver Wyman IntelliViewSM Market Intelligence Reports. IntelliView Market Intelligence Reports, Dec. 22, 2020, experian.com/decision-analytics/market-intelligence/intelliview. Average APR of credit card as of Q3 2020 was extracted from National Credit Union Administration website. Credit Union and Bank Rates 2020 Q3, Dec. 22, 2020, https://www.ncua.gov/analysis/cuso-economic-data/credit-union-bank-rates/credit-union-and-bank-rates-2020-q3. Estimated risk-adjusted yield is calculated as the difference between average APR and charge-offs. A profitable retail product allows a credit union to share those profits back with members consistent with its mission of promoting and supporting the financial health and well-being of its members. Credit cards provide diversification of income streams. Income diversification provides a level of stability across cyclical economic conditions when some types of credit exposures may perform poorly, while others may be more stable. When combined with sound and effective risk governance, credit diversification allows lenders to mitigate levels of concentration risks in their aggregate portfolio. Offering credit cards to members is one avenue to grow loan volume and achieve scale that’s sufficiently manageable for credit unions. Scale is particularly important today as it’s needed to fund technology investments. The pandemic accelerated the massive movement toward digital engagement, and scale makes technology investments more cost-effective. When lenders become more productive and efficient, they further lower the cost of credit products to members. (Stovall, Nathan. Dec. 14, 2020. Desire to compete with megabanks driving more U.S. regional bank M&A — KBW CE blog. https://platform.mi.spglobal.com/web/client?auth=inherit#news/.) The barriers to offering credit cards have moderately declined. Technology partners, payment processors and specialized industry companies are available in the marketplace. The biggest challenge for credit unions and lenders is credit risk management. To be profitable and to stay relevant, credit cards require a relatively sophisticated risk management framework of underwriting criteria, pricing, credit line management, operations and marketing. Industry and specialized support for launching and managing credit cards is widely available and accessible. Analytics play an essential role in managing credit cards. With an average active life of approximately five years, credit card portfolios need regular and periodic performance reviews to manage inherent risk and to identify opportunities for growth and profitability. Account management for credit cards is equally as important as underwriting. Credit line management, authorization, activation and retention have significant impact to the performance of existing accounts. Continuous engagement with members is critical and has taken on a new meaning lately. Credit cards provide an opportunity to engage members, to grow lending relationships and to support financial well-being. Marketing and meaningful card offers drive card usage and relevance. They’re critical components in customer communication and service. The benefits of credit cards contribute positively to a credit union portfolio. With sound and effective risk management practices, credit cards are profitable, help diversify income streams, grow loan volume and support member credit needs.
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
This is the fourth in a series of blog posts highlighting optimization, artificial intelligence, predictive analytics, and decisioning for lending operations in times of extreme uncertainty. The first post dealt with optimization under uncertainty, the second with predicting consumer payment behavior, and the third with validating consumer credit scores. This post describes some specific Experian solutions that are especially timely for lenders strategizing their response to the COVID Recession. Will the US economy recover from the pandemic recession? Certainly yes. When will the economy recover? There is a lot more uncertainty around that question. Many people are encouraged by positive indicators, such as the initial rebound of the stock market, a return of many of the jobs lost at the beginning of the pandemic, and a significant increase in housing starts. August’s retail spending and homebuilder confidence are very encouraging economic indicators. Other experts doubt that the “V-shaped” recovery can survive flare-ups of the virus in various parts of the US and the world, and are calling for a “W-shaped” recovery. Employment indicators are alarming: many people remain out of work, some job losses are permanent, and there are more initial jobless claims each week now than at the height of the Great Recession. Serious hurdles to economic recovery may remain until a vaccine is widely available: childcare, urban transportation, and global trade, for example. I’m encouraged by the resilience of many of our country’s consumer lenders. They are generally responding well to these challenges. If past recessions are a guide, some lenders will not survive these turbulent times. This time, many lenders—whether or not they have already adopted the CECL accounting standards—have been increasing allowances for their anticipated credit losses. At least one rating agency believes major banks are prepared to absorb those losses from earnings. The lenders who are most prepared for the eventual recovery will be those that make good decisions during these volatile times and take action to put themselves in the best position in anticipation of the recovery that will certainly follow. The best lenders are making smart investments now to be prepared to capitalize on future opportunities. Experian’s analytics and consulting experts are continuously improving our suite of solutions that help consumer lenders and others assess consumer behavior and respond quickly to the rapidly fluctuating market conditions as well as changing regulations and credit reporting practices. Our newly announced Economic Response and Recovery Suite includes the ABCD’s that lenders need to be resilient and competitive now and to prepare to thrive during the eventual recovery: A – Analytics. As I’ve written about in prior blog posts, data is a prerequisite to making good business decisions, but data alone is not enough. To make wise, insightful decisions, lenders need to use the most appropriate analytical techniques, whether that means more meaningful attributes, more predictive and compliant credit scores, more accurate and defensible loss forecasting solutions, or optimization systems that help develop strategies in a world where budgets, regulations, and other constraints are changing. For example, Experian has released a set of Spotlight 2020 Attributes that help consumer lenders create a positive experience for customers who have received an accommodation during the pandemic. In many cases motivated by the new race to improve customer experience online, and in other cases as a reaction to new and creative fraud schemes, some clients are using this period as an opportunity to explore or deploy ethical and explainable Artificial Intelligence. B – Business Intelligence. Credit bureaus like Experian are uniquely situated to understand the impact of the COVID recession on America’s consumers. With impact reports, dashboards, and custom business intelligence solutions, lenders are working during the recession to gain an even better understanding of their current and prospective customers. We’re helping many of them to proactively help consumers when they need it most. For example, lenders have turned to us to understand their customer’s payment hierarchy—which bills they pay first when times are tough. Our free COVID-19 US Business Risk Index helps make lending options available to the businesses who need them most. And we’ve armed lenders with recommendations for which of our pre-existing attributes and scores are most helpful during trying times. Additional reporting tools such as the Auto Market Tracker, Ascend Market Insights Dashboard, and the weekly economic update video provide businesses with information on new market trends—information that helps them respond during the recession and promises to help them grow during the eventual recovery. C – Consulting. It’s good to turn data into information and information into insight, but how do these lenders incorporate these insights in their business strategies? Lenders and other businesses have been turning to Experian’s analytics and Advisory services consultants to unlock the information hidden in credit and other data sources—finding ways to make their business processes more efficient and more effective while developing quick response plans and more long-term recovery strategies. D – Delivery. Decision science is the practice of using advanced analytics, artificial intelligence, and other techniques to determine the best decision based on available data and resources. But putting those decisions into action can be a challenge. (Organizations like IBM and Gartner estimate that a great majority of data science projects are never put into production.) Experian technologies—from our analytics platform to our attribute integration and decision management solutions ensure that data-driven decisions can be quickly implemented to make a real difference. Treating each customer optimally has a number of benefits—whether you are trying to responsibly grow your portfolio, reduce credit losses and allowances, control servicing costs, or simply staying in compliance during dynamic times. In the age of COVID, IT departments have placed increased priority on agility, security, customer experience, and cost control, and appreciate cloud-first approach to deploying analytics. It’s too early to know how long this period of extreme uncertainty will last. But one thing is certain: it will come to an end, and the economy will recover someday. I predict that many of the companies that make the best use of data now will be the ones who do the best during the recovery. To hear more ways your organization can navigate this downturn and the recovery to follow, please watch our on-demand webinar and check out our Economic Response and Recovery Suite. Watch the Webinar
In today’s uncertain economic environment, the question of how to reduce portfolio volatility while still meeting consumers’ needs is on every lender’s mind. With more than 100 million consumers already restricted by traditional scoring methods used today, lenders need to look beyond traditional credit information to make more informed decisions. By leveraging alternative credit data, you can continue to support your borrowers and expand your lending universe. In our most recent podcast, Experian’s Shawn Rife, Director of Risk Scoring and Alpa Lally, Vice President of Data Business, discuss how to enhance your portfolio analysis after an economic downturn, respond to the changing lending marketplace and drive greater access to credit for financially distressed consumers. Topics discussed, include: Making strategic, data-driven decisions across the credit lifecycle Better managing and responding to portfolio risk Predicting consumer behavior in times of extreme uncertainty Listen in on the discussion to learn more. Experian · Effective Lending in the Age of COVID-19
Do consumers pay certain types of credit accounts before others during financial distress? For instance, do they prioritize paying mortgage bills over credit card bills or personal loans? During the Great Recession, the traditional notion of payment priority among multiple credit accounts was upended, throwing strategies employed by financial institutions into disarray. Similarly, current circumstances in the context of COVID-19 might cause sudden shifts in prioritization of payments which might have a dramatic impact on your credit portfolio. Financial institutions would be better able to forecast and control exposure to credit risk, and to optimize servicing practices such as forbearance and collections treatments if they could understand changing customer payment behaviors and priorities of their existing customers across all open trades. Unfortunately, financial institutions’ data—including their own behavioral data and refreshed credit bureau data--are limited to information about their own portfolio. Experian data provides insight which complements the financial institutions’ data expanding understanding of consumer payment behavior and priorities spanning all trades. Experian recently completed a study aimed at providing financial institutions valuable insights about their customer portfolios prior to COVID-19 and during the initial months of COVID-19. Using the Experian Ascend Technology Platform™, our data scientists evaluated a random 10% sample of U.S. consumers from its national credit file. Data from multiple vintages were pulled (June 2006, June 2008 and February 2018) and the payment trends were studied over the subsequent performance period. Experian tabulated the counts of consumers who had various combinations of open and active trade types and selected several trade type combinations with volume to differentiate performance by trade type. The selected combinations collectively span a variety of scenarios involving six trade types (Auto Loans, Bankcard, Student Loan, Unsecured Personal Loans, Retail Cards and First Mortgages). The trade combinations selected accommodate a variety of lenders offering different products. For each of the consumer groups identified, Experian calculated default rates associated with each trade type across several performance periods. For brevity, this blog will focus on customers identified as of February 2018 and their subsequent performance through February 2020. As the recession evolves and when the economy eventually recovers, we will continue to monitor the impacts of COVID-19 on consumer payment behavior and priorities and share updates to this analysis. Consumers with Bankcard, Mortgage, Auto and Retail accounts Among consumers having open and recently active Bankcard, Mortgage, Auto and Retail accounts, bankcard delinquency was highest throughout the 24-month performance window, followed by Retail. Delinquency rates for Auto and Mortgage were the lowest. During the pre-COVID-19 period, consumers paid their secured loans before their unsecured loans. As demonstrated in the table below, customer payment priority was stable across the entire 24-month period, with no significant shift in payment priorities between trade types. Consumers with Unsecured Personal Loan, Retail Card and Bankcard accounts. Among consumers having open and recently active Unsecured Personal Loan, Retail Card and Bankcard accounts, consumers are likely to pay unsecured personal loans first when in financial distress. Retail is the second priority, followed by Bankcard. KEY FINDINGS From February 2018 through April 2020, relative payment priority by trade type has been stable Auto and Mortgage trades, when present, show very high payment priority Download the full Payment Hierarchy Report here. Download Now Learn more about how Experian can create a custom payment hierarchy for the customers in your own portfolio, contact your Experian Account Executive, or visit our website.
Account management is a critical strategy during any type of economy (pro-cycle, counter-cycle, cycle neutral). In times like these, marked by economic volatility, it is an effective way to identify which parts of your portfolio and which of your consumers need the most attention. Check out this podcast where Cyndy Chang, Senior Director of Product Management, and Craig Wilson, Senior Director of Consulting, discuss the foundational elements of account management, best practices and use cases. Account management today looks very different than what it has been during over a decade of growth proactive; account review is a critical part of navigating the path forward. Questions that need to be addressed include: Do you have the right data? Are you monitoring between data loads? Are you reviewing accounts at the frequency that today’s changing demands require? Listen in on the discussion to learn more. Experian · Look Ahead Podcast
Today, Experian and Oliver Wyman launched the Ascend Portfolio Loss ForecasterTM, a solution built to help lenders make better decisions – during COVID-19 and beyond – with customized forecasts and macroeconomic data. Phrases like “the new normal,” “unprecedented times,” and “extreme economic volatility” have flooded not only media for the last few months, but also financial institutions’ strategic discussions regarding plans to move forward. What has largely been crisis response is quickly shifting to an urgent need to answer the many questions around “Will we survive this crisis?,” let alone “What’s next?” And arguably, we’ve entered a new era of loss forecasting. After the longest period of economic growth in post-war U.S. history, previously built models are not sufficient for the unprecedented and sudden changes in economic conditions due to COVID-19. Lenders need instant insights to assess impact and losses to their portfolios. The Ascend Portfolio Loss Forecaster combines advanced modeling from Oliver Wyman, pandemic-specific insights and macroeconomic scenarios from Oxford Economics, and Experian’s quality data to analyze and produce accurate loan loss forecasts. Additionally, all of the data, including the forecasts and models, are regularly updated as macroeconomic conditions change. “Experian’s agility and innovative technologies allow us to help lenders make informed decisions in real time to mitigate future risk,” said Greg Wright, chief product officer of Experian’s Consumer Information Services, in a recent press release. “We’re proud to work with our partners, Oxford Economics and Oliver Wyman, to bring lenders a product powered by machine learning, comprehensive data and macroeconomic forecast scenarios.” Built using advanced modeling and expert scenarios, the web-based application maximizes the more than 15 years of Experian’s loan-level data, including VantageScore®, bankruptcy scores and customer-level attributes. Financial institutions can gauge loan portfolio performance under various scenarios. “It is important that the banks take into account the evolving credit behaviors due to the COVID-19 pandemic, in addition to the robust modeling technique for their loss forecasting and strategic decisioning,” said Anshul Verma, senior director of products at Oliver Wyman, also in the release. “With the Ascend Portfolio Loss Forecaster, lenders get robust models that work in the current conditions and take into account evolving consumer behaviors,” Verma said. To watch Experian’s webinar on portfolio loss forecasting, please click here and to learn more about the Ascend Portfolio Loss Forecaster, click the button below. Learn More
To combat the growing threat of synthetic identity fraud, Experian recently announced the launch of Sure ProfileTM, a revolutionary change to the credit profile that gives lenders peace of mind with Experian’s commitment to share in losses that result from an identity we’ve assured. “Experian has always been a leader in combatting fraud, and with Sure Profile, we’re proud to deliver an industry-first fraud offering integrated into the credit profile that mitigates lender losses while protecting millions of consumers’ identities,” said Robert Boxberger, President of Decision Analytics, Experian North America. Synthetic identity fraud is expected to drive $48 billion in annual online payment fraud losses by 2023. Between opportunistic fraudsters and a lack of a unified definition for synthetic identity theft it can be nearly impossible to detect—and therefore prevent—this type of fraud. This breakthrough solution provides a composite history of a consumer’s identification, public record, and credit information and determines the risk of synthetic fraud associated with that consumer. It’s not just a fraud tool, it’s a comprehensive credit profile that utilizes premium data so lenders can make positive credit decisions. Sure Profile leverages the capabilities of the Experian Ascend Identity PlatformTM and uses Experian’s industry-leading data assets and data quality to drive advanced analytics that set a higher level of protection for lenders. It’s powered by newly-developed machine learning and AI models. And it offers a streamlined approach to define and detect synthetic identities early in the originations process. Most importantly, Sure Profile differentiates between real people and potentially risky applicants so lenders can increase application approvals with greater assurance and less risk. “Experian can confidently define and help detect synthetic fraud. That\'s why we can help stop it,” said Craig Boundy, CEO of Experian North America. “Experian stands behind our data with assurance given to our clients. It’s better for lenders and it’s better for consumers.” Sure Profile is a complement to our robust set of identity protection and fraud management capabilities, which are designed to address fraud and identity challenges including account openings, account takeovers, e-commerce fraud and more. This first-of-its kind profile is the future of underwriting and portfolio protection and it’s here now. Read press release Learn More About Sure Profile
While an overdue economic downturn has been long discussed, arguably no one could have foreseen the economic disruption from COVID-19 to the extent that’s been witnessed thus far. But now that we’re here, is there a line of sight to financial institutions’ next move? With the current situation marked by a history-making rise in unemployment, massive amounts of uncertainty within the market as well as for consumers and small businesses and consumer spending changes, loss forecasting is more important now than ever before. After the longest period of economic growth in history, financial institutions are caught off guard. While large banks are more prepared as they have stress testing capabilities in place and are estimating the potential large impact on their loss allowances, the since-delayed CECL requirements emphasized forecasting for the masses, and yet many are still under-equipped. Loss forecasting has evolved from a need for a small few to now a necessary strategy for all. While some financial institutions will look to loss forecasting to potentially reduce the severity of impact for the path ahead during these times (or even how they might come out stronger than their competition), for many, loss forecasting is the key to survival. Bare necessities. Understanding the possible outcomes of the pandemic’s impact is necessary to make critical business decisions. Lenders are likely receiving numerous questions about their portfolios and possible outcomes. These questions include, but are not limited to: What could the range of outcomes to my portfolio based on expert forecasts of macroeconomic conditions? How will I make lending decisions in the short term? Do my models need to change? How bad could charge offs be for my portfolio? If I have reduced marketing and application flows, at what point do I need to begin opening new accounts or consider portfolio acquisitions? How can lenders get answers? Loss forecasting. As Mohammed Chaudhri, Experian Chief Economist, said, “Loss forecasting is more pivotal than ever…existing models are not going to be up to the task of accurately predicting losses.” Whatever questions you’re receiving, you need certain necessary pieces of information to navigate this new era of loss forecasting. Those pieces are frequently updated client and industry data; ongoing access to expert macroeconomic forecasts; and sophisticated and evolved forecasting models. Client and Industry Data Loan-level data, bankruptcy scores and customer-level attributes are key insights to fueling loss forecasting models. By combining several data sets and scores (and a comprehensive history of both) your organization can see greater benefits. Macroeconomic Forecasts As has been mentioned numerous times, the economic impact resulting from COVID-19 is not at all like the Great Recession. As such, leveraging macroeconomic forecasts, and specifically COVID-19 forecasts, is critical to analyzing the potential impacts to your organization. Sophisticated Models Whether building models on your own or leveraging an expert, the key ingredients include the innerworkings of the model, leveraging historical data and making sure that both the models and the data are updated regularly to ensure you have the most accurate, thorough forecasts available. Also, leveraging machine learning tools is imperative for model specification and evaluation. Fortunately, while model building and loss forecasting used to be synonymous with countless resources and dollar signs, innovation and digital transformation have made these strategies within reach for financial institutions of all sizes. Incorporating the right data (and ensuring that data is regularly updated), with the right tools and macroeconomic scenarios (including COVID-19, upside, baseline, adverse and severely adverse scenarios) enables you to get a line of sight into the actions you need to take now. Empowered with insights to compare and benchmark results, discover the cause of changes in results, explore result scenarios in advance, and access recommended optimizations, loss forecasting enables you to focus on the critical decisions your business depends on. Experian helps you with loss forecasting for now and the future. For more information, including an on-demand webinar Experian presented with Oliver Wyman as well as the opportunity to engage Experian experts into your loss forecasting strategy, please click the button below. Learn More
This is the third in a series of blog posts highlighting optimization, artificial intelligence, predictive analytics, and decisioning for lending operations in times of extreme uncertainty. The first post dealt with optimization under uncertainty and the second with predicting consumer payment behavior. In this post I will discuss how well credit scores will work for consumer lenders during and after the COVID-19 crisis and offer some recommendations for what lenders can be doing to measure and manage that model risk in a time like this. Perhaps no analytics innovation has created opportunity for more individuals than the credit score has. The first commercially available credit score was developed by MDS (now part of Experian) in 1987. Soon afterwards FICO® popularized the use of scores that evaluate the risk that a consumer would default on a loan. Prior to that, lending decisions were made by loan officers largely on the basis on their personal familiarity with credit applicants. Using data and analytics to assess risk not only created economic opportunity for millions of borrowers, but it also greatly improved the financial soundness of lending institutions worldwide. Predictive models such as credit scores have become the most critical tools for consumer lending businesses. They determine, among other things, who gets a loan and at what price and how an account such as a credit line is managed through its life cycle. Predictive models are in many cases critical for calculating loan and loss reserves, for stress testing, and for complying with accounting standards. Nearly all lenders rely on generic scores such as the FICO score and VantageScore®. Most larger companies also have a portfolio of custom scorecards that better predict particular aspects of payment behavior for the customers of interest. So how well are these scorecards likely to perform during and after the current pandemic? The models need to predict consumer credit risk even as: Nearly all consumers change their behaviors in response to the health crisis, Millions of people—in America and internationally—find their income suddenly reduced, and Consumers receive large numbers of accommodations from creditors, who have in turn temporarily changed some of their credit reporting practices in response to guidelines in the federal CARES Act. In an earlier post, I pointed out that there is good reason to believe that credit scores will tend to continue to rank order consumers from most likely to least likely to repay their debts even as we move from the longest economic expansion in history to a period of unforeseen and unexpected challenges. But the interpretation of the score (for example, the log odds or the bad rate) may need to be adjusted. Furthermore, that assumes that the model was working well on a lender’s population before this crisis started. If it has been a long time since a scorecard was validated, that assumption needs to be questioned. Because experts are considering several different scenarios regarding both the immediate and long-term economic impacts of COVID-19, it’s important to have a plan for ongoing monitoring as long as necessary. Some lenders have strong Model Risk Management (MRM) teams complying with requirements from the Federal Reserve, Federal Deposit Insurance Corporation (FDIC), the Office of the Comptroller of the Currency (OCC). Those resources are now stretched thin. Other institutions, with fewer resources for MRM, are now discovering gaps in their model inventories as they implement operational changes. In either case, now’s the time to reassess how well scorecards are working. Good model validation practices are especially critical now if lenders are to continue to make the sound data-driven decisions that promote fairness for consumers and financial soundness for the institution. If you’re a credit risk manager responsible for the generic or custom models driving your lending, servicing, or capital allocation policies, there are several things you can do--starting now--to be sure that your organization can continue to make fair and sound lending decisions throughout this volatile period: Assess your model inventory. Do you have good documentation showing when each of the models in your organization was built? When was it last validated? Assign a level of criticality to each model in use. Starting with your most critical models, perform a baseline validation to determine how the model was performing prior to the global health crisis. It may be prudent to conduct not only your routine validation (verifying that the model was continuing to perform at the beginning of the period) but also a baseline validation with a shortened performance window (such as 6-12 months). That baseline validation will be useful if the downturn becomes a protracted one—in which case your scorecard models should be validated more frequently than usual. A shorter outcome window will allow a timelier assessment of the relationship between the score and the bad rate—which will help you update your lending and servicing policies to prevent losses. Determine if any of your scorecards had deteriorated even before the global pandemic. Consider recalibrating or rebuilding those scorecards. (Use metrics such as the Population Stability Index, the K-S statistic and the Gini Coefficient to help with that decision.) Many lenders chose not to prioritize rebuilding their behavioral scorecards for account management or collections during the longest period of economic growth in memory. Those models may soon be among the most critical models in your organization as you work to maintain the trust of your accountholders while also maintaining your institution’s financial soundness. Once the CARES accommodation period has expired, it will be important to revalidate your models more frequently than in the past—for as long as it takes until consumer behavior normalizes and the economy finds its footing. When you find it appropriate to rebuild a scorecard model, consider whether now is the time to implement ethical and explainable AI. Some of our clients are finding that Machine Learned models are more predictive than traditional scorecards. Early Experian research using data from the last recession indicates this will continue to be true for the foreseeable future. Furthermore, Experian has invested in Research and Development to help these clients deliver FCRA-compliant Adverse Action reasons to their consumers and to make the models explainable and transparent for model risk governance and compliance purposes. The sudden economic volatility that has resulted from this global health crisis has been a shock to all organizations. It is important for lenders to take the pulse of their predictive models now and throughout the downturn. They are especially critical tools for making sound data-driven business decisions until the economy is less volatile. Experian is committed to helping your organization during times of uncertainty. For more resources, visit our Look Ahead 2020 Hub. Learn more
Today’s lending market has seen a significant increase in alternative business lending, with companies utilizing new data assets and technology. As the lending landscape becomes increasingly competitive, consumers have more choices than ever when it comes to lending products. To drive profitable growth, lenders must find new ways to help applicants gain access to the loans they need. How Spring EQ is leveraging Experian BoostTM Home equity lender Spring EQ turned to Experian’s first-of-its-kind financial tool that empowers consumers to add positive payments directly into their credit file to assist applicants with attaining the best loan opportunities and rates. By using Experian BoostTM, which captures the value of consumer’s utility and telecom trade lines, in their current lending process, Spring EQ can help applicants near approval or risk thresholds move to higher risk tiers and qualify for better loan terms and conditions. Driving growth with consumer-permissioned data Over 40 million consumers in the U.S. either have no credit file or have insufficient information in their files to generate a traditional credit score. Consumer-permissioned data empowers these individuals to leverage their online financial data and payment histories to gain better access to loans and other financial services while providing lenders with a more comprehensive view of their creditworthiness. According to Experian research, 70% of consumers see the benefits of sharing additional financial information and contributing positive payment history to their credit file if it increases their odds of approval and helps them access more favorable credit terms. Read our case study for more insight on using Experian Boost to: Make better lending decisions Offer or underwrite credit to more people Promote the right credit products Increase conversion and utilization rates Read case study Learn more about Experian Boost
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