Tag: credit decisions

Machine learning (ML) is a powerful tool that can consume vast amounts of data to uncover patterns, learn from past behaviors, and predict future outcomes. By leveraging ML-powered credit risk models, lenders can better determine the likelihood that a consumer will default on a loan or credit obligation, allowing them to score applicants more accurately. When applied to credit decisioning, lenders can achieve a 25 percent reduction in exposure to risky customers and a 35 percent decrease in non-performing loans.1 While ML-driven models enable lenders to target the right audience and control credit losses, many organizations face challenges in developing and deploying these models. Some still rely on traditional lending models with limitations preventing them from making fast and accurate decisions, including slow reaction times, fewer data sources, and less predictive performance. With a trusted and experienced partner, financial institutions can create and deploy highly predictive ML models that optimize their credit decisioning. Case study: Increase customer acquisition with improved predictive performance Looking to meet growth goals without increasing risk, a consumer goods retailer sought out a modern and flexible solution that could help expand its finance product options. This meant replacing existing ML models with a custom model that offers greater transparency and predictive power. The retailer partnered with Experian to develop a transparent and explainable ML model. Based on the model’s improved predictive performance, transparency, and ability to derive adverse action reasons for declines, the retailer increased sales and application approval rates while reducing credit risk. Read the case study Learn about our custom modeling capabilities 1 Experian (2020). The Art of Decisioning in Uncertain Times

At Experian, we know that financial institutions, fintechs and lenders across the entire spectrum – small, medium and large, are further exploring and adopting AI-powered solutions to unlock growth and improve operational efficiencies. With increasing competition and a dynamic economy, AI-driven strategies across the entire customer lifecycle are no longer a nice to have, they are a must. Our dedication to delivering on this need for our clients is why we are thrilled to be recognized as a Fintech Breakthrough Award winner for the fifth consecutive year. Experian’s Ascend Intelligence ServicesTM (AIS) platform hosts a suite of analytics solutions and has been named “Best Consumer Lending Product” in the sixth annual FinTech Breakthrough Awards. This awards program is conducted by FinTech Breakthrough, an independent market intelligence organization that recognizes the top companies, technologies and products in the global fintech market today. This is the second consecutive year that AIS has been recognized with a FinTech Breakthrough Award, previously being selected for the “Consumer Lending Innovation Award” in 2021. “Winning another award from FinTech Breakthrough is a fantastic validation of the success and momentum of our Ascend Intelligence Services suite. Now more than ever, the world is in a state of constant change and companies are being reactive, with data scientists spending too much time on manual, repetitive data-wrangling tasks, at a time when they cannot afford to do so,” said Shri Santhanam, Experian’s executive vice president and general manager of Global Analytics and AI. “Companies need to be able to rapidly develop and deploy ML-powered models in an agile way at low cost. We are now able to offer this to more lenders no matter their size.” With AIS, Experian can empower financial services firms to make the best decisions across the customer life cycle with rapid model and strategy build, seamless deployment, optimization and continuous monitoring. The AIS suite is comprised of two key solution models: Ascend Intelligence Services Acquire is a managed services offering that enables financial institutions to increase approval rates and control bad debt by acquiring the right customers and providing the best offers. This is accomplished through a rapid AI/ML model build that will help better quantify the risk of an individual applicant. Next, a mathematically optimized decision strategy is designed to provide a more granular view of the applicant and help make the best decision possible based on the institution’s specific business goals and constraints. The combination of the AI/ML model and optimized decision strategy provides increased predictive power that mitigates risk and allows more automated decisions to be made. The model and strategy are seamlessly deployed to help deliver business value quickly. Ascend Intelligence Services™ Limit enables financial institutions to make the right credit limit decisions at account origination and during account management. Limit uses Experian’s data, predictive risk and balance models and our powerful optimization engine to design the right credit limit strategy that maximizes product usage, while keeping losses low. To learn more about how Ascend Intelligence Services can support your business, please explore our solutions page. Learn more For a list of all award winners selected for the 2022 FinTech Breakthrough Awards, click here.

As more consumers apply for credit and increase their spending1, lenders and financial institutions have an opportunity to expand their portfolios and improve profitability. The challenge is ensuring they’re extending credit responsibly and inclusively. Millions of Americans, many of whom are creditworthy, lack access to mainstream credit options. This may be because they have limited or no credit history, negative information within their credit file, or are a part of a historically disadvantaged group. To say “yes” to consumers they otherwise couldn’t or wouldn’t lend to, lenders must gain a deeper understanding of an individual’s stability, ability and willingness to pay. That’s where expanded FCRA-regulated and trended data come in. While traditional credit data has long been the primary means of gauging creditworthiness, it doesn’t tell the full story of a consumer’s financial situation. Let’s explore how differentiated data can help lenders make more informed credit decisions. Using differentiated data for deeper lending Expanded FCRA-regulated data provides supplemental credit data to help lenders gain a more holistic view of their current and prospective customers. Some examples of expanded FCRA-regulated data include alternative financial services data from nontraditional lenders, consumer-permissioned account data, rental payments and full-file public records. Because this data drives greater visibility and transparency around inquiry and payment behaviors, lenders can more accurately determine a consumer’s ability to pay and distinguish between reliable and high-risk applicants. In turn, lenders can approve more creditworthy consumers, grow their portfolios and increase financial opportunities for underserved communities, all while preventing and mitigating risk. 89% of lenders agree that expanded FCRA-regulated data allows them to extend credit to more consumers. Trended data empowers lenders with predictive insights into consumers by providing key balance and payment data for the previous 24 months. This is important as lenders can determine if a consumer’s credit behavior has improved or deteriorated over time. In turn, lenders can: Identify creditworthy customers: Establish if a consumer has a demonstrated ability to pay, is consistently paying more than the minimum payment, or shows no signs of payment stress. Increase response rates: Match the right products with the right prospects. Determine upsell and cross-sell opportunities: Present relevant offers based on anticipated needs and behaviors. Limit loss exposure: Understand the direction and velocity of payment performance to effectively manage risk exposure. Trended data helps lenders better predict future behavior, manage portfolio risk and design the best marketing offers. Turning insights into action Together, trended and expanded FCRA-regulated data benefit lenders and consumers alike. With a more holistic view of their customers, lenders gain powerful insights to lend deeper, ultimately helping them to expand their portfolios and drive greater access to credit for underserved communities. Learn more 1 The Recovery of Credit Applications to Pre-Pandemic Levels, Consumer Financial Protection Bureau, 2021.

It is no news that businesses are increasing their focus on advanced analytics and models. Whether looking to increase resources or focus on artificial intelligence (AI) and machine learning (ML), growth is the name of the game. But how do you maximize impact while minimizing risk? And how can you secure expertise and ROI when budgets are strapped?  Does your organization have the knowledge and talent in-house to remain competitive? No matter where you are on the analytics maturity curve, (outlined in detail below), your organization can benefit from making sure your machine learning models solution consists of: Regulatory documentation: Documentation for model and strategy governance is critical, especially as there is more conversation surrounding fair lending and how it relates to machine learning models. How does your organization ensure your models are explainable, well documented and making fair decisions? These are all questions you must be asking of your partners and solutions. Integrated services: For some service providers, “integrated,” is merely a marketing ploy, but it is essential that your solution truly integrates attributes, scores, models and decisions into one another. Not only does this serve as a “checks and balances” system of sorts, but it also is a primary driver for the speed of decisioning, which is crucial in today’s digital-first world. Deep expertise: Models are a major component for your decisioning, but ensuring those models are built and backed by experts is the one-two punch your strategies depend on. Make sure your services are managed by data scientists with extensive experience to take the best approach to solving your business problems. Usability: Does your solution close the loop? To future proof your processes, your solution must analyze the performance of attributes, scores and strategies. On top of that, your solution should make sure the items being built are useable and can be modified when needed. A one-and-done model does not suit the unique needs of your organization, so ensure your solution provides actionable analysis for continual refinement. Does your machine learning model solution check these boxes? Do you want to transform your existing system into a state-of-the-art AI platform? Learn more about how you can take your business challenges head-on by rapidly developing, deploying and monitoring sophisticated models and strategies to more accurately predict risk and achieve better outcomes. Learn more Access infographic   More information: What’s the analytics maturity curve? “Analytics” is the discovery, interpretation and communication of meaningful patterns in data; the connective tissue between data and effective decision-making within an organization. You can be along this journey for different decision points you’re making or product types, said Mark Soffietti, Director of Analytics Consulting at Experian, at our recent AI-driven analytics and strategy optimization webinar. Where you are on this curve often depends on your organization’s use of generic versus custom scores, the systems currently engaged to make those decisions and the sophistication of an organization’s models and/or strategies. Here’s a breakdown of each of the four stages: Descriptive Analytics – Descriptive analytics is the first step of the analytics maturity curve. These analytics answer the question “What is happening?” and typically revolve around some form of reporting. An example would be the information that your organization received 100 applications. Diagnostic Analytics – These analytics move from what happened to, “Why did it happen?” By digging into the 100 applications received, diagnostic analytics answer questions like “Who were we targeting?” and “How did those people come into our online portal/branch?” This information helps organizations be more strategic in their practices. Predictive Analytics – Models come into play at this stage as organizations try to predict what will happen. Based on the data set and an understanding of what the organization is doing, effort is put towards automating information to better solve business problems. Prescriptive Analytics – Optimization is key for prescriptive analytics. At this point in the maturity curve, there are multiple models and/or information that may be competing against one another. Prescriptive analytics will attempt to prescribe what an organization is doing and how it can drive more desired behaviors. For more information and to get personalized recommendations throughout your analytics journey, visit our website.

Shri Santhanam, Executive Vice President and General Manager of Global Analytics and Artificial Intelligence (AI) was recently featured on Lendit’s ‘Fintech One-on-One’ podcast. Shri and podcast creator, Peter Renton, discussed advanced analytics and AI’s role in lending and how Experian is helping lenders during what he calls the ‘digital lending revolution.’ Digital lending revolution “Over the last decade and a half, the notion of digital tools, decisioning, analytics and underwriting has come into play. The COVID-19 pandemic has dramatically accelerated that, and we’re seeing three big trends shake up the financial services industry,” said Shri. A shift in consumer expectations More than ever before, there is a deep focus on the customer experience. Five or six years ago, consumers and businesses were more accepting of waiting several days, sometimes even weeks, for loan approvals and decisions. However, the expectation has dramatically changed. In today’s digital world, consumers expect lending institutions to make quick approvals and real-time decisions. Fintechs being quick to act Fintech lenders have been disrupting the traditional financial services space in ways that positively impacts consumers. They’ve made it easier for borrowers to access credit – particularly those who have been traditional excluded or denied – and are quick to identify, develop and distribute market solutions. An increased adoption of machine learning, advanced analytics and AI Fintechs and financial institutions of all sizes are further exploring using AI-powered solutions to unlock growth and improve operational efficiencies. AI-driven strategies, which were once a ‘nice-to-have,’ have become a necessity. To help organizations reduce the resources and costs associated with building in-house models, Experian has launched Ascend Intelligence Services™, an analytics solution delivered on a modern tech AI platform. Ascend Intelligence Services helps streamline model builds and increases decision automation and approval rates. The future of lending: will all lending be done via AI, and what will it take to get there? According to Shri, lending in AI is inevitable. The biggest challenge the lending industry may face is trust in advanced analytics and AI decisioning to ensure lending is fair and transparent. Can AI-based lending help solve for biases in credit decisioning? We believe so, with the right frameworks and rules in place. Want to learn more? Explore our fintech solutions or click below. Listen to Podcast Learn more about Ascend Intelligence Services

Experian recently announced that it has made the IDC 2021 Fintech Rankings Top 100, highlighting the best global providers of financial technology. Experian is ranked number 11, rising 33 places from its 2020 ranking. IDC also refers to Experian as a ‘rising star.’ The robust data assets of Experian, combined with best-in-class modeling, decisioning and technology are powering new and innovative solutions. Experian has invested heavily in new technologies and infrastructures to deliver the freshest insights at the right time, to make the best decision. For example, Experian's Ascend Intelligence Services™ provides data, analytics, strategy, and performance monitoring, delivered on a modern-tech AI platform. With the investment in Ascend Intelligence Services, Experian has been able to streamline the delivery speed of analytical solutions to clients, improve decision automation rates and increase approval rates, in some cases by double digits. “Recognition in the top 20 of IDC FinTech Rankings demonstrates Experian’s commitment to the success of its financial clients,” said Marc DeCastro, research director at IDC Financial Insights. “We congratulate Experian for being ranked 11th in the 2021 IDC FinTech Rankings Top 100 list.” View the IDC Fintech Rankings list in its entirety here. Focus on Data, Advanced Analytics and Decisioning Creates Winning Strategy for Experian Experian’s focus on data, advanced analytics and decisioning has continued to gain recognition from various notable programs that acknowledge Fintech industry leaders and breakthrough technologies worldwide. Beyond the IDC Fintech Rankings Top 100, Experian won honors from the 2021 FinTech Breakthrough Awards, the 2021 CIO 100 Awards and was most recently shortlisted in the CeFPro Global Fintech Leaders List for 2022 in the categories of advanced analytics, anti-fraud, credit risk and core banking/back-end system technologies. “At Experian, we are committed to supporting the Fintech community. It’s great to see our continued efforts and investments driving positive impacts for our clients and their consumers. We will continue to invest and innovate to help our clients solve problems, create opportunities and support their customer-first missions,” said Jon Bailey, Vice President for Fintech at Experian. Learn more about how Experian can help advance your business goals with our Fintech Solutions and Ascend Intelligence Services. Explore fintech solutions Learn more about AIS

Artificial intelligence is here to stay, and businesses who are adopting the newest AI technology are ahead of the game. From targeting the right prospects to designing effective collections efforts, AI-driven strategies across the entire customer lifecycle are no longer a nice to have - they are a must.  Many organizations are late to the game of AI and/or are spending too much time and money designing and redesigning models and deploying them over weeks and months. By the time these models are deployed, markets may have already shifted again, forcing strategy teams to go back to the drawing board. And if these models and strategies are not being continuously monitored, they can become less effective over time and lead to missed opportunities and lost revenue. By implementing artificial intelligence in predictive modeling and strategy optimization, financial institutions and lenders can design and deploy their decisioning strategies faster than ever before and make incremental changes on the fly to adapt to evolving market trends.  While most organizations say they want to incorporate artificial intelligence and machine learning into their business strategy, many do not know where to start. Targeting, portfolio management, and collections are some of the top use cases for AI/ML strategy initiatives.  Targeting  One way businesses are using AI-driven modeling is for targeting the audiences that will most likely meet their credit criteria and respond to their offers. Financial institutions need to have the right data to inform a decisioning strategy that recognizes credit criteria, can respond immediately when prospects meet that criteria and can be adjusted quickly when those factors change. AI-driven response models and optimized decision strategies perform these functions seamlessly, giving businesses the advantage of targeting the right prospects at the right time.  Credit portfolio management  Risk models optimized with artificial intelligence and machine learning, built on comprehensive data sets, are being used by credit lenders to acquire new revenue and set appropriate balance limits. Strategies built around AI-driven risk models enable businesses to send new offers and cross-sell offers to current customers, while appropriately setting initial credit limits and managing limits over time for increased wallet share and reduced risk.   Collections  AI- and ML-driven analytics models are also optimizing collections strategies to improve recovery rates. Employing AI-powered balance and response models, credit lenders can make smarter collections decisions based on the most predictive and accurate information available.   For lending businesses who are already tight on resources, or those whose IT teams cannot meet the demand of quickly adapting to ever-changing market conditions and decisioning criteria, a managed service for AI-powered models and strategy design might be the best option. Managed service teams work closely with businesses to determine specific use cases, develop models to meet those use cases, deploy models quickly, and monitor models to ensure they keep producing and predicting optimally.  Experian offers Ascend Intelligence Services, the only managed service solution to provide data, analytics, strategy and performance monitoring. Experian’s data scientists provide expert guidance as they collaborate with businesses in developing and deploying models and strategies around targeting, acquisitions, limit-setting, and collections. Once those strategies are deployed, Experian continually monitors model health to ensure scores are still predictive and presents challenger models so credit lenders can always have the most accurate decisioning models for their business. Ascend Intelligence Services provides an online dashboard for easy visibility, documentation for regulatory compliance, and cloud capabilities to deliver scores and decisions in real-time.  Experian’s Ascend Intelligence Services makes getting into the AI game easy. Start realizing the power of data and AI-driven analytics models by using our ROI calculator below: initIframe('611ea3adb1ab9f5149cf694e'); For more information about Ascend Intelligence Services, visit our webpage or join our upcoming webinar on October 21, 2021.  Learn more Register for webinar

The pandemic changed nearly everything – and consumer credit is no exception. Data, analytics, and credit risk decisioning are gaining an even more significant role as we grow closer to the end of the global crisis. Consumers face uneven roads to recovery, and while some are ready to spend again, others are still dealing with pandemic-related financial stress. We surveyed nearly 9,000 consumers and 2,700 businesses worldwide about how consumers are stabilizing their finances and businesses are returning to growth for our new Global Decisioning Report. In this report, we dive into: Key business priorities in 2021 Financial concerns for consumers How to navigate an uneven recovery Business priorities for the year ahead The importance of the online experience As we begin to near the end of the pandemic, businesses need to prioritize technology that enables a responsive, flexible, efficient and confident approach. This can be done by leveraging advanced data and analytics and integrating machine learning tools into model development. By investing in the right credit risk decisioning tools now, you can help ensure your future. Download the report

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.

Changing consumer behaviors caused by the COVID-19 pandemic have made it difficult for businesses to make good lending decisions. Maintaining a consistent lending portfolio and differentiating good customers who are facing financial struggles from bad actors with criminal intent is getting more difficult, highlighting the need for effective decisioning tools. As part of our ongoing Q&A perspective series, Jim Bander, Experian’s Market Lead, Analytics and Optimization, discusses the importance of automated decisions in today’s uncertain lending environment. Check out what he had to say: Q: What trends and challenges have emerged in the decisioning space since March? JB: In the age of COVID-19, many businesses are facing several challenges simultaneously. First, customers have moved online, and there is a critical need to provide a seamless digital-first experience. Second, there are operational challenges as employees have moved to work from home; IT departments in particular have to place increase priority on agility, security, and cost-control. Note that all of these priorities are well-served by a cloud-first approach to decisioning. Third, the pandemic has led to changes in customer behavior and credit reporting practices. Q: Are automated decisioning tools still effective, given the changes in consumer behaviors and spending? JB: Many businesses are finding automated decisioning tools more important than ever. For example, there are up-sell and cross-sell opportunities when an at-home bank employee speaks with a customer over the phone that simply were not happening in the branch environment. Automated prequalification and instant credit decisions empower these employees to meet consumer needs. Some financial institutions are ready to attract new customers but they have tight marketing budgets. They can make the most of their budget by combining predictive models with automated prescreen decisioning to provide the right customers with the right offers. And, of course, decisioning is a key part of a debt management strategy. As consumers show signs of distress and become delinquent on some of their accounts, lenders need data-driven decisioning systems to treat those customers fairly and effectively. Q: How does automated decisioning differentiate customers who may have missed a payment due to COVID-19 from those with a history of missed payments? JB: Using a variety of credit attributes in an automated decision is the key to understanding a consumer’s financial situation. We have been helping businesses understand that during a downturn, it is important for a decisioning system to look at a consumer through several different lenses to identify financially stressed consumers with early-warning indicators, respond quickly to change, predict future customer behavior, and deliver the best treatment at the right time based on customer specific situations or behaviors.  In addition to traditional credit attributes that reflect a consumer’s credit behavior at a single point in time, trended attributes can highlight changes in a consumer’s behavior. Furthermore, Experian was the first lender to release new attributes specifically created to address new challenges that have arisen since the onset of COVID. These attributes help lenders gain a broader view of each consumer in the current environment to better support them. For example, lenders can use decisioning to proactively identify consumers who may need assistance. Q: What should financial institutions do next? JB: Financial institutions have rarely faced so much uncertainty, but they are generally rising to the occasion. Some had already adopted the CECL accounting standard, and all financial institutions were planning for it. That regulation has encouraged them to set aside loss reserves so they will be in better financial shape during and after the COVID-19 Recession than they were during the Great Recession. The best lenders are making smart investments now—in cloud technology, automated decisioning, and even Ethical and Explainable Artificial Intelligence—that will allow them to survive the COVID Recession and to be even more competitive during an eventual recovery. Financial institutions should also look for tools like Experian’s In the Market Model and Trended 3D Attributes to maximize efficiency and decisioning tactics – helping good customers remain that way while protecting the bottom line. In the Market Models Trended 3D Attributes  About our Expert: [avatar user="jim.bander" /] Jim Bander, PhD, Market Lead, Analytics and Optimization, Experian Decision Analytics Jim joined Experian in April 2018 and is responsible for solutions and value propositions applying analytics for financial institutions and other Experian business-to-business clients throughout North America. He has over 20 years of analytics, software, engineering and risk management experience across a variety of industries and disciplines. Jim has applied decision science to many industries, including banking, transportation and the public sector.

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

In the face of severe financial stress, such as that brought about by an economic downturn, lenders seeking to reduce their credit risk exposure often resort to tactics executed at the portfolio level, such as raising credit score cut-offs for new loans or reducing credit limits on existing accounts. What if lenders could tune their portfolio throughout economic cycles so they don’t have to rely on abrupt measures when faced with current or future economic disruptions? Now they can. The impact of economic downturns on financial institutions Historically, economic hardships have directly impacted loan performance due to differences in demand, supply or a combination of both. For example, let’s explore the Great Recession of 2008, which challenged financial institutions with credit losses, declines in the value of investments and reductions in new business revenues. Over the short term, the financial crisis of 2008 affected the lending market by causing financial institutions to lose money on mortgage defaults and credit to consumers and businesses to dry up. For the much longer term, loan growth at commercial banks decreased substantially and remained negative for almost four years after the financial crisis. Additionally, lending from banks to small businesses decreased by 18 percent between 2008-2011. And – it was no walk in the park for consumers. Already faced with a rise in unemployment and a decline in stock values, they suddenly found it harder to qualify for an extension of credit, as lenders tightened their standards for both businesses and consumers. Are you prepared to navigate and successfully respond to the current environment? Those who prove adaptable to harsh economic conditions will be the ones most poised to lead when the economy picks up again. Introducing the FICO® Resilience Index The FICO® Resilience Index provides an additional way to evaluate the quality of portfolios at any point in an economic cycle. This allows financial institutions to discover and manage potential latent risk within groups of consumers bearing similar FICO® Scores, without cutting off access to credit for resilient consumers. By incorporating the FICO® Resilience Index into your lending strategies, you can gain deeper insight into consumer sensitivity for more precise credit decisioning. What are the benefits? The FICO® Resilience Index is designed to assess consumers with respect to their resilience or sensitivity to an economic downturn and provides insight into which consumers are more likely to default during periods of economic stress. It can be used by lenders as another input in credit decisions and account strategies across the credit lifecycle and can be delivered with a credit file, along with the FICO® Score. No matter what factors lead to an economic correction, downturns can result in unexpected stressors, affecting consumers’ ability or willingness to repay. The FICO® Resilience Index can easily be added to your current FICO® Score processes to become a key part of your resilience-building strategies. Learn more

Time – it’s the only resource we can’t get more of, which is why we tend to obsess over saving it. Despite this obsession, it can be hard for us to identify time-wasting activities. From morning habits to credit decisioning, processes and routines that seem, well, routine, can get in the way of maximizing how we use our time. Identifying the Problem Every morning, I used to turn on my coffee maker, walk to the bathroom to take my multivitamin, then walk back into the kitchen to finish making my coffee. This required maybe twenty steps to the bathroom and twenty steps back, and while this isn’t a huge amount of time—half a minute at best—it’s not insignificant, especially in the morning when time feels particularly precious. One day, I realized I could eliminate the waste by moving my multivitamin to the cabinet above my coffeemaker. What if we could all make minor changes to enhance our efficiency both at home and at work? Imagine how much time we could save by cutting out unnecessary steps. And how saving that time could help drive significant revenue increases. Time Equals Money When businesses waste time with unnecessary steps, that’s money from their bottom line, and out of the pockets of people who are connected to them. Over the last several years, a new time saver has emerged – Application Programming Interface (API). APIs allow application programs to communicate with other operating systems or control programs through a series of server requests or API calls, enabling seamless interaction, data sharing and decisioning. Experian’s partners utilize our ever-growing suite of APIs to quickly access better data, making existing processes more effective and routines more efficient. In the past, banks and other partners had to send files back and forth to Experian when they needed decisioning on a customer’s credit-worthiness prior to approving a new loan or extending a credit limit increase. Now, partners can have their origination system call an Experian API and send their data through that. Our system processes it and sends it back in milliseconds, giving the lenders real-time decisioning rather than shuttling information back and forth unnecessarily. Instead of effectively walking away from one process (assisting the customer/making coffee) to start another (retrieving credit info/walking down the hall to take the multivitamin), our partners are able to link these processes up and save time, allowing them to capitalize on the presence and interest of their customer. The Proof When Washington State Employees Credit Union, the second-largest credit union in the state, realized they needed to make a change to keep pace with increasing competition, they turned to Experian. With our solution, the credit union is now able to provide its members with instant credit decisioning through their online banking platform. This real-time decisioning at the point of member-initiated contact increased the credit union’s loan and credit applications by 25%. Additionally, member satisfaction increased, with 90% of members finding the simplified prequalification process to be more efficient. By accessing Experian’s decisioning services through your existing connection, lenders can to save time and match consumers with the products that match their credit profile before they apply – increasing approval rates once the application is submitted. Best of all, the entire process with the consumers is completed within seconds. Find out how Experian’s solutions can help you improve your existing processes and cut out unnecessary steps. Get started

Retailers are already starting to display their Christmas decorations in stores and it’s only early November. Some might think they are putting the cart ahead of the horse, but as I see this happening, I’m reminded of the quote by the New York Yankee’s Yogi Berra who famously said, “It gets late early out there.” It may never be too early to get ready for the next big thing, especially when what’s coming might set the course for years to come. As 2019 comes to an end and we prepare for the excitement and challenges of a new decade, the same can be true for all of us working in the lending and credit space, especially when it comes to how we will approach the use of alternative data in the next decade. Over the last year, alternative data has been a hot topic of discussion. If you typed “alternative data and credit” into a Google search today, you would get more than 200 million results. That’s a lot of conversations, but while nearly everyone seems to be talking about alternative data, we may not have a clear view of how alternative data will be used in the credit economy. How we approach the use of alternative data in the coming decade is going to be one of the most important decisions the lending industry makes. Inaction is not an option, and the time for testing new approaches is starting to run out – as Yogi said, it’s getting late early. And here’s why: millennials. We already know that millennials tend to make up a significant percentage of consumers with so-called “thin-file” credit reports. They “grew up” during the Great Recession and that has had a profound impact on their financial behavior. Unlike their parents, they tend to have only one or two credit cards, they keep a majority of their savings in cash and, in general, they distrust financial institutions. However, they currently account for more than 21 percent of discretionary spend in the U.S. economy, and that percentage is going to expand exponentially in the coming decade. The recession fundamentally changed how lending happens, resulting in more regulation and a snowball effect of other economic challenges. As a result, millennials must work harder to catch up financially and are putting off major life milestones that past generations have historically done earlier in life, such as homeownership. They more often choose to rent and, while they pay their bills, rent and other factors such as utility and phone bill payments are traditionally not calculated in credit scores, ultimately leaving this generation thin-filed or worse, credit invisible. This is not a sustainable scenario as we enter the next decade. One of the biggest market dynamics we can expect to see over the next decade is consumer control. Consumers, especially millennials, want to be in the driver’s seat of their “credit journey” and play an active role in improving their financial situations. We are seeing a greater openness to providing data, which in turn enables lenders to make more informed decisions. This change is disrupting the status quo and bringing new, innovative solutions to the table. At Experian, we have been testing how advanced analytics and machine learning can help accelerate the use of alternative data in credit and lending decisions. And we continue to work to make the process of analyzing this data as simple as possible, making it available to all lenders in all verticals. To help credit invisible and thin-file consumers gain access to fair and affordable credit, we’ve recently announced Experian Lift, a new suite of credit score products that combines exclusive traditional credit, alternative credit and trended data assets to create a more holistic picture of consumer creditworthiness that will be available to lenders in early 2020. This new Experian credit score may improve access to credit for more than 40 million credit invisibles. There are more than 100 million consumers who are restricted by the traditional scoring methods used today. Experian Lift is another step in our commitment to helping improve financial health of consumers everywhere and empowers lenders to identify consumers who may otherwise be excluded from the traditional credit ecosystem. This isn’t just a trend in the United States. Brazil is using positive data to help drive financial inclusion, as are others around the world. As I said, it’s getting late early. Things are moving fast. Already we are seeing technology companies playing a bigger role in the push for alternative data – often powered by fintech startups. At the same time, there also has been a strong uptick in tech companies entering the banking space. Have you signed up for your Apple credit card yet? It will take all of 15 seconds to apply, and that’s expected to continue over the next decade. All of this is changing how the lending and credit industry must approach decision making, while also creating real-time frictionless experiences that empower the consumer. We saw this with the launch of Experian Boost earlier this year. The results speak for themselves: hundreds of thousands of previously thin-file consumers have seen their credit scores instantly increase. We have also empowered millions of consumers to get more control of their credit by using Experian Boost to contribute new, positive phone, cable and utility payment histories. Through Experian Boost, we’re empowering consumers to play an active role in building their credit histories. And, with Experian Lift, we’re empowering lenders to identify consumers who may otherwise be excluded from the traditional credit ecosystem. That’s game-changing. Disruptions like Experian Boost and newly announced Experian Lift are going to define the coming decade in credit and lending. Our industry needs to be ready because while it may seem early, it’s getting late.