Tag: advanced analytics

Even as 75% of large and mid-sized U.S. e-commerce marketplace merchants predict continued double-digit online sales growth rates through the end of 2022,1 their success is hampered by unnecessary friction driven by concerns of card-not-present fraud and additional fraud risks in an online world. Compared to the 96% approval rate for point-of-sale purchases, card-not-present transactions yield a surprisingly low 81% approval rate. According to a survey conducted by Aite Novarica,1 the difference stems from reviewing up to 16% of attempted transactions for possible fraud. Even more surprising is that many of the respondents report that more than two-thirds of these reviews are later found to be unwarranted. Current transaction processing and risk capabilities are impeding growth and creating friction that damages e-commerce marketplace brands. What do we mean when we talk about online card-not-present transaction friction? Much of the success or failure of e-commerce depends on how easy merchants make it for consumers to complete a transaction. Effective identity resolution, fraud mitigation and risk solutions can lead to increased sales, while unrefined solutions and unnecessary friction will run merchants the risk of denying a legitimate customer purchase at checkout because they have been incorrectly labeled a fraudster–a ‘false positive’ or ‘false decline.’ These solutions leave room for improvement based on several key factors–the limited amount of data that passes through the authorization stream from the merchant to the issuer is a key contributor. According to Aite-Novarica Group’s The E-Commerce Fraud Enigma: The Quest to Maximize Revenue While Minimizing Fraud Report, “This reinforces the importance for merchants to augment the decisioning on their side with a wide variety of data sources that can help inform them regarding the risk profile of both the customer and the transaction.” Challenges with current transaction processing and verification tools Today, merchants leverage email address data, device information and other technologies to augment their address verification capabilities. The challenge is that these tools each judge the risk of a specific component of the transaction or the individual. Where integration is lacking, false positives are amplified and that is exactly what the data1 says is happening. Different tools working in isolation all catch the same fraud but flag different false positives—dragging down overall performance. The result is that 75% of e-commerce merchants place maximizing sales, minimizing friction and reducing false declines at the top of their to-do list. 88% say they are ready for a change to achieve these goals.1 Fast Facts 16% of all attempted online transactions experience friction for suspected fraud. 70% of this number is unnecessary, and upon manual review, are ultimately approved.1 78% of e-commerce merchants report friction driven by suspected fraud is increasing. 78% of merchants report increasing declines due to suspected fraud over the last two years. 46% indicate an increase of more than 5%.1 81% of consumers say that a positive online experience makes them think more highly of a brand.2 The longer it takes for banks and issuers to process new account, the higher the rate of abandonment, which reaches 40% when the process takes longer than 10 minutes.3 The friction that consumers encounter throughout their buying journey and the expenses associated with merchant and issuer manual reviews can be costly. It is estimated that 70% of unwarranted friction is costing businesses ~$11B in false decline losses and sales annually.1 That number is expected to increase. And, beyond profit losses incurred from the order that was declined, merchants risk damaging brand reputation because of poor customer/buying experiences, and in some cases, the loss of the customer relationship as well. Reducing friction and providing a positive shopping experience is increasingly important to business success Businesses looking to address this and limit false declines should not allow this to come at the expense completing transactions for legitimate customers. Experian can help. By leveraging our multidimensional data, technical expertise and advanced analytics capabilities, we can help businesses authenticate valid customers without unnecessary friction, thus increasing revenue by increased approval rates, without increasing fraud or operating expenses. Get started with Experian Link™ - our frictionless credit card owner verification solution. Learn more. Experian Link   1"E-Commerece Fraud Enigma: The Quest to Maximize Revenue While Minimizing Fraud Report" Aite-Novarica Group, July 2022 2"Global Insights Report: The Evolving Expectations and Experience of the New Digital Customer" Experian, April 2022 3"Capturing the Digital Identity Evolution Through a Layered Approach" Liminal, June 2021

Experian’s newest Global Insights Report found that consumers are online 25% more today than they were just a year ago, highlighting the importance of the digital customer experience. To acquire customers and retain their loyalty, businesses need to focus on improving the online experience, preventing fraud, and managing credit risk.   This September, Experian surveyed 3,000 consumers and 900 businesses across all industries to explore business priorities and recent changes in consumer activities.   Many businesses and consumers are reportedly feeling more economically stable now than they were a year ago. As consumers resume spending the digital customer experience becomes even more paramount – requiring businesses to invest in scalable software solutions that will accurately assess credit risk and meet ever-changing needs and priorities.   Our research found that:   42% of consumers have increased concern for the safety of banking and shopping transactions Business adoption of advanced analytics has increased over last year, and adoption of artificial intelligence is up from 69% to 74% Consumers are more likely to share their personal data if it improves their experience, with 56% willing to share their contact information The top three consumer priorities continue to be security, privacy and convenience   Download the report to get all the latest insights into consumer desires and business behaviors as we move further through the digital evolution. Download the report

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

Lately, I’ve been surprised by the emphasis that some fraud prevention practitioners still place on manual fraud reviews and treatment. With the market’s intense focus on real-time decisions and customer experience, it seems that fraud processing isn’t always keeping up with the trends. I’ve been involved in several lively discussions on this topic. On one side of the argument sit the analytical experts who are incredibly good at distilling mountains of detailed information into the most accurate fraud risk prediction possible. Their work is intended to relieve users from the burden of scrutinizing all of that data. On the other side of the argument sits the human side of the debate. Their position is that only a human being is able to balance the complexity of judging risk with the sensitivity of handling a potential customer. All of this has led me to consider the pros and cons of manual fraud reviews. The Pros of Manual Review When we consider the requirements for review, it certainly seems that there could be a strong case for using a manual process rather than artificial intelligence. Human beings can bring knowledge and experience that is outside of the data that an analytical decision can see. Knowing what type of product or service the customer is asking for and whether or not it’s attractive to criminals leaps to mind. Or perhaps the customer is part of a small community where they’re known to the institution through other types of relationships—like a credit union with a community- or employer-based field of membership. In cases like these, there are valuable insights that come from the reviewer’s knowledge of the world outside of the data that’s available for analytics. The Cons of Manual Review When we look at the cons of manual fraud review, there’s a lot to consider. First, the costs can be high. This goes beyond the dollars paid to people who handle the review to the good customers that are lost because of delays and friction that occurs as part of the review process. In a past webinar, we asked approximately 150 practitioners how often an application flagged for identity discrepancies resulted in that application being abandoned. Half of the audience indicated that more than 50% of those customers were lost. Another 30% didn’t know what the impact was. Those potentially good customers were lost because the manual review process took too long. Additionally, the results are subjective. Two reviewers with different levels of skill and expertise could look at the same information and choose a different course of action or make a different decision. A single reviewer can be inconsistent, too—especially if they’re expected to meet productivity measures. Finally, manual fraud review doesn’t support policy development. In another webinar earlier this year, a fraud prevention practitioner mentioned that her organization’s past reliance on manual review left them unable to review fraud cases and figure out how the criminals were able to succeed. Her organization simply couldn’t recreate the reviewer’s thought process and find the mistake that lead to a fraud loss. To Review or Not to Review? With compelling arguments on both sides, what is the best practice for manually reviewing cases of fraud risk? Hopefully, the following list will help: DO: Get comfortable with what analytics tell you. Analytics divide events into groups that share a measurable level of fraud risk. Use the analytics to define different tiers of risk and assign each tier to a set of next steps. Start simple, breaking the accounts that need scrutiny into high, medium and low risk groups. Perhaps the high risk group includes one instance of fraud out of every five cases. Have a plan for how these will be handled. You might require additional identity documentation that would be hard for a criminal to falsify or some other action. Another group might include one instance in every 20 cases. A less burdensome treatment can be used here – like a one-time-passcode (OTP) sent to a confirmed mobile number. Any cases that remain unverified might then be asked for the same verification you used on the high-risk group. DON’T: Rely on a single analytical score threshold or risk indicator to create one giant pile of work that has to be sorted out manually. This approach usually results in a poor experience for a large number of customers, and a strong possibility that the next steps are not aligned to the level of risk. DO: Reserve manual review for situations where the reviewer can bring some new information or knowledge to the cases they review. DON’T: Use the same underlying data that generated the analytics as the basis of a review. Consider two simplistic cases that use a new address with no past association to the individual. In one case, there are several other people with different surnames that have recently been using the same address. In the other, there are only two, and they share the same surname. In the best possible case, the reviewer recognizes how the other information affects the risk, and they duplicate what the analytics have already done – flagging the first application as suspicious. In other cases, connections will be missed, resulting in a costly mistake. In real situations, automated reviews are able to compare each piece of information to thousands of others, making it more likely that second-guessing the analytics using the same data will be problematic. DO: Focus your most experienced and talented reviewers on creating fraud strategies. The best way to use their time and skill is to create a cycle where risk groups are defined (using analytics), a verification treatment is prescribed and used consistently, and the results are measured. With this approach, the outcome of every case is the result of deliberate action. When fraud occurs, it’s either because the case was miscategorized and received treatment that was too easy to discourage the criminal—or it was categorized correctly and the treatment wasn’t challenging enough. Gaining Value While there is a middle ground where manual review and skill can be a force-multiplier for strong analytics, my sense is that many organizations aren’t getting the best value from their most talented fraud practitioners. To improve this, businesses can start by understanding how analytics can help group customers based on levels of risk—not just one group but a few—where the number of good vs. fraudulent cases are understood. Decide how you want to handle each of those groups and reserve challenging treatments for the riskiest groups while applying easier treatments when the number of good customers per fraud attempt is very high. Set up a consistent waterfall process where customers either successfully verify, cascade to a more challenging treatment, or abandon the process. Focus your manual efforts on monitoring the process you’ve put in place. Start collecting data that shows you how both good and bad cases flow through the process. Know what types of challenges the bad guys are outsmarting so you can route them to challenges that they won’t beat so easily. Most importantly, have a plan and be consistent. Be sure to keep an eye out for a new post where we’ll talk about how this analytical approach can also help you grow your business. Contact us

The tax gap—the difference between what taxpayers should pay and what they actually pay on time—can have a substantial impact on states’ budgets. Tax agencies and other state departments are responsible for helping states manage their budgets by minimizing expected revenue shortfalls. Underreported income is a significant budget complication that continues to frustrate even the most effective tax agencies, until the right tools are brought into play.   The Problem Underreporting is a large, complex issue for agencies. The IRS currently estimates the annual tax gap at $441 billion. There are multiple factors that comprise that total, but the most prevalent is underreporting, which represents 80% of the total tax gap. Of that, 54% is due to underreporting of individual income tax. In addition to being the largest contributor to the tax gap, underreporting is also extremely challenging to identify out of the millions of returns being filed. With 85% of taxes owed correctly reported and paid, finding underreporting can be like trying to locate a needle in the proverbial haystack. Making this even more challenging is the limited resources available for auditing returns, which makes efficiency key. The Solution Data, combined with artificial intelligence (AI) equals efficient detection. The problem with trying to detect which returns are most likely to have underreported income is similar to many other challenges Experian has solved with AI. Partnerships between Experian and state agencies combine what we know about consumers with what their agency knows about their population. We can take the data and use AI to separate the signal from the noise, finding opportunities to recoup lost revenue. Read our case study on how Experian was able to help an agency identify instances of underreporting, detecting an estimated $80 million annual lost revenue from underreported income. Download case study Contact us

To grow in today’s economic climate and beat the competition, financial institutions need to update their acquisition and cross-sell strategies. By doing so, they are able to drive up conversions, minimize risk, and ultimately connect consumers with the right offers at the right time. Businesses and consumers are spending more time online than ever before, with 40% of consumers increasing the number of businesses they visit online. They’ve also made it clear that they expect easy, frictionless transactions with their providers. This includes new accounts and offers of credit – creating the need for better delivery systems. Effective targeting and conversion come down to more than just direct mail and email subject lines, especially now in a volatile economy where consumers are seeking appropriate products for their current situation. Be the first to meet consumers’ needs by leveraging the freshest data, advanced analytics, and automated decision systems. For example, when a consumer tries to open a checking account, the system can initiate a “behind-the-scenes” real-time prescreen request while assessing information needed to open the deposit account. The financial institution can then see if the consumer qualifies for overdraft protection, refinancing offers, loans, credit cards, and more. By performing the pre-approval process in seconds, financial institutions can be sure that they're making the right offers to the right customer, and doing it at the right time. All of this helps to increase the offer acceptance rate, improving customer retention, and maximizing customer account life-time value. The pandemic upended a lot of the ways that your businesses run day-to-day – from where you work to how you (better) engage with customers. Arguably, some of the changes have been long overdue, particularly the acceleration to digital and better customer acquisition strategies. Ahead lies the opportunity to grow – strategies enacted now will determine the extent of that opportunity. To learn more about how Experian can help you assess your prescreen strategy and grow, contact us today. Request a call

Experian is proud to announce, for the second year in a row, we have been named to the global Fintech Leaders list, placing in the top 20 for 2021. The list and adjoining report are released annually by international research organization, the Center for Financial Professionals (CeFPro). In addition to placing 19th on the list, Experian also placed in the Credit Risk category. The Center for Financial Professionals’ Fintech Leaders 2021 Report is one of the most rigorous programs that rank fintech industry leaders. The report’s coverage includes evaluating top fintech companies, solution providers, and vendors. The results are usually based on gathered surveys from end-users, practitioners, and subject matter experts. CeFPro’s report comes from the group’s market analysis and original research, which are backed by an advisory board that consists of 60 international industry professionals. Andreas Simou, CeFPro’s Managing Director, shared that the CeFPro board and voting members recognized Experian within the fintech survey as leaders for their data, decisioning and analytical capabilities. Simou said, "Experian cements its place on the Fintech Leaders List, and has once again been very highly regarded, as a leading player within credit risk, most notably for their subject-matter expertise and excelling within the areas of data management and modelling,” he said. “We are honored to once again be recognized as a Fintech Leader by CeFPro and the global Fintech marketplace,” said Jon Bailey, Vice President for Fintech at Experian. “We are committed to supporting the Fintech community and we will continue to invest and innovate to help our clients solve problems, create opportunities, and promote financial inclusion,” Bailey said.

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

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

As financial institutions and other organizations scramble to formulate crisis response plans, it’s important to consider the power of data and analytics. Jim Bander, PhD, Experian’s Analytics and Optimization Market Lead discusses the ways that data, analytics and models can help during a crisis. Check out what he had to say: What implications does the global pandemic have on financial institutions’ analytical needs?  JB: COVID-19 is a humanitarian crisis, one that parallels Hurricanes Sandy and Katrina and other natural disasters but which far exceeds their magnitude. It is difficult to predict the impact as huge parts of the global economy have shut down. Another dimension of this disaster is the financial impact: in the US alone, more than 17 million people applied for unemployment in the first 6 weeks of the COVID-19 crisis. That compares to 15 million people in 18 months during the Great Recession. Data and analytics are more important than ever as financial institutions formulate their responses to this crisis. Those institutions need to focus on three key things: safety, soundness, and compliance. Safety: Financial institutions are taking immediate action to mitigate safety risks for their employees and their customers. Soundness: Organizations need to mitigate credit and fraud risk and to evaluate capital and liquidity. Some executives may need a better understanding of how their bank’s stress scenarios were calculated in the past to understand how they must be updated for the future. Important analytic functions include performing portfolio monitoring and benchmarking—quantifying the effects not only of consumer distress, but also of low interest rates. Compliance: Understanding and meeting complex regulatory and compliance requirements is crucial at this time. Companies have to adapt to new credit reporting guidelines. CECL requirements have been relaxed but lenders should assess the effects of COVID, and not only during their annual stress tests. As more consumers seek credit, from an analytics perspective, what considerations should financial institutions make during this time?  JB: During this volatile time, analytics will help financial institutions: Identify financially stressed consumers with early warning indicators Predict future consumer behavior Respond quickly to changes Deliver the best treatments at the right time for individual customers given their specific situations and their specific behavior. Financial institutions should be reevaluating where their organizations have the most vulnerability and should be taking immediate action to mitigate these risks. Some important areas to keep an eye on include early warning indicators, changes in fraudulent behavior (with the increase in digital engagements), and changes in customer behavior. Banks are already offering payment flexibility, deferments, and credit reporting accommodations. If volatility continues or increases, they may need to offer debt forgiveness plans. These organizations should also be prepared to understand their own changing constraints—such as budget, staffing levels, and liquidity requirements— especially as consumers accelerate their move to digital channels. In the near future, lenders should be optimizing their operations, servicing treatments, and lending policies to meet a number of possibly conflicting objectives in the presence of changing constraints and somewhat unpredictable transaction volumes.   What is the smartest next play for financial institutions?  JB: I see our smartest clients doing four things: Adapting to the new normal Maintaining engagement with existing customers by refreshing data that companies have on-hand for these consumers, and obtain additional views of these customers for analytics and data-driven decisioning Reallocating operational resources and anticipating the need for increased capacity in various servicing departments in the future Improving their risk management practices   What is Experian doing to help clients improve their risk management? JB: During this time, banks and other financial institutions are searching for ways to predict consumer behavior, especially during a crisis that combines aspects of a natural disaster with characteristics of a global recession. It is more important than ever to use analytics and optimization. But some of the details of the methodology is different now than during a time of economic expansion. For example, while credit scores (like FICO® and VantageScore® credit scores) will continue to rank consumers in terms of their probability to pay, those scores must be interpreted differently. Furthermore, those scores should be combined with other views of the consumer—such as trends in consumer behavior and with expanded FCRA-compliant data (data that isn’t reported to traditional credit bureaus). One way we’re helping clients improve their credit risk management is to provide them with a list of 140 consumer credit data attributes in 10 categories. With this list, companies will be able to better manage portfolio risk, to better understand consumer behavior, and to select the next best action for each consumer. Four other things we’re doing: We’re quickly updating our loss forecasting and liquidity management offerings to account for new stress scenarios. We’re helping clients review their statistical models’ performance and their customer segmentation practices, and helping to update the models that need refreshing. Our consulting team—Experian Advisory Services—has been meeting with clients virtually--helping them update, execute their crisis and downturn responses, and whiteboard new or updated tactical plans. Last but not least, we’re helping lenders and consumers defend themselves against a variety of fraud and identity theft schemes. Experian is committed to helping your organization during these uncertain times. For more resources, visit our Look Ahead 2020 Hub. Learn more Jim Bander, PhD, Analytics and Optimization Market Lead, Decision Analytics, Experian North America Jim Bander, PhD 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. Jim has over 20 years of analytics, software, engineering and risk management experience across a variety of industries and disciplines. He has applied decision science to many industries including banking, transportation and the public sector. He is a consultant and frequent speaker on topics ranging from artificial intelligence and machine learning to debt management and recession readiness. Prior to joining Experian, he led the Decision Sciences team in the Risk Management department at Toyota Financial Services.

This is the first to a series of blog posts highlighting optimization, artificial intelligence, predictive analytics, and decisioning for lending operations in times of extreme uncertainty.   Like all businesses, lenders are facing tremendous change and uncertainty in the face of the COVID-19 crisis.  While focusing first on how to keep their employees and customers safe during the new normal, they are asking how to make data-driven decisions in this new environment.  It’s only natural that business people are skeptical about whether analytics will work in a situation like today's – in which the data deviate from all historical precedents.  Certainly, nobody predicted, for example, that the number of loans with forbearance requests would increase by over 1000% during each two-week period in March. Can anyone possibly make an optimized decision when things are changing so quickly and when so many things are unknown?   Prescriptive analytics – also known as mathematical optimization – is the practice of developing a business strategy to achieve a business objective subject to capacity and other constraints, often using a demand forecast. For example, banks use optimization software to develop marketing and debt management strategies to run their lending operations.  But what happens when the demand forecast might be wrong, when the constraints change quickly, and when decision-makers cannot agree on a single objective? The reality is that decisionmakers have to balance multiple competing objectives related to many different stakeholders. And, especially during the COVID-19 crisis and the period of change that will certainly follow, they have to do so in the face of uncertainty.   Let's discuss some of the methods that analysts use to control risk while optimizing lending practices during times like these. These techniques, collectively known as robust optimization and robust statistics, help lenders and other business people deal with the uncomfortable reality that we do not know what the future holds.     Consider a hypothetical bank or other lender servicing a portfolio of consumer loans and forecasting its loss performance in this environment. Management probably has several competing objectives: they want to improve service levels on their digital channel, they want to minimize credit and fraud losses, they're facing a reduced operating budget, and they're not certain how many employees they will have and which vendors will be able to provide adequate service levels. Furthermore, they anticipate new and unpredicted changes, and they need to be able to update their strategies quickly.   The mathematics can be quite technical, but Experian’s Marketswitch Optimization is user-friendly software to help businesspeople--not engineers--design and deploy optimal strategies for practices such as Account Management and Loan Originations while facing such a dynamic and uncertain environment. The bank's business analysts (not computer specialists or mathematicians) will use techniques such as these:   With Sensitivity Analysis, the analysts will explore the performance of their optimized Account Management, Collections, and Loan Originations strategies while considering possible changes in input variables.   Optimization Scenarios with Uncertainty (technically known as Stochastic Optimization) allow the managers and analysts to design operational strategies that control risk, particularly the bank’s exposure to probabilistic and worst-case scenarios.   Using Scenario Performance Analysis, the lender's team will validate and test their optimization scenarios against a variety of different data sets to understand how their strategies would perform in each case.   Model Quality Evaluation techniques help the credit risk managers compare model predictions against actual performance during a quickly changing economy.   Model impact analysis (related to Model Risk Management) helps senior leadership assess when it is time to invest in improving its statistical models.   Robust Model Calibration Analysis removes unjustifiable variations in the lender's predictive models to make their predictions more valid as things change over time.   These six advanced analytics techniques are especially helpful when developing business strategies for a time in which some values are unknown—including future unemployment levels, staffing budgets, data reporting practices, interest rates, and customer demands.  Business decisions can—and arguably must—be optimized during times of uncertainty. But during times like these, it is especially important that the analysts understand how and why to account for the uncertainty in both the data and the models.   Lenders, are you optimizing your servicing and debt management strategies? It has never been more important than now to do so--using the advanced techniques available to manage uncertainty mathematically. Learn more about how Marketswitch can help you solve complex business problems and meet organizational objectives. Learn more

While many companies are interested in implementing technology with advanced analytic capabilities, the concepts behind the technology can often be hard to understand. Demystifying the terminology around artificial intelligence and machine learning is one of the first steps for successful implementation. Discover what they mean for your financial institution in our new infographic: Learn more