Financial Services
Are You #TeamTrended or #TeamAlternative? There’s no such thing as too much data, but when put head to head, differences between the data sets are apparent. Which team are you on? Here’s what we know: With the entry and incorporation of alternative credit data into the data arena, traditional credit data is no longer the sole determinant for credit worthiness, granting more people credit access. Built for the factors influencing financial health today, alternative credit data essentially fills the gaps of the traditional credit file, including alternative financial services data, rental payments, asset ownership, utility payments, full file public records, and consumer-permissioned data – all FCRA-compliant data. Watch this video to see more: Trended data, on the other hand shows actual, historical credit data. It provides key balance and payment data for the previous 24 months to allow lenders to leverage behavior trends to determine how individuals are utilizing their credit. Different splices of that information reveal particular behavior patterns, empowering lenders to then act on that behavior. Insights include a consumer’s spend on all general purpose credit and charge cards and predictive metrics that identify consumers who will be in the market for a specific type of credit product. In the head-to-head between alternative credit data and trended data, both have clear advantages. You need both on your roster to supplement traditional credit data and elevate your game to the next level when it comes to your data universe. Compared to the traditional credit file, alternative credit data can reveal information differentiating two consumers. In the examples below, both consumers have moderate limits and have making timely credit card payments according to their traditional credit reports. However, alternative data gives insight into their alternative financial services information. In Example 1, Robert Smith is currently past due on his personal loan, whereas Michelle Lee in Example 2 is current on her personal loan, indicating she may be the consumer with stronger creditworthiness. Similarly, trended data reveals that all credit scores are not created equal. Here is an example of how trended data can differentiate two consumers with the same score. Different historical trends can show completely different trajectories between seemingly similar consumers. While the traditional credit score is a reliable indication of a consumer’s creditworthiness, it does not offer the full picture. What insights are you missing out on? Go to Infographic Get Started Today
Experian Boost gives consumers greater control over their credit profiles by allowing them to add non-traditional credit information to their Experian credit file.
From the time we wake up to the minute our head hits the pillow, we make about 35,000 conscious and unconscious decisions a day. That’s a lot of processing in a 24-hour period. As part of that process, some decisions are intuitive: we’ve been in a situation before and know what to expect. Our minds make shortcuts to save time for the tasks that take a lot more brainpower. As for new decisions, it might take some time to adjust, weigh all the information and decide on a course of action. But after the new situation presents itself over and over again, it becomes easier and easier to process. Similarly, using traditional data is intuitive. Lenders have been using the same types of data in consumer credit worthiness decisions for decades. Throwing in a new data asset might take some getting used to. For those who are wondering whether to use alternative credit data, specifically alternative financial services (AFS) data, here are some facts to make that decision easier. In a recent webinar, Experian’s Vice President of Analytics, Michele Raneri, and Data Scientist, Clara Gharibian, shed some light on AFS data from the leading source in this data asset, Clarity Services. Here are some insights and takeaways from that event. What is Alternative Financial Services? A financial service provided outside of traditional banking institutions which include online and storefront, short-term unsecured, short-term installment, marketplace, car title and rent-to-own. As part of the digital age, many non-traditional loans are also moving online where consumers can access credit with a few clicks on a website or in an app. AFS data provides insight into each segment of thick to thin-file credit history of consumers. This data set, which holds information on more than 62 million consumers nationwide, is also meaningful and predictive, which is a direct answer to lenders who are looking for more information on the consumer. In fact, in a recent State of Alternative Credit Data whitepaper, Experian found that 60 percent of lenders report that they decline more than 5 percent of applications because they have insufficient information to make a loan decision. The implications of having more information on that 5 percent would make a measurable impact to the lender and consumer. AFS data is also meaningful and predictive. For example, inquiry data is useful in that it provides insight into the alternative financial services industry. There are also more stability indicators in this data such as number of employers, unique home phone, and zip codes. These interaction points indicate the stability or volatility of a consumer which may be helpful in decision making during the underwriting stage. AFS consumers tend to be younger and less likely to be married compared to the U.S. average and traditional credit data on File OneSM . These consumers also tend to have lower VantageScores, lower debt, higher bad rates and much lower spend. These statistics lend themselves to seeing the emerging consumer; millennials, immigrants with little to no credit history and also those who may have been subprime or near prime consumers who are demonstrating better credit management. There also may be older consumers who may have not engaged in traditional credit history in a while or those who have hit a major life circumstance who had nowhere else to turn. Still others who have turned to nontraditional lending may have preferred the experience of online lending and did not realize that many of these trades do not impact their traditional credit file. Regardless of their individual circumstances, consumers who leverage alternative financial services have historically had one thing in common: their performance in these products did nothing to further their access to traditional, and often lower cost, sources of credit. Through Experian’s acquisition and integration of Clarity Services, the nation’s largest alternative finance credit bureau, lenders can gain access to powerful and predictive supplemental credit data that better detect risk while benefiting consumers with a more complete credit history. Alternative finance data can be used across the lending cycle from prospecting to decisioning and account review to collections. Alternative data gives lenders an expanded view of consumer behavior which enables more complete and confident lending decisions. Find out more about Experian’s alternative credit data: www.experian.com/alternativedata.
We’ve popped the bottles at midnight, now it’s time to burst the reality bubble. Countdown: t-minus less than 90 days until what is for many the dreaded April 15 tax deadline. Tax Season - Get Started Coupled with debt consolidation post-holidays, January is a harsh contrast to all the feasting and festivities of the holiday season. However, the tax season doesn’t necessarily have to be synonymous with doom and gloom – many Americans look forward to receiving a tax refund. And of those people expecting a tax refund, 35% of consumers said they would use it to pay down debt, according to the National Retail Federation. Lenders and financial institutions can help their consumers get off on the right financial foot for 2019 by helping them to pay down their debt. Here are 5 tools you need to have this tax season to make the most of your collections efforts: 1. Identify your target market – Tax Season Payment IndicatorTM Did you know the average tax refund in 2016 and 2017 was over $2,760, according to the IRS? Also, during the 2017 tax season, 45 million consumers paid at least $500 and 10% or more of a tradeline balance(s), according to Experian data. Tax Season Payment Indicator examines payment behavior over the past two years to determine whether a consumer has made a large payment to a tradeline balance – or balances – during tax season. 2. Keep up-to-date on consumer information – Clear ProfileTM Skip tracing just got easier. Narrow in on the right contact information for your past-due consumer using Clear Profile. Leveraging Clarity Service’s database, Clear Profile provides the most recent and historical demographic elements associated with your consumer’s previous applications including addresses, phone numbers, employers, emails and banks. 3. Know the right time to collect – Collection TriggersSM Take the guesswork out of how to manage your collections efforts. Track your accounts to notify you of a new contact information and changes that indicate your past-due consumers’ ability to pay. 4. Stay ahead of fraudsters – CrossCoreTM Fraudsters are everywhere, so protect your customers and your organization by monitoring your portfolio to keep fraudulent accounts from being opened. Still wondering how to get tax season ready? Get Your Collections Tax Season Ready
With scarce resources and limited experience available in the data science field, a majority of organizations are partnering with outside firms to fill gaps within their teams. A report compiled by Hexa Research found that the data analytics outsourcing market is set to expand at a compound annual growth rate of 30 percent between 2016 and 2024, reaching annual revenues of more than $6 billion. With data science becoming a necessity for success, outsourcing these specific skills will be the way of the future. When working with outside firms, you may be given the option between offshore and onshore resources. But how do you decide? Let’s discuss a few things you can consider. Offshore A well-known benefit of using offshore resources is lower cost. Offshore resources provide a larger pool of talent, which includes those who have specific analytical skills that are becoming rare in North America. By partnering with outside firms, you also expose your organization to global best practices by learning from external resources who have worked in different industries and locations. If a partner is investing research and development dollars into specific data science technology or new analytics innovations, you can use this knowledge and apply it to your business. With every benefit, however, there are challenges. Time zone differences and language barriers are things to consider if you’re working on a project that requires a large amount of collaboration with your existing team. Security issues need to be addressed differently when using offshore resources. Lastly, reputational risk also can be a concern for your organization. In certain cases, there may be a negative perception — both internally and externally — of moving jobs offshore, so it’s important to consider this before deciding. Onshore While offshore resources can save your organization money, there are many benefits to hiring onshore analytical resources. Many large projects require cross-functional collaboration. If collaboration is key to the projects you’re managing, onshore resources can more easily blend with your existing resources because of time zone similarities, reduced communication barriers and stronger cultural fit into your organization. In the financial services industry, there also are regulatory guidelines to consider. Offshore resources often may have the skills you’re looking for but don’t have a complete understanding of our regulatory landscape, which can lead to larger problems in the future. Hiring resources with this type of knowledge will help you conduct the analysis in a compliant manner and reduce your overall risk. All of the above Many of our clients — and we ourselves — find that an all-of-the-above approach is both effective and efficient. In certain situations, some timeline reductions can be made by having both onshore and offshore resources working on a project. Teams can include up to three different groups: Local resources who are closest to the client and the problem Resources in a nearby foreign country whose time zone overlaps with that of the local resources More analytical team members around the world whose tasks are accomplished somewhat more independently Carefully focusing on how the partnership works and how the external resources are managed is even more important than where they are located. Read 5 Secrets to Outsourcing Data Science Successfully to help you manage your relationship with your external partner. If your next project calls for experienced data scientists, Experian® can help. Our Analytics on DemandTM service provides senior-level analysts, either offshore or onshore, who can help with analytical data science and modeling work for your organization.
2019 is here — with new technology, new regulations and new opportunities on the docket. What does that mean for the financial services space? Here are the five trends you should keep your eye on and how these affect your credit universe. 1. Credit access is at an all-time high With 121 million Americans categorized as credit-challenged (subprime scores and a thin or nonexistent credit file) and 45 million considered credit-invisible (no credit history), the credit access many consumers take for granted has appeared elusive to others. Until now. The recent launch of Experian BoostTM empowers consumers to improve their credit instantly using payment history from their utility and phone bills, giving them more control over their credit scores and making them more visible to lenders and financial institutions. This means more opportunities for more people. Coupled with alternative credit data, which includes alternative financial services data, rental payments, and full-file public records, lenders and financial institutions can see a whole new universe. In 2019, inclusion is key when it comes to universe expansion goals. Both alternative credit and consumer-permissioned data will continue to be an important part of the conversation. 2. Machine learning for the masses The financial services industry has long been notorious for being founded on arguably antiquated systems and steeped in compliance and regulations. But the industry’s recent speed of disruption, including drastic changes fueled by technology and innovation, may suggest a changing of the guard. Digital transformation is an industry hot topic, but defining what that is — and navigating legacy systems — can be challenging. Successfully integrating innovation is the convergence at the center of the Venn diagram of strategy, technology and operations. The key, according to Deloitte, is getting “a better handle on data to extract the greatest value from technology investments.” How do you get the most value? Risk managers need big data, machine learning and artificial intelligence strategies to deliver market insights and risk evaluation. Between the difficulty of leveraging data sets and significant investment in time and money, it’s impossible for many to justify. To combat this challenge, the availability and access to an analytical sandbox (which contains depersonalized consumer data and comparative industry intel) is crucial to better serve clients and act on opportunities in lenders’ credit universe and beyond. “Making information analysis easily accessible also creates distinct competitive advantages,” said Vijay Mehta, Chief Innovation Officer for Experian’s Consumer Information Services, in a recent article for BAI Banking Strategies. “Identifying shifts in markets, changes in regulations or unexpected demand allows for quick course corrections. Tightening the analytic life cycle permits organizations to reach new markets and quickly respond to competitor moves.” This year is about meaningful metrics for action, not just data visualization. 3. How to fit into the digital-first ecosystem With so many things available on demand, the need for instant gratification continues to skyrocket. It’s no secret that the financial services industry needs to compete for attention across consumers’ multiple screens and hours of screen time. What’s in the queue for 2019? Personalization, digitalization and monetization. Consumers’ top banking priorities include customized solutions, omnichannel experience improvement and enhancing the mobile channel (as in, can we “Amazonize” everything?). Financial services leaders’ priorities include some of the same things, such as enhancing the mobile channel and delivering options to customize consumer solutions (BAI Banking Strategies). From geolocation targeting to microinteractions in the user experience journey to leveraging new strategies and consumer data to send personalized credit offers, there’s no shortage of need for consumer hyper-relevance. 33 percent of consumers who abandon business relationships do so because personalization is lacking, according to Accenture data for The Financial Brand. This expectation spans all channels, emphasizing the need for a seamless experience across all devices. 4. Keeping fraudsters out Many IT professionals regard biometric authentication as the most secure authentication method currently available. We see this technology on our personal devices, and many companies have implemented it as well. Biometric hacking is among the predicted threats for 2019, according to Experian’s Data Breach Industry Forecast, released last month. “Sensors can be manipulated and spoofed or deteriorate with too much use. ... Expect hackers to take advantage of not only the flaws found in biometric authentication hardware and devices, but also the collection and storage of data,” according to the report. 5. Regulatory changes and continued trends Under the Trump Administration, the regulatory front has been relatively quiet. But according to the Wall Street Journal, as Democrats gain control of the House of Representatives, lawmakers may be setting their sights on the financial services industry — specifically on legislation in response to the credit data breach in 2017. The Democratic Party leadership has indicated that the House Financial Services Committee will be focused on protecting consumers and investors, preserving sector stability, and encouraging responsible innovation in financial technology, according to Deloitte. In other news, the focus on improving accuracy in data reporting, transparency for consumers in credit scoring and other automated decisions can be expected to continue. Consumer compliance, and specifically the fair and responsible treatment of consumers, will remain a top priority. For all your needs in 2019 and beyond, Experian has you covered. Learn more
With the new year just days behind us, and as the uptick in holiday spending comes back down, debt consolidation will take precedence along with the making (and breaking) of new year’s resolutions. Personal loans were the fastest growing unsecured lending product for much of last year. From debt consolidation to major purchases, consumers are increasingly choosing these flexible, easy-access loans over credit cards throughout the course of the year. Recent Experian research highlighted the trends around this fast-paced lending product: Previously, while industry experts had predicted a leveling off of personal loans originations, Experian data shows steady growth. Additionally, there were 35.7 million personal loan trades in the second quarter, the highest number to date since Q1 of 2007. What is driving this growth? Observations suggest growth trends across the industry as a whole – not just in the personal loans segment. And the numbers prove it. Growth is occurring across the board. Experian statistics show: Consumer confidence is up 5.6% year over year Investor confidence remains high – up 18% year over year since 1987 Unemployment remains low and continued decrease is forecasted in the near future With increased confidence and increased spending often comes increased personal loans. More financial institutions are bringing personal loans under their roofs. As many consumers enter each new year as part of a “debt consolidation nation” per se, focus for many will be on personal loans as they seek to consolidate revolving debt. Since this is a known trend, lenders across the board – from traditional financial institutions to fintechs – need to be strategic with their marketing efforts in order to reach the right consumers with the right products at the right time. Consumers consider important factors in choosing the lender(s) for their personal loans including interest rate and the ability to apply online among others. These factors see differences across generations as well. These factors and others should influence lenders’ marketing strategies, on top of their best practices. Experian partnered with Mintel Group for their insights on the 2019 trends and best practices for digital credit marketing. Register for our upcoming webinar to learn more about Digital Credit Marketing 2019 Trends and Best Practices. Register for the Webinar
What if you had an opportunity to boost your credit score with a snap of your fingers? With the announcement of Experian BoostTM, this will soon be the new reality. As part of an increasingly customizable and instant consumer reality in the marketplace, Experian is innovating in the space of credit to allow consumers to contribute information to their credit profiles via access to their online bank accounts. For decades, Experian has been a leader in educating consumers on credit: what goes into a credit score, how to raise it and how to maintain it. Now, as part of our mission to be the consumer’s bureau, Experian is ushering in a new age of consumer empowerment with Boost. Through an already established and full-fledged suite of consumer products, Experian Boost is the next generation offering a free online platform that places the control in the consumers’ hands to influence their credit scores. The platform will feature a sign-in verification, during which consumers grant read-only permission for Experian Boost to connect to their online bank accounts to identify utility and telecommunications payments. After they verify their data and confirm that they want the account information added to their credit file, consumers will receive an instant updated FICO® Score. The history behind credit information spans several centuries from a group of London tailors swapping information on customers to keeping credit files on index cards being read out to subscribers over the telephone. Even with the evolution of the credit industry being very much in the digital age today, Experian Boost is a significant step forward for a credit bureau. This new capability educates the consumer on what types of payment behavior impacts their credit score while also empowering them to add information to change it. This is a big win-win for consumers and lenders alike. As Experian is taking the next big step as a traditional credit bureau, adding these data sources is a new and innovative way to help consumers gain access to the quality credit they deserve as well as promoting fair and responsible lending to the industry. Early analysis of Experian’s Boost impact on the U.S. consumer credit scores showed promising results. Here’s a snapshot of some of those findings: These statistics provide an encouraging vision into the future for all consumers, especially for those who have a limited credit history. The benefit to lenders in adding these new data points will be a more complete view on the consumer to make more informed lending decisions. Only positive payment histories will be collected through the platform and consumers can elect to remove the new data at any time. Experian Boost will be available to all credit active adults in early 2019, but consumers can visit www.experian.com/boost now to register for early access. By signing up for a free Experian membership, consumers will receive a free credit report immediately, and will be one of the first to experience the new platform. Experian Boost will apply to most leading consumer credit scores used by lenders. To learn more about the platform visit www.experian.com/boost.
It’s the holiday season — time for jingle bells, lighting candles, shopping sprees and credit card fraud. But we’re prepared. Our risk analyst team constantly monitors our FraudNet solution performance to identify anomalies our clients experience as millions of transactions occur this month. At its core, FraudNet analyzes incoming events to determine the risk level and to allow legitimate events to process without causing frustrating friction for legitimate customers. That ensures our clients can recognize good customers across digital devices and channels while reducing fraud attacks and the need for internal manual reviews. But what happens when things don’t go as planned? Here’s a recent example. One of our banking clients noticed an abnormally high investigation queue after a routine risk engine tuning. Our risk analyst team looked further into the attacks to determine the cause and assess whether it was a tuning issue or a true fraud attack. After an initial analysis, the team learned that the events shared many of the same characteristics: Came from the same geo location that has been seen in previous attacks on clients Showed suspicious device and browser characteristics that were recognized by Experian’s device identification technology Identified suspicious patterns that have been observed in other recent attacks on banks The conclusion was that it wasn’t a mistake. FraudNet had correctly identified these transactions as suspicious. Experian® then worked with our client and recommended a strategy to ensure this attack was appropriately managed. This example highlights the power of device identification technology as a mechanism to detect emerging fraud threats, as well as link analysis tools and the expertise of a highly trained fraud analyst to uncover suspicious events that might otherwise go unnoticed. In addition to proprietary device intelligence capabilities, our clients take advantage of a suite of capabilities that can further enhance a seamless authentication experience for legitimate customers while increasing fraud detection for bad actors. Using advanced analytics, we can detect patterns and anomalies that may indicate a fraudulent identity is being used. Additionally, through our CrossCore® platform businesses can leverage advanced innovation, such as physical and behavioral biometrics (facial recognition, how a person holds a phone, mouse movements, data entry style), email verification (email tenure, reported fraud on email identities), document verification (autofill, liveliness detection) and digital behavior risk indicators (transaction behavior, transaction velocity), to further advance their existing risk mitigation strategies and efficacy. With expanding partnerships and capabilities offered via Experian’s CrossCore platform, in conjunction with consultative industry expertise, businesses can be more confident during the authentication process to ensure a superb, frictionless customer experience without compromising security.
Your model is only as good as your data, right? Actually, there are many considerations in developing a sound model, one of which is data. Yet if your data is bad or dirty or doesn’t represent the full population, can it be used? This is where sampling can help. When done right, sampling can lower your cost to obtain data needed for model development. When done well, sampling can turn a tainted and underrepresented data set into a sound and viable model development sample. First, define the population to which the model will be applied once it’s finalized and implemented. Determine what data is available and what population segments must be represented within the sampled data. The more variability in internal factors — such as changes in marketing campaigns, risk strategies and product launches — and external factors — such as economic conditions or competitor presence in the marketplace — the larger the sample size needed. A model developer often will need to sample over time to incorporate seasonal fluctuations in the development sample. The most robust samples are pulled from data that best represents the full population to which the model will be applied. It’s important to ensure your data sample includes customers or prospects declined by the prior model and strategy, as well as approved but nonactivated accounts. This ensures full representation of the population to which your model will be applied. Also, consider the number of predictors or independent variables that will be evaluated during model development, and increase your sample size accordingly. When it comes to spotting dirty or unacceptable data, the golden rule is know your data and know your target population. Spend time evaluating your intended population and group profiles across several important business metrics. Don’t underestimate the time needed to complete a thorough evaluation. Next, select the data from the population to aptly represent the population within the sampled data. Determine the best sampling methodology that will support the model development and business objectives. Sampling generates a smaller data set for use in model development, allowing the developer to build models more quickly. Reducing the data set’s size decreases the time needed for model computation and saves storage space without losing predictive performance. Once the data is selected, weights are applied so that each record appropriately represents the full population to which the model will be applied. Several traditional techniques can be used to sample data: Simple random sampling — Each record is chosen by chance, and each record in the population has an equal chance of being selected. Random sampling with replacement — Each record chosen by chance is included in the subsequent selection. Random sampling without replacement — Each record chosen by chance is removed from subsequent selections. Cluster sampling — Records from the population are sampled in groups, such as region, over different time periods. Stratified random sampling — This technique allows you to sample different segments of the population at different proportions. In some situations, stratified random sampling is helpful in selecting segments of the population that aren’t as prevalent as other segments but are equally vital within the model development sample. Learn more about how Experian Decision Analytics can help you with your custom model development needs.
As our society becomes ever more dependent on everything mobile, criminals are continually searching for and exploiting weaknesses in the digital ecosystem, causing significant harm to consumers, businesses and the economy. In fact, according to our 2018 Global Fraud & Identity Report, 72 percent of business executives are more concerned than ever about the impact of fraud. Yet, despite the awareness and concern, 54 percent of businesses are only “somewhat confident” in their ability to detect fraud. That needs to change, and it needs to change right away. Our industry has thrived by providing products and services that root out bad transactions and detect fraud with minimal consumer friction. We continue to innovate new ways to authenticate consumers, apply new cloud technologies, machine learning, self-service portals and biometrics. Yet, the fraud issue still exists. It hasn’t gone away. How do we provide effective means to prevent fraud without inconveniencing everyone in the process? That’s the conundrum. Unfortunately, a silver bullet doesn’t exist. As much as we would like to build a system that can detect all fraud, eliminate all consumer friction, we can’t. We’re not there yet. As long as money has changed hands, as long as there are opportunities to steal, criminals will find the weak points – the soft spots. That said, we are making significant progress. Advances in technology and innovation help us bring new solutions to market more quickly, with more predictive power than ever, and the ability to help clients to turn these services on in days and weeks. So, what is Experian doing? We’ve been in the business of fraud detection and identity verification for more than 30 years. We’ve seen fraud patterns evolve over time, and our product portfolio evolves in lock-step to counter the newest fraud vectors. Synthetic identity fraud, loan stacking, counterfeit, identity theft; the specific fraud attacks may change but our solution stack counters each of those threats. We are on a continuous innovation path, and we need to be. Our consumer and small business databases are unmatched in the industry for quality and coverage, and that is an invaluable asset in the fight against fraud. It used to be that knowing something about a person was the same as authenticating that same person. That’s just not the case today. But, just because I may not be the only person who knows where I live, doesn’t mean that identity information is obsolete. It is incredibly valuable, just in different ways today. And that’s where our scientists come into their own, providing complex predictive solutions that utilize a plethora of data and insight to create the ultimate in predictive performance. We go beyond traditional fraud detection methods, such as knowledge-based authentication, to offer a custom mix of passive and active authentication solutions that improve security and the customer experience. You want the latest deep learning techniques? We have them. You want custom models scored in milliseconds alongside your existing data requests. We can do that. You want a mix of cloud deployment, dedicated hosted services and on-premise? We can do that too. We have more than 20 partners across the globe, creating the most comprehensive identity management network anywhere. We also have teams of experts across the world with the know how to combine Experian and partner expertise to craft a bespoke solution that is unrivaled in detection performance. The results speak for themselves: Experian analyzes more than a billion credit applications per year for fraud and identity, and we’ve helped our clients save more than $2 billion in annual fraud losses globally. CrossCore™, our fraud prevention and identity management platform, leverages the full breadth of Experian data as well as the data assets of our partners. We execute machine learning models on every decision to help improve the accuracy and speed with which decisions are made. We’ve seen CrossCore machine learning result in a more than 40 percent improvement in fraud detection compared to rules-based systems. Our certified partner community for CrossCore includes only the most reputable leaders in the fraud industry. We also understand the need to expand our data to cover those who may not be credit active. We have the largest and most unique sets of alternative credit data among the credit bureaus, that includes our Clarity Services and RentBureau divisions. This rich data helps our clients verify an individual’s identity, even if they have a thin credit file. The data also helps us determine a credit applicant’s ability to pay, so that consumers are empowered to pursue the opportunities that are right for them. And in the background, our models are constantly checking for signs of fraud, so that consumers and clients feel protected. Fraud prevention and identity management are built upon a foundation of trust, innovation and keeping the consumer at the heart of every decision. This is where I’m proud to say that Experian stands apart. We realize that criminals will continue to look for new ways to commit fraud, and we are continually striving to stay one step ahead of them. Through our unparalleled scale of data, partnerships and commitment to innovation, we will help businesses become more confident in their ability to recognize good people and transactions, provide great experiences, and protect against fraud.
Every morning, I wake up and walk bleary eyed to the bathroom, pop in my contacts and start my usual routine. Did I always have contacts? No. But putting on my contacts and seeing clearly has become part of my routine. After getting used to contacts, wearing glasses pales in comparison. This is how I view alternative credit data in lending. Are you having qualms about using this new data set? I get it, it’s like sticking a contact into your eye for the first time: painful and frustrating because you’re not sure what to do. To relieve you of the guesswork, we’ve compiled the top four myths related to this new data set to provide an in-depth view as to why this data is an essential supplement to your traditional credit file. Myth 1: Alternative credit data is not relevant. As consumers are shifting to new ways of gaining credit, it’s important for the industry to keep up. These data types are being captured by specialty credit bureaus. Gone are the days when alternative financing only included the payday store on the street corner. Alternative financing now expands to loans such as online installment, rent-to-own, point-of-sale financing, and auto-title loans. Consumers automatically default to the financing source familiar to them – which doesn’t necessarily mean traditional financial institutions. For example, some consumers may not walk into a bank branch anymore to get a loan, instead they may search online for the best rates, find a completely digital experience and get approved without ever leaving their couches. Alternative credit data gives you a lens into this activity. Myth 2: Borrowers with little to no traditional credit history are high risk. A common misconception of a thin-file borrower is that they may be high risk. According to the CFPB, roughly 45 million Americans have little to no credit history and this group may contain minority consumers or those from low income neighborhoods. However, they also may contain recent immigrants or young consumers who haven’t had exposure to traditional credit products. According to recent findings, one in five U.S. consumers has an alternative financial services data hit– some of these are even in the exceptional or very good credit segments. Myth 3: Alternative credit data is inaccurate and has poor data quality. On the contrary, this data set is collected, aggregated and verified in the same way as traditional credit data. Some sources of data, such as rental payments, are monthly and create a consistent look at a consumer’s financial behaviors. Experian’s Clarity Services, the leading source of alternative finance data, reports their consumer information, which includes application information and bank account data, as 99.9% accurate. Myth 4: Using alternative credit data might be harmful to the consumer. This data enables a more complete view of a consumer’s credit behavior for lenders, and provides consumers the opportunity to establish and maintain a credit profile. As with all information, consumers will be assessed appropriately based on what the data shows about their credit worthiness. Alternative credit data provides a better risk lens to the lender and consumers may get more access and approval for products that they want and deserve. In fact, a recent Experian survey found 71% of lenders believe alternative credit data will help consumers who would have previously been declined. Like putting in a new pair of contact lenses the first time, it may be uncomfortable to figure out the best use for alternative credit data in your daily rhythm. But once it’s added, it’s undeniable the difference it makes in your day-to-day decisions and suddenly you wonder how you’ve survived without it so long. See your consumers clearly today with alternative credit data. Learn More About Alternative Credit Data
Synthetic identities come from accounts held not by actual individuals, but by fabricated identities created to perpetrate fraud. It often starts with stealing a child’s Social Security number (SSN) and then blending fictitious and factual data, such as a name, a mailing address and a telephone number. What’s interesting is the increase in consumer awareness about synthetic identities. Previously, synthetic identity was a lender concern, often showing itself in delinquent accounts since the individual was fabricated. Consumers are becoming aware of synthetic ID fraud because of who the victims are — children. Based on findings from a recent Experian survey, the average age of child victims is only 12 years old. Children are attractive victims since fraud that uses their personal identifying information can go for years before being detected. I recently was interviewed by Forbes about the increase of synthetic identities being used to open auto loans and how your child’s SSN could be used to get a phony auto loan. The article provides a good overview of this growing concern for parents and lenders. A recent Javelin study found that more than 1 million children were victims of fraud. Most upsetting is that children are often betrayed by people close to them -- while only 7 percent of adults are victimized by someone they know, 60 percent of victims under 18 know the fraudster. Unfortunately, when families are in a tight spot financially they often resort to using their child’s SSN to create a clean credit record. Fraud is an issue we all must deal with — lenders, consumers and even minors — and the best course of action is to protect ourselves and our organizations.
In 2011, data scientists and credit risk managers finally found an appropriate analogy to explain what we do for a living. “You know Moneyball? What Paul DePodesta and Billy Beane did for the Oakland A’s, I do for XYZ Bank.” You probably remember the story: Oakland had to squeeze the most value out of its limited budget for hiring free agents, so it used analytics — the new baseball “sabermetrics” created by Bill James — to make data-driven decisions that were counterintuitive to the experienced scouts. Michael Lewis told the story in a book that was an incredible bestseller and led to a hit movie. The year after the movie was made, Harvard Business Review declared that data science was “the sexiest job of the 21st century.” Coincidence? The importance of data Moneyball emphasized the recognition, through sabermetrics, that certain players’ abilities had been undervalued. In Travis Sawchik’s bestseller Big Data Baseball: Math, Miracles, and the End of a 20-Year Losing Streak, he notes that the analysis would not have been possible without the data. Early visionaries, including John Dewan, began collecting baseball data at games all over the country in a volunteer program called Project Scoresheet. Eventually they were collecting a million data points per season. In a similar fashion, credit data pioneers, such as TRW’s Simon Ramo, began systematically compiling basic credit information into credit files in the 1960s. Recognizing that data quality is the key to insights and decision-making and responding to the demand for objective data, Dewan formed two companies — Sports Team Analysis and Tracking Systems (STATS) and Baseball Info Solutions (BIS). It seems quaint now, but those companies collected and cleaned data using a small army of video scouts with stopwatches. Now data is collected in real time using systems from Pitch F/X and the radar tracking system Statcast to provide insights that were never possible before. It’s hard to find a news article about Game 1 of this year’s World Series that doesn’t discuss the launch angle or exit velocity of Eduardo Núñez’s home run, but just a couple of years ago, neither statistic was even measured. Teams use proprietary biometric data to keep players healthy for games. Even neurological monitoring promises to provide new insights and may lead to changes in the game. Similarly, lenders are finding that so-called “nontraditional data” can open up credit to consumers who might have been unable to borrow money in the past. This includes nontraditional Fair Credit Reporting Act (FCRA)–compliant data on recurring payments such as rent and utilities, checking and savings transactions, and payments to alternative lenders like payday and short-term loans. Newer fintech lenders are innovating constantly — using permissioned, behavioral and social data to make it easier for their customers to open accounts and borrow money. Similarly, some modern banks use techniques that go far beyond passwords and even multifactor authentication to verify their customers’ identities online. For example, identifying consumers through their mobile device can improve the user experience greatly. Some lenders are even using behavioral biometrics to improve their online and mobile customer service practices. Continuously improving analytics Bill James and his colleagues developed a statistic called wins above replacement (WAR) that summarized the value of a player as a single number. WAR was never intended to be a perfect summary of a player’s value, but it’s very convenient to have a single number to rank players. Using the same mindset, early credit risk managers developed credit scores that summarized applicants’ risk based on their credit history at a single point in time. Just as WAR is only one measure of a player’s abilities, good credit managers understand that a traditional credit score is an imperfect summary of a borrower’s credit history. Newer scores, such as VantageScore® 4.0, are based on a broader view of applicants’ credit history, such as credit attributes that reflect how their financial situation has changed over time. More sophisticated financial institutions, though, don’t rely on a single score. They use a variety of data attributes and scores in their lending strategies. Just a few years ago, simply using data to choose players was a novel idea. Now new measures such as defense-independent pitching statistics drive changes on the field. Sabermetrics, once defined as the application of statistical analysis to evaluate and compare the performance of individual players, has evolved to be much more comprehensive. It now encompasses the statistical study of nearly all in-game baseball activities. A wide variety of data-driven decisions Sabermetrics began being used for recruiting players in the 1980’s. Today it’s used on the field as well as in the back office. Big Data Baseball gives the example of the “Ted Williams shift,” a defensive technique that was seldom used between 1950 and 2010. In the world after Moneyball, it has become ubiquitous. Likewise, pitchers alter their arm positions and velocity based on data — not only to throw more strikes, but also to prevent injuries. Similarly, when credit scores were first introduced, they were used only in originations. Lenders established a credit score cutoff that was appropriate for their risk appetite and used it for approving and declining applications. Now lenders are using Experian’s advanced analytics in a variety of ways that the credit scoring pioneers might never have imagined: Improving the account opening experience — for example, by reducing friction online Detecting identity theft and synthetic identities Anticipating bust-out activity and other first-party fraud Issuing the right offer to each prescreened customer Optimizing interest rates Reviewing and adjusting credit lines Optimizing collections Analytics is no substitute for wisdom Data scientists like those at Experian remind me that in banking, as in baseball, predictive analytics is never perfect. What keeps finance so interesting is the inherent unpredictability of the economy and human behavior. Likewise, the play on the field determines who wins each ball game: anything can happen. Rob Neyer’s book Power Ball: Anatomy of a Modern Baseball Game quotes the Houston Astros director of decision sciences: “Sometimes it’s just about reminding yourself that you’re not so smart.”
I believe it was George Bernard Shaw that once said something along the lines of, “If economists were laid end-to-end, they’d never come to a conclusion, at least not the same conclusion.” It often feels the same way when it comes to big data analytics around customer behavior. As you look at new tools to put your customer insights to work for your enterprise, you likely have questions coming from across your organization. Models always seem to take forever to develop, how sure are we that the results are still accurate? What data did we use in this analysis; do we need to worry about compliance or security? To answer these questions and in an effort to best utilize customer data, the most forward-thinking financial institutions are turning to analytical environments, or sandboxes, to solve their big data problems. But what functionality is right for your financial institution? In your search for a sandbox solution to solve the business problem of big data, make sure you keep these top four features in mind. Efficiency: Building an internal data archive with effective business intelligence tools is expensive, time-consuming and resource-intensive. That’s why investing in a sandbox makes the most sense when it comes to drawing the value out of your customer data.By providing immediate access to the data environment at all times, the best systems can reduce the time from data input to decision by at least 30%. Another way the right sandbox can help you achieve operational efficiencies is by direct integration with your production environment. Pretty charts and graphs are great and can be very insightful, but the best sandbox goes beyond just business intelligence and should allow you to immediately put models into action. Scalability and Flexibility: In implementing any new software system, scalability and flexibility are key when it comes to integration into your native systems and the system’s capabilities. This is even more imperative when implementing an enterprise-wide tool like an analytical sandbox. Look for systems that offer a hosted, cloud-based environment, like Amazon Web Services, that ensures operational redundancy, as well as browser-based access and system availability.The right sandbox will leverage a scalable software framework for efficient processing. It should also be programming language agnostic, allowing for use of all industry-standard programming languages and analytics tools like SAS, R Studio, H2O, Python, Hue and Tableau. Moreover, you shouldn’t have to pay for software suites that your analytics teams aren’t going to use. Support: Whether you have an entire analytics department at your disposal or a lean, start-up style team, you’re going to want the highest level of support when it comes to onboarding, implementation and operational success. The best sandbox solution for your company will have a robust support model in place to ensure client success. Look for solutions that offer hands-on instruction, flexible online or in-person training and analytical support. Look for solutions and data partners that also offer the consultative help of industry experts when your company needs it. Data, Data and More Data: Any analytical environment is only as good as the data you put into it. It should, of course, include your own client data. However, relying exclusively on your own data can lead to incomplete analysis, missed opportunities and reduced impact. When choosing a sandbox solution, pick a system that will include the most local, regional and national credit data, in addition to alternative data and commercial data assets, on top of your own data.The optimum solutions will have years of full-file, archived tradeline data, along with attributes and models for the most robust results. Be sure your data partner has accounted for opt-outs, excludes data precluded by legal or regulatory restrictions and also anonymizes data files when linking your customer data. Data accuracy is also imperative here. Choose a big data partner who is constantly monitoring and correcting discrepancies in customer files across all bureaus. The best partners will have data accuracy rates at or above 99.9%. Solving the business problem around your big data can be a daunting task. However, investing in analytical environments or sandboxes can offer a solution. Finding the right solution and data partner are critical to your success. As you begin your search for the best sandbox for you, be sure to look for solutions that are the right combination of operational efficiency, flexibility and support all combined with the most robust national data, along with your own customer data. Are you interested in learning how companies are using sandboxes to make it easier, faster and more cost-effective to drive actionable insights from their data? Join us for this upcoming webinar. Register for the Webinar
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