Tag: analytics
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.
“We don’t know what we don’t know.” It’s a truth that seems to be on the minds of just about every financial institution these days. The market, not-to-mention the customer base, seems to be evolving more quickly now than ever before. Mergers, acquisitions and partnerships, along with new competitors entering the space, are a daily headline. Customers expect the same seamless user experience and instant gratification they’ve come to expect from companies like Amazon in just about every interaction they have, including with their financial institutions. Broadly, financial institutions have been slow to respond both in the products they offer their customers and prospects, and in how they present those products. Not surprisingly, only 26% of customers feel like their financial institutions understand and appreciate their needs. So, it’s not hard to see why there might be uncertainty as to how a financial institution should respond or what they should do next. But what if you could know what you don’t know about your customer and industry data? Sound too good to be true? It’s not—it’s exactly what Experian’s Ascend Analytical Sandbox was built to do. “At OneMain we’ve used Sandbox for a lot of exploratory analysis and feature development,” said Ryland Ely, a modeler at Experian partner client, OneMain Financial and a Sandbox user. For example, “we’ve used a loan amount model built on Sandbox data to try and flag applications where we might be comfortable with the assigned risk grade but we’re concerned we might be extending too much or too little credit,” he said. The first product built on Experian’s big data platform, Ascend, the Analytical Sandbox is an analytics environment that can have enterprise-wide impact. It provides users instant access to near real-time customer data, actionable analytics and intelligence tools, along with a network of industry and support experts to drive the most value out of their data and analytics. Developed with scalability, flexibility, efficiency and security at top-of-mind, the Sandbox is a hybrid-cloud system that leverages the high availability and security of Amazon Web Services. This eliminates the need, time and infrastructure costs associated with creating an internally hosted environment. Additionally, our web-based interface speeds access to data and tools in your dedicated Sandbox all behind the protection of Experian’s firewall. In addition to being supported by a revolutionized tech stack backed by an $825 million annual investment, Sandbox enables use of industry-leading business intelligence tools like SAS, RStudio, H2O, Python, Hue and Tableau. Where the Ascend Sandbox really shines is in the amount and quality of the data that’s put into it. As the largest, global information services provider, the Sandbox brings the full power of Experian’s 17+ years of full-file historical tradeline data, boasting a data accuracy rate of 99.9%. The Sandbox also allows users the option to incorporate additional data sets including commercial small business data and soon real estate data, among others. Alternative data assets add to the 50 million consumers who use some sort of financial service, in addition to rental and utility payments. In addition to including Experian’s data on the 220+ million credit-active consumers, small business and other data sets, the Sandbox also allows companies to integrate their own customer data into the system. All data is depersonalized and pinned to allow companies to fully leverage the value of Experian’s patented attributes and scores and models. Ascend Sandbox allows companies to mine the data for business intelligence to define strategy and translate those findings into data visualizations to communicate and win buy-in throughout their organization. But here is where customers are really identifying the value in this big data solution, taking those business intelligence insights and being able to take the resulting models and strategies from the Sandbox directly into a production environment. After all, amassing data is worthless unless you’re able to use it. That’s why 15 of the top financial institutions globally are using the Experian Ascend Sandbox for more than just benchmarking and data visualization but also risk modeling, score migration, share of wallet, market entry, cross-sell and much more. Moreover, clients are seeing time-savings, deeper insights and reduced compliance concerns as a result of consolidating their production data and development platform inside Sandbox. “Sandbox is often presented as a tool for visualization or reporting, sort of creating summary statistics of what’s going on in the market. But as a modeler, my perspective is that it has application beyond just those things,” said Ely. To learn more about the Experian Ascend Analytical Sandbox and hear more about how OneMain Financial is getting value out of the Sandbox, watch this on-demand webinar.
Picking up where we left off, online fintech lenders face the same challenges as other financial institutions; however, they continue to push the speed of evolution and are early adopters across the board. Here’s a continuation of my conversation with Gavin Harding, Senior Business Consultant at Experian. (Be sure to read part 1.) Part two of a two-part series: As with many new innovations, fintechs are early adopters of alternative data. How are these firms using alt data and what are the results that are being achieved? In a competitive market, alternative data can be the key to helping fintechs lend deeper and better reach underserved consumers. By augmenting traditional credit data, a lender has access to greater insights on how a thin-file consumer will perform over time, and can then make a credit decision based on the identified risk. This is an important point. While alternative data often helps lenders expand their universe, it can also provide quantitative risk measures that traditional data doesn’t necessarily provide. For example, alternative data can recognize that a consumer who changes residences more than once every two years presents a higher credit risk. Another way fintechs are using alternative data is to screen for fraud. Fraudsters are digitally savvy and are using technology to initiate fraud attacks on a broader array of lenders, in bigger volumes than ever before. If I am a consumer who wants to get a loan through an online fintech lender, the first thing the lender wants to know is that I am who I say I am. The lender will ask me a series of questions and use traditional data to validate. Alternative data takes authentication a step further and allows lenders to not only identify what device I am using to complete the application, but whether the device is connected to my personal account records – giving them greater confidence in validating my identity. A second example of using alternative data to screen for fraud has to do with the way an application is actually completed. Most individuals who complete an online application will do so in a logical, sequential order. Fraudsters fall outside of these norms – and identifying these patterns can help lenders increase fraud detection. Lastly, alternative data can help fintech lenders with servicing and collections by way of utilizing behavioral analytics. If a consumer has a history of making payments on time, a lender may be apt to approve more credit, at better terms. As the consumer begins to pay back the credit advance, the lender can see the internal re-payment history and recommend incremental line increases. From your perspective, what is the future of data and what should fintechs consider as they evolve their products? The most sophisticated, most successful “think tanks” have two things that are evolving rapidly together: Data: Fintechs want all possible data, from a quality source, as close to real-time as possible. The industry has moved from “data sets” to “data lakes” to “data oceans,” and now to “data universes.” Analytics: Fintechs are creating ever-more sophisticated analytics and are incorporating machine learning and artificial intelligence into their strategies. Fintechs will continue to look for data assets that will help them reach the consumer. And to the degree that there is a return on the data investment, they will continue to capitalize on innovative solutions – such as alternative data. In the competitive financial marketplace, insight is everything. Aite Group recently conducted a new report about alternative data that dives into new qualitative research collected by the firm. Join us to hear Aite Group’s findings about fintechs, banks, and credit unions at their webinar on December 4. Register today! Register for the Webinar Click here for more information about Experian’s Alternative Data solutions. Don’t forget to check out part one of this series here. About Gavin Harding With more than 20 years in banking and finance Gavin leverages his expertise to develop sophisticated data and analytical solutions to problem solve and define strategies across the customer lifecycle for banking and fintech clients. For more than half of his career Gavin held senior leadership positions with a large regional bank, gaining experience in commercial and small business strategy, SBA lending, credit and risk management and sales. Gavin has guided organizations through strategic change initiatives and regulatory and supervisory oversight issues. Previously Gavin worked in the business leasing, agricultural and construction equipment sectors in sales and credit management roles.
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
Big Data is no longer a new concept. Once thought to be an overhyped buzzword, it now underpins and drives billions in dollars of revenue across nearly every industry. But there are still companies who are not fully leveraging the value of their big data and that’s a big problem. In a recent study, Experian and Forrester surveyed nearly 600 business executives in charge of enterprise risk, analytics, customer data and fraud management. The results were surprising: while 78% of organizations said they have made recent investments in advanced analytics, like the proverbial strategic plan sitting in a binder on a shelf, only 29% felt they were successfully using these investments to combine data sources to gather more insights. Moreover, 40% of respondents said they still rely on instinct and subjectivity when making decisions. While gut feeling and industry experience should be a part of your decision-making process, without data and models to verify or challenge your assumptions, you’re taking a big risk with bigger operations budgets and revenue targets. Meanwhile, customer habits and demands are quickly evolving beyond a fundamental level. The proliferation of mobile and online environments are driving a paradigm shift to omnichannel banking in the financial sector and with it, an expectation for a customized but also digitized customer experience. Financial institutions have to be ready to respond to and anticipate these changes to not only gain new customers but also retain current customers. Moreover, you can bet that your competition is already thinking about how they can respond to this shift and better leverage their data and analytics for increased customer acquisition and engagement, share of wallet and overall reach. According to a recent Accenture study, 79% of enterprise executives agree that companies that fail to embrace big data will lose their competitive position and could face extinction. What are you doing to help solve the business problem around big data and stay competitive in your company?
At their heart, car dealers have always been marketers. It\'s part of learning the trade and understanding the business to gain natural insight into modern marketing and advertising practices. One could even argue that the experience gained through knowledge passed down, trial and error, and exposure to the automotive game itself can yield better strategies than a marketing degree. With all that said, it\'s still important to have the right data to guide the decisions as well as the tools necessary to decipher the data. Although we have a vast amount of information at our fingertips, it\'s very possible to truly build on \"actionable data\" and allow it to define the parameters for a dealership\'s marketing strategy. One of the most important things to consider when you\'re building and enhancing your strategies is that the data allows for decision making on the macro and micro levels. We see trend reports, analytics, and test cases that can influence decisions on both sides of the spectrum. Making decisions on the macro level means wholesale changes or additions. For example, the overall effectiveness of a particular classified advertising website can be broken down to determine whether or not it\'s making the right type of impact. Dealers have so many options today to advertise both online and offline, so making sure that any particular venue is effective is key to success. On the micro level, decisions can be made about how to position the dealership within the individual venues. You may be a big believer in search pay-per-click advertising, for example, and data can help to guide you or your vendor partners to position the dealership properly on search. Knowing which messages about individual cars are effective can be a guide. Then, understanding what zip codes have the highest opportunity level for the individual model can mold your PPC spend, while demographic data can drive effective messaging and help you optimize campaign creative and landing pages. Having access to the data is only the first step. Looking at the data appropriately is an important second step that many dealers are missing. Putting it all together into a decision-driving model is the step that almost every dealer should embrace to allow them to make the best decisions, macro or micro.
The auto industry has been riding a wave of prosperity for the past seven years, bouncing back nicely from the 2008 market collapse. But, it looks like rising sales of the past 10 years, are, well...a thing of the past. According to Alix Partners, 2016 sales of 17.5 million units might be the high-water sales mark, at least through 2022. Alix Partners says the next five years sales will range between 15.6 million to 16.8 million annually. Suddenly, it will be challenging for dealers to stay in strong growth mode. How can dealers best react to the tightening market? The Experian white paper “Data Tools Evolve to Give Dealers an Edge in a Tight Sales Market” takes a look at how new and improved data and analytic tools can provide deeper insights to help automotive retailers unlock sales. The paper reviews current market sales statistics, historical sales trends and how dealers reacted during similar market conditions in the past. In addition, the paper provides a look at the challenges faced by automotive retailers, in terms of shrinking gross profit, higher advertising expenses and increased competition. Automotive retailers also will find information on the importance of customer conquesting and a look at technology tools to help provide a deeper understanding and actionable intelligence about local markets. Data and analytics are no longer the private purview of large mega-dealers. The Experian white paper outlines today’s data tools that can be implemented quickly and cost effectively by dealers of any size. To learn more about these trends, download the paper here: https://www.experian.com/automotive/dealerwhitepaper.html
We regularly hear from clients that charge-offs are increasing and they’re struggling to keep up with the credit loss. Many clients use the same debt collection strategy they’ve used for years – when businesses or consumers can’t repay a loan, the creditor or collection agency aggressively contacts them via phone or mail to obtain repayment – never considering the customer experience for the debtor. Our data shows that consumers accounted for $37.24 billion in bankcard charge-offs in Q2 2017, a 17.1 percent increase from Q2 2016. Absorbing credit losses at such a high rate can impact the sustainability of the institution. Clearly the process could use some adjusting. Traditionally, debt collection has been solely about the money. The priority was ensuring that as much of the outstanding debt as possible was repaid. But collecting needs to be about more than that. It also should focus on the customer and his or her individual situation. When it comes to debt collection, customers should not all be treated the same way. I recently shared some tips in Credit Union Business Magazine about how to actively engage and collect from members. The same holds true for other financial institutions – they need to know the difference between a customer who has simply forgotten to make a payment and one who is dealing with financial hardship. As an example, if a person is current on his or her mortgage payment but has slipped behind on his or her credit card payment, that doesn’t necessarily signify financial hardship. It’s an opportunity to work with the customer to manage the debt and get back to current. Modern financial institutions build acquisition and customer management strategies targeted at individuals, so why should the collection process be any different? The challenge is keeping the customer at the center while also managing against potential increases in delinquencies. This holistic approach may be slightly more complex, but technology and analytics will simplify the process and bring about a more engaging experience for customers. The Power of Data and Technology Instead of relying on the same outdated collections approach – which results in uncomfortable exchanges on the phone that don’t ensure repayment –leverage data to your advantage. The data and technology exists to help you make more informed decisions, such as: What’s the most effective communication channel to reach the defaulting customer? When should you contact him or her? How often? The best course of action could be high-touch outreach, but sometimes doing nothing is the right approach. It all depends on the situation. Data and analytics can help uncover which customers are most likely to pay on their own and those who may need a little more help, allowing you to adjust your treatment strategy accordingly. By catering to the preferences of the customer, there’s a greater chance for a positive experience on both sides. The results: less charge-off debt, higher customer satisfaction and a stronger relationship. Explore the Digital Age In 2016, 36 million Americans made some form of mobile payment—paying a bill, purchasing something online, or paying for fast food, or making a Mobile Wallet purchase at a retailer. By 2020, nearly 184 million consumers will have done so, according to Aite. Consumers expect and deserve convenience. In the digital world, financial institutions have an opportunity to provide that expectation and then some. Imagine a customer being able to negotiate and manage his or her past-due account virtually, in the privacy of his or her own home, when it’s most convenient, to set their payment dates and terms. Luckily, the technology exists to make this vision a reality. Customers, not money, need to be at the heart of every debt collections strategy. Gone are the days of mass phone calls to debtors. That strategy made consumers unhappy, embarrassed and resentful. Successful debt collection comes down to a basic philosophy: Treat customers and his or her unique situation individually rather than as a portfolio profile. The creditors who live by that philosophy have an opportunity to reap the rewards on the back-end.
Part 3 in our series on Insights from the Vision 2016 fraud and identity track Our Vision 2016 fraud track session titled “Deployment Made Easy — solving new fraud problems by Adapting Legacy Solutions” offered insights into the future of analytics and the mechanisms for delivering them. The session included two case studies, the first of which highlighted a recently completed project in which an Experian client struggling with rising application fraud losses had to find a way to deploy advanced analytics without any IT resources. To assist the customer, data passing through an existing customer interface was reformatted and redirected to our Precise ID® platform. Upon arrival in Precise ID, a custom-built fraud scoring model was invoked. The results were then translated back into the format used by the legacy interface so that they could be ingested by the customer’s systems. This case study illustrates the key value proposition of Experian’s new CrossCoreTM fraud and identity platform. CrossCore features a similar “translation layer” for inquiries coming into Experian’s fraud and identity tools that will allow customers to define fraud-screening workflows that call a variety of services. The IT burden for connecting the inquiry to various Experian and non-Experian services will fall on Experian — sparing the customer from the challenge of financing and prioritizing IT resources. Similarly, the output from CrossCore will provide a ready-to-consume response that integrates directly with our customers’ host systems. The audience showed keen interest in the “here and now” illustration of what CrossCore will enable. Our second case study was provided by Eric Heikkila at Amazon Web Services™ and focused on the future of analytics. For an audience accustomed to the constraints of developing advanced analytics in a rigid data-structure, Amazon’s description of a “data lake” was a fascinating picture of what’s possible. The data lake offers the simultaneous ability to accommodate existing structured customer data along with new unstructured data in an infinitely scalable data set. Equally important is the data lake’s ability to accommodate an unlimited array of data mining and analytical tools. Amazon’s message was clear and simple — the fraud industry’s trepidation around the use of big data is misplaced. The fear of making the wrong choice of data storage and analytical tools is unnecessary. To illustrate this point, Eric shared an Amazon Web Services case study that used FINRA (Financial Industry Regulatory Authority). FINRA is responsible for overseeing U.S. securities markets to ensure that rules are followed and integrity is maintained. Amid a bewildering set of ever-changing regulations and peak volumes of 35 trillion per day — yes, trillion — Amazon’s data lake supports both the scale and analytical demands of a complex industry. As the delivery and access to fraud products is made easy by CrossCore, the data and analytics will expand through the use of services like Amazon’s data lake. As the participants will agree, the future of fraud technology is closer than you think!
It’s the “Battle of the Sexes” credit edition. Who sports higher scores, less debt and more on-time payments? According to Experian’s latest analysis, women take the credit title. Thank you very much. The report analyzed multiple categories including credit scores, average debt, number of open credit cards, utilization ratios, mortgage amounts and mortgage delinquencies of men and women in the United States. Results revealed: Women’s average credit score of 675 compared to men’s score of 670 Women have 3.7 percent less average debt than men Women have 23.5 percent more open credit cards Women and men have the same revolving utilization ratio of 29.9 percent Women’s average mortgage loan amount is 7.9 percent less than men’s Women have a lower incidence of late mortgage payments by 8.1 percent “There were several gaps between men and women in this study, including the five-point credit score lead that the women hold,” said Michele Raneri, Experian’s Vice President of Analytics and New Business Development. “Even with more credit cards, women have fewer overall debts and are managing to pay those debts on time.” The report also takes a look at the vehicle preferences of men and women and how those choices play into their overall credit and financial health. Below are the top-line results: Women were more likely to purchase a more functional, utilitarian vehicle, while men tended to lean toward sports cars and trucks The top three vehicle segments men purchased in 2015 were mid-size pickup trucks, large pickup trucks and standard specialty cars. In fact, they were 1.37 times more likely to purchase a mid-sized pickup truck than the general population The top three vehicle segments for women were small crossover-utility vehicles, mid-size sports-utility vehicles and compact crossover-utility vehicles. Women were 1.40 times more likely to purchase the small crossover-utility vehicle than the general population Experian conducted a similar study, comparing men and women on various credit attributes in 2013. At that time, women also scored higher than men in the credit score category - holding steady with a 675 VantageScore compared to the men’s 674 VantageScore, but the gap has widened, with the men’s score further lowering to 670. While men’s scores have dropped since 2013, the overall financial health for both sexes is strong. Most notably, the mortgage 60-plus delinquency rate has dropped significantly. In the 2013 pull, men were tracking at 5.7 percent and women were 5.3 percent. Today, those numbers have dropped to .86 percent for men and .79 percent for women. What a difference a few years has made in regards to the recovering housing market. Time will tell if the country’s state of credit will continue to trend higher, as indicated in the 2015 annual report, or if the buzz of potential recession and an election year will reverse the positive trend. As for now, the women once again claim bragging rights as it pertains to credit. Analysis methodology The analysis is based on a statistically relevant, sampling of depersonalized data of Experian’s consumer credit database from December 2015. Gender information was obtained from Experian Marketing Services.
Experian data shows consumers are more confident managing their credit since the recession. The Q3 2015 Experian Market Intelligence Brief was released today featuring data that highlights consumer credit card debt has now reached its highest level since Q4 2009. Credit card debt levels reached $650 billion in Q3 2015, the highest it has been since Q4 2009 when it was $667 billion. Credit card delinquency rates on outstanding balances 60 or more days past due have decreased 71 percent during the same time period. Combining those indicators with the national unemployment rate dropping 50 percent during the same span illustrates a positive economic outlook on credit card trends among lenders and consumers. “Overall credit card limits have increased 102 percent since Q4 2009 with $82 billion originated in Q3 2015,” said Kelly Kent, vice president of Experian Decision Analytics. “The increase in limits from lenders and the steady climb in credit card debt combined with exceptional delinquency rates signals greater confidence among consumers as they are showing more assurance in managing their credit since the recession. We expect to see credit card debt increase in Q4 based on historical seasonal trends driven by the holiday shopping season especially with the early positive holiday sales as a sign.” The Q3 2015 Experian Market Intelligence Brief report is now available.
The overarching ‘business driver’ in adopting a risk-based authentication strategy, particularly one that is founded in analytics and proven scores, is the predictive ‘lift’ associated with using scoring in place of a more binary rule set. While basic identity element verification checks, such as name, address, Social Security number, date-of-birth, and phone number are important identity proofing treatments, when viewed in isolation, they are not nearly as effective in predicting actual fraud risk. In other words, the presence of positive verification across multiple identity elements does not, alone, provide sufficient predictive value in determining fraud risk. Positive verification of identity elements may be achieved in customer access requests that are, in fact, fraudulent. Conversely, negative identity element verification results may be associated with both ‘true’ or ‘good’ customers as well as fraudulent ones. In other words, these false positive and false negative conditions lead to a lack of predictive value and confidence as well as inefficient and unnecessary referral and out-sort volumes. The most predictive authentication and fraud models are those that incorporate multiple data assets spanning traditionally used customer information categories such as public records and demographic data, but also utilize, when possible, credit history attributes, and historic application and inquiry records. A risk-based fraud detection system allows institutions to make customer relationship and transactional decisions based not on a handful of rules or conditions in isolation, but on a holistic view of a customer’s identity and predicted likelihood of associated identity theft, application fraud, or other fraud risk. To implement efficient and appropriate risk-based authentication procedures, the incorporation of comprehensive and broadly categorized data assets must be combined with targeted analytics and consistent decisioning policies to achieve a measurably effective balance between fraud detection and positive identity proofing results. The inherent value of a risk-based approach to authentication lies in the ability to strike such a balance not only in a current environment, but as that environment shifts as do its underlying forces.
By: Staci Baker For the one-third of the U.S. population that rents, in the past, rental payment history has not been included in determining a credit score. With the acquisition of RentBureau by Experian, renters’ credit can now be affected by on-time payments or account delinquency, the same as an individual that owns a home. Why is it important to include rental payment history? Including rental payment history in credit score data strengthens the analytics used to compile credit scores by giving a more complete picture of an individual’s payment history. For consumers with no history on which to build a credit score, this allows them to create track record of continuous, on time, repayment. I believe the power this brings to multi-family owned units is the ability to quickly and easily determine who is either a low credit risk, or higher credit risk when leasing an apartment. As a property manager or resident screener, the risk that is taken on new tenants can be high. There are many unknown variables regarding a tenant’s credit worthiness, even once an application is completed – do they have a good history of making payments on time, did they fill out their application truthfully, will they be a good neighbor and many more. Now that the credit risk management of applicants includes rental payment history in the consumer credit file, it will identify better quality residents, reduce delinquency rates and lead to greater collections management.
To calculate the expected business benefits of making an improvement to your decisioning strategies, you must first identify and prioritize the key metrics you are trying to positively impact. For example, if one of your key business objectives is improved enterprise risk management, then some of the key metrics you seek to impact, in order to effectively address changes in credit score trends, could include reducing net credit losses through improved credit risk modeling and scorecard monitoring. Assessing credit risk is a key element of enterprise risk management and can addressed as part of your application risk management processes as well as other decisioning strategies that are applied at different points in the customer lifecycle. In working with our clients, Experian has identified 15 key metrics that can be positively impacted through optimizing decisions. As you review the list of metrics below, you should identify those metrics that are most important to your organization. • Approval rates • Booking or activation rates • Revenue • Customer net present value • 30/60/90-day delinquencies • Average charge-off amount • Average recovery amount • Manual review rates • Annual application volume • Charge-offs (bad debt & fraud) • Avg. cost per dollar collected • Average amount collected • Annual recoveries • Regulatory compliance • Churn or attrition Based on Experian’s extensive experience working with clients around the world to achieve positive business results through optimizing decisions, you can expect between a 10 percent and 15 percent improvement in any of these metrics through the improved use of data, analytics and decision management software. The initial high-level business benefit calculation, therefore, is quite important and straightforward. As an example, assume your current approval rate for vehicle loans is 65 percent, the average value of an approved application is $200 and your volume is 75,000 applications per year. Keeping all else equal, a 10 percent improvement in your approval rates (from 65 percent to 72 percent) would generate $10.7 million in incremental business value each year ($200 x 75,000 x .65 x 1.1). To prioritize your business improvement efforts, you’ll want to calculate expected business benefits across a number of key metrics and then focus on those that will deliver the greatest value to your organization.
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