Data & Analytics

Prescriptive solutions can synthesize big data, analytics, and business strategies to provide businesses an optimized workflow to reach a final decision.

Consumer card balance transfer activity is estimated to be $35B to $40B a year. Identify these consumers before they make transfers by using trended data.

Organizations are beginning to use data to optimize or improve nearly every aspect of their organization. Make your data quality business case.

Experian estimates card-to-card consumer balance transfer activity to be between $35 and $40 billion a year, representing a sizeable opportunity for proactive lenders seeking to grow their revolving product line. This opportunity, however, is a threat for reactive lenders that only measure portfolio attrition instead of working to retain current customers. While billions of dollars are transferred every year, this activity represents only a small percentage of the total card population. And given the expense of direct marketing, lenders seeking to capitalize on and protect their portfolio from balance transfer activity must leverage data insights to make more informed decisions. Predicting a consumer’s future propensity to engage in card-to-card balance transfers starts with trended data. A credit score is a snapshot in time, but doesn’t reveal deep insights about a consumer’s past balance transfer activity. Lenders that rely only on current utilization will group large populations of balance revolvers into one bucket – and many of these individuals will have no intention of transferring to another product in the near future. Still, balance transfer activity can be identified and predicted by utilizing trended data. By analyzing the spend and payment data over time to see when one (or multiple) trade’s payment approximately matches another trade’s spend, we have the logic that suggests there has been a card-to-card transfer. What most people don’t realize is that trended data is difficult to work with. With 24 months of history on five fields, a single trade includes 120 data points. That’s 720 data points for a consumer with six trades on file and 72,000,000 for a file with 100,000 records, not to mention the other data fields in the file. It’s easy to see why even the most sophisticated organizations become paralyzed working with trended data. While teams of analysts get buried in the data, projects drag, costs swell, and eventually the world changes as rates climb and fall. By the time the analysis is complete, it must be recalibrated. But there is a solution. Experian has developed powerful predictions tools that combine past balance transfer history, historical transfer amounts, current trades carried and utilized, payments, and spend. Combined, these data fields can help identify consumers who are most likely to transfer a balance in the future. With Experian’s Balance Transfer Index the highest scoring 10 percent of consumers capture nearly 70 percent of total balance transfer dollars. Imagine the impact on ROI of reducing 90 percent of the marketing cost of your next balance transfer campaign and still reaching 70 percent of the balance transfer activity. Balance transfer activity represents a meaningful dollar opportunity for growth, but is concentrated in a small percentage of the population making predictive analytics key to success. Trended data is essential for identifying those opportunities, but financial institutions must assess their capabilities when it comes to managing the massive data attached. The good news is that regardless of financial institution size, solutions now exist to capture the analytics and provide meaningful and actionable insights to lenders of all sizes.

While organizations increasingly rely on data to make decisions, when it comes to data accuracy, too many wait to correct errors rather than implement proactive solutions.

It’s impossible to capture all of the insights and learnings of 36 breakout sessions and several keynote addresses in one post, but let’s summarize a few of the highlights from the first day of Vision 2016. 1. Who better to speak about the state of our country, specifically some of the threats we are facing than Leon Panetta, former Secretary of Defense and Director of the CIA. While we are at a critical crossroads in the United States, there is room for optimism and his hope that we can be an America in Renaissance. 2. Alex Lintner, Experian President of Consumer Information Services, conveyed how the consumer world has evolved, in large part due to technology: 67 percent of consumers made purchases across multiple channels in the last six months. More than 88M U.S. consumers use their smartphone to do some form of banking. 68 percent of Millennials believe within five years the way we access money will be totally different. 3. Peter Renton of Lend Academy spoke on the future of Online Marketplace Lending, revealing: Banks are recognizing that this industry provides them with a great opportunity and many are partnering with Online Marketplace Lenders to enter the space. Millennials are not the largest consumers in this space today, but they will be in the future. Sustained growth will be key for this industry. The largest platforms have everything they need in place to endure – even through an economic downturn.In other words, Online Marketplace Lenders are here to stay. 4. Tom King, Experian’s Chief Information Security Officer, addressed the crowds on how the world of information security is growing increasingly complex. There are 1.9 million records compromised every day, and sadly that number is expected to rise. What can businesses do?  “We need to make it easier to make the bad guys go somewhere else,” says King. 5. Look at how the housing market has changed from just a few years ago: Inventory continues to be extraordinarily lean. Why? New home building continues to run at recession levels. And, 8.5 percent of homeowners are still underwater on their mortgage, preventing them from placing it on the market. In the world of single-family home originations, 2016 projections show that there will be more purchases, less refinancing and less volume. We may see further growth in HELOC’s. With a dwindling number of mortgages benefiting from refinancing, and with rising interest rates, a HELOC may potentially be the cheapest and easiest way to tap equity. 6. As organizations balance business needs with increasing fraud threats, the important thing to remember is that the customer experience will trump everything else. Top fraud threats in 2015 included: Card Not Present (CNP) First Party Fraud/Synthetic ID Application Fraud Mobile Payment/Deposit Fraud Cross-Channel FraudSo what do the experts believe is essential to fraud prevention in the future? Big Data with smart analytics. 7. The need for Identity Relationship Management can be seen by the dichotomy of “99 percent of companies think having a clear picture of their customers is important for their business; yet only 24 percent actually think they achieve this ideal.” Connecting identities throughout the customer lifecycle is critical to bridging this gap. 8. New technologies continue to bring new challenges to fraud prevention. We’ve seen that post-EMV fraud is moving “upstream” as fraudsters: Apply for new credit cards using stolen ID’s. Provision stolen cards into mobile wallet. Gain access to accounts to make purchases.Then, fraudsters are open to use these new cards everywhere. 9. Several speakers addressed the ever-changing regulatory environment. The Telephone Consumer Protection Act (TCPA) litigation is up 30 percent since the last year. Regulators are increasingly taking notice of Online Marketplace Lenders. It’s critical to consider regulatory requirements when building risk models and implementing business policies. 10. Hispanics and Millennials are a force to be reckoned with, so pay attention: Millennials will be 81 million strong by 2036, and Hispanics are projected to be 133 million strong by 2050. Significant factors for home purchase likelihood for both groups include VantageScore® credit score, age, student debt, credit card debt, auto loans, income, marital status and housing prices. More great insights from Vision coming your way tomorrow!          

Who will take the coveted Super Bowl title in 2016? Now that we’re down to the final two teams, the commentary will heighten. Sportscasters, analysts, former athletes, co-workers ... even your local barista has an opinion. Will it be Peyton Manning's Denver Broncos or the rising Carolina Panthers? Millions will make predictions in the coming weeks, but a little research can go a long way in delivering meaningful insights. How have the teams been trending over the season? Are there injuries? Who is favored and what’s the spread? Which quarterback is leading in pass completions, passing yards, touchdowns, etc.? Who has been on this stage before, ready to embrace the spotlight and epic media frenzy? The world of sports is filled with stats resulting from historical data. And when you think about it, the world of credit could be treated similarly. Over the past several years, there has been much hype about “credit invisibles” and the need to “score more.” A traditional pull will likely leave many “no-file” and “thin-file” consumers out, so it’s in a lender’s best interest to leverage alternative scoring models to uncover more. But it’s also important to remember a score is just a snapshot, a mere moment in time. How did a consumer arrive to that particular score pulled on any given day? Has their score been trending up or down? Has an individual been paying off debt at a rapid pace or slipping further behind?   Two individuals could have the exact same score, but likely arrived to that place differently. The backstory is good to know – in sports and in the world of credit. Trended data can be attached to balances, credit limits, minimum payment due, actual payment and date of payment. By assessing these areas on a consumer file for 24 months, more insights are delivered and lenders can take note of behavior patterns to assist with risk assessment, marketing and share-of-wallet analysis. For example, looking closer at those consumers with five trades or more, Experian trended data reveals: 27% are revolvers, carrying balances each month 27% are transactors, paying off large portions, or all of their balances 9% are rate surfers, who tend to frequently transfer balances to credit cards with 0% or low introductory rates. Now these consumers can be viewed beyond a score. Suddenly, lenders can look within or outside their portfolio to understand how consumers use credit, what to offer them, and assess overall profitability. In short, trended data provides a more detailed view of a borrower’s historical credit performance, and that richness makes for a more informed decision. Without a doubt, there is power in the score – and being able to score more – but when it comes time to place your bets, the trended data matters, adding a whole new dimension to an individual’s credit score. Place your wagers accordingly. As for who will win Super Bowl 2016? I haven’t a clue. I’m more into the commercials. And I hear Coldplay is on for the half-time show. If you’re betting, best of luck, and do your homework.

Businesses must be vigilant and apply comprehensive, data-driven customer intelligence to thwart breaches and the malicious use of breached information.

As Big Data becomes the norm in the credit industry and others, the seemingly non-stop efforts to accumulate more and more data leads me to ask the question - when is Big Data too much data?  The answer doesn’t lie in the quantity of data itself, but rather in the application of it – Big Data is too much data when you can’t use it to make better decisions. So what do I mean by a better decision? From any number of perspectives, the answer to that question will vary. From the viewpoint of a marketer, maybe that decision is about whether new data will result in better response rates through improved segmentation. From a lender perspective, that decision might be about whether a borrower will repay a loan or the right interest rate to charge the borrower. That is one the points of the hype around Big Data – it is helping companies and individuals in all sorts of situations make better decisions – but regardless of the application, it appears that the science of Big Data must not just be based on an assumption that more data will always lead to better decisions, but that more data can lead to better decisions – if it is also the “right data”. Then how does one know when another new data source is helping? It’s not obvious that additional data won’t help make a better decision. It takes an expert to understand not only the data employed, but ultimately the use of the data in the decision-making process. It takes expertise that is not found just anywhere. At Experian, one of our core capabilities is based on the ability to distinguish between data that is predictive and can help our clients make better decisions, and that which is noise and is not helpful to our clients.  Our scores and models, whether they be used for prospecting new customers, measuring risk in offering new credit, or determining how to best collect on an outstanding receivable, are all designed to optimize the decision making process. Learn more about our big data capabilities

A recent Experian study on data insights found that 83% of chief information officers see data as a valuable asset that is not being fully exploited within their organization, resulting in the need for more organizations to appoint a dedicated chief data officer (CDO).

Data migrations are very common in today’s business environment. A recent Experian Data Quality study found that while 91% of businesses engage in data migrations, 85% encounter significant challenges.

Data quality continues to be a challenge for many organizations.

By: Barbara Rivera Every day, 2.5 quintillion bytes of data are created – in fact, 90% of the world’s data was created in only the last few years. With the staggering amount of data available, we have an unprecedented opportunity to uncover new insights and improve the way our world functions. The implications of these new capabilities are perhaps nowhere else as crucial as within our government. Public sector officials carry the great responsibility of conducting complex missions that directly affect our communities, our economy, and our nation’s future. The ability to make more informed, insightful choices and better decisions is paramount. Especially at a time of broader global unrest and uncertainty, Americans rely on our government to be transparent, fair, ready and to make the right decisions – our trust is in the hands of our elected officials and public servants. Data alone is not enough to inform and affect change. However, with integrated information assets, insightful analysts and collaborative processes, data can be transformed into something meaningful and actionable. Our government has already begun leveraging data for good across agencies and varied missions, with more potential unlocked each day. Local governments like Orange County, California are utilizing data through address verification services to keep their voting lists accurate – ensuring the integrity of elections and saving the taxpayers thousands of dollars otherwise wasted on mailings to outdated lists. The Orange County Registrar of Voters – the fifth largest voting jurisdiction in the county – has been able to cancel 40,000 voting records, with an estimated savings of $94,000 expected from 2012 through 2016. The examples are numerous and growing: A suite of optimization tools helps states find non-custodial parents, determine their capacity and likelihood to pay child support, and trigger alerts with new critical information, maximizing the likelihood of payment and recovery, ultimately improving the welfare of children and reducing poverty More than 150 state, county and local law enforcement agencies leverage data to help identify persons of interest, conduct background screening for employees and contractors and provide financial backgrounds for criminal investigations, ensuring our continued safety By using the power of data to manage user authentication, credentials and access controls, the government is working harder – and smarter – to protect our security The government is leveraging verified commercial data to help agencies validate the fiscal responsibility of potential contractors and monitor existing contractors, which helps provide transparency and reduce risk By using data and analytics to authenticate applicants and validate financial data, the government is ensuring access to benefits for those who meet eligibility requirements, while at the same time reducing fraud Private sector partners are supporting municipal efforts to improve financial stability in households by providing the current credit standing of consumers and monitoring overall changes in financial behaviors over time, to help counsel and educate citizens And that’s only the beginning. The possibilities are endless – from healthcare to finance to energy – data can be leveraged for the advancement of our society. It even happens behind the scenes, working to protect information in ways most citizens never realize. Data insights are used to ensure citizens have secure online access to their information – ever see those randomized, personal questions? That’s data at work. The same technology is the de facto ID Proofing standard for the VA and CMS. How does it all work? By combing through the data carefully, putting it in context, looking at it in new ways, and thinking about what all this information really means. Much of this is made possible through public-private partnerships between the government and companies like Experian. So the next time someone complains about the slow pace of government, let them know the truth is government is moving quickly, leveraging data and private sector partnerships to uncover new insights that impact the greater good.

This is the third post in a three-part series. Experian® is not a doctor. We don’t even play one on TV. However, because of our unique business model and experience with a large number of data providers, we do know data governance. It is a part of our corporate DNA. Our experiences across our many client relationships give us unique insight into client needs and appropriate best practices. Note the qualifier — appropriate. Just as every patient is different in his or her genetic predispositions and lifestyle influences,  every institution is somewhat unique and does not have a similar business model or history. Nor does every institution have the same issues with data governance. Some institutions have stabile growth in a defined footprint and a history of conservative audit procedures. Others have grown quickly through aggressive acquisition marketing plans and unique channels and via institution acquisition/merger, leading to multiple receivable systems and data acquisition and retention platforms. Experian has provided valuable services to both environments many times throughout the years. As the regulatory landscape has evolved, lenders/service providers demand a higher level of hands-on experience and regulatory-facing credibility. Most recently, lenders have required assistance on the issues driven by mandates coming from the Comprehensive Capital Analysis and Review (CCAR), Office of the Comptroller of the Currency (OCC) and the Consumer Financial Protection Bureau (CFPB) bulletins and guidelines. Lenders are best served to begin their internal review of their data governance controls with a detailed individual attribute audit and documentation of findings. We have seen these reviews covering  fewer than 200 attributes to as many as more than 1,000 attributes. Again, the lender/provider size, analytic sophistication and legacy growth and IT issues will influence this scope. The source and definition of the attribute and any calculation routines should be fully documented. The life cycle stage of attribute acquisition and usage also is identified, and the fair lending implication regarding the use of the attribute across the life cycle needs to be considered and documented. As part of this comprehensive documentation, variances in intended definition and subsequent design and deployment are to be identified and corrective action guidance must be considered and documented for follow-up. Simultaneously, an assessment of the current risk governance policies, processes and documentation typically is undertaken. A third party frequently is leveraged in this review to ensure an objective perspective is maintained. This initiative usually is a series of exploratory reviews and a process and procedures assessment with the appropriate management team, risk teams, attribute design and development personnel, and finally business and end-user teams, as necessary. From these interviews and the review of available attribute-level documentation, documents depicting findings and best practices gap analysis are produced to clarify the findings and provide a hierarchy of need to guide the organization’s next steps: A more recent evolution in this data integrity ecosystem is the implication of leveraging a third party to house and manipulate data within client specifications. When data is collected or processed in “the cloud,” consistent data definitions are needed to maintain data integrity and to limit operational costs related to data cleansing and cloud resource consumption. Maintaining the quality of customer personal data is a critical compliance and privacy principle. Another challenge is that of maintaining cloud-stored data in synchronization with on-premises copies of the same data. Delegation to a third party does not discharge the organization from managing risk and compliance or from having to prove compliance to the appropriate authorities. In summary, a lender/service provider must ensure it has developed a rigorous data governance ecosystem for all internal and external processes supporting data acquisition, retention, manipulation and utilization: A secure infrastructure includes both physical and system-level access and control. Systemic audit and reporting are a must for basic compliance standards. If data becomes corrupted, alternative storage, backup or other mechanisms should be available to protect the information. Comprehensive documentation must be developed to reveal the event, the causes and the corrective actions. Data persistence may have multiple meanings. It is imperative that the institution documents the data definition. Changes to the data must be documented and frequently will lead to the creation of a new data attribute meeting the newer definition to ensure that usage in models and analytics is communicated clearly. Issues of data persistence also include making backups and maintaining multiple archive copies. Periodic audits must validate that data and usage conform to relevant laws, regulations, standards and industry best practices. Full audit details, files used and reports generated must be maintained for inspection. Periodic reporting of audit results up to the board level is recommended. Documentation of action plans and follow-up results is necessary to disclose implementation of adequate controls. In the event of lost or stolen data, appropriate response plans and escalation paths should be in place for critical incidents. Throughout this blog series, we have discussed the issues of risk and benefits from an institution’s data governance ecosystem. The external demands show no sign of abating. The regulators are not looking for areas to reduce their oversight. The institutional benefits of an effective data governance program are significant. Discover how a proven partner with rich experience in data governance, such as Experian, can provide the support your company needs to ensure a rigorous data governance ecosystem. Do more than comply. Succeed with an effective data governance program.

Data quality continues to be a challenge for many organizations as they look to improve efficiency and customer interaction.