Margin test

Health information security breaches and identity theft have become an epidemic with losses occurring across the country. In fact, according to a recent Ponemon Institute study sponsored by the Medical Identity Fraud Alliance, medical ID theft has increased by 21.7 percent since 2013. Additionally, data from the Department of Health and Human Services indicates that health data on more than 120 million people has been compromised in more than 1,100 separate breaches since 2009. In May 2015, CareFirst BlueCross BlueShield, the largest health insurer in the Mid-Atlantic region, reported a cyber-attack that affected 1.1 million past and present customers. This comes on the heels of the February 2015 data breach at Anthem, the second-largest health insurer in the United States that affected about 80 million customers, and Premera Blue Cross’ reported cyber-attack that may impact as many as 11 million people. These attacks reflect an unsettling pattern in cybercrime as identity thieves expand their target from the financial sector into healthcare.The mere fact that health records are now digital makes them a prime target. Providers have now moved rapidly into the digital space and many don’t deploy the same robust security measures taken by their banking counterparts. Furthermore, patients now have unprecedented access to their health information thanks to the widespread use of patient portals. With providers, payers, pharmacies, labs and patients all having access to sensitive records, information security becomes vulnerable to the weakest link in the data chain. To compound the issue, stolen medical identity information is extremely valuable. While a purloined credit card number might fetch $10 on the black market, a stolen medical identity can bring in more than five times that amount. So, what’s the solution? Other major industries including financial services, telecommunications and insurance have been using Big Data and analytics for years to protect their online portals, minimize risk and reduce fraud losses. When applied in a healthcare setting, it is these same techniques that will enable professionals to gain insights that can be turned into actions to protect patient data. For example, identity-matching tools can confirm whether a patient or a physician is who they claim to be, and analyzing data and usage characteristics can more effectively assess the risk of a patient’s remote interaction. In essence, by utilizing these techniques, data can be a force for good – good for the patient, good for the healthcare provider and good for the industry.

With rising insurance costs, deductibles and copays, some people struggle to afford the out-of-pocket expense that can come with seeking medical treatment. Because of this, some consumers decide not to seek treatment, which could have negative effects on their health and overall well-being. While it’s true healthcare organizations do provide financial assistance and often have charity programs to help offset the costs, most do not have the financial resources to absorb a substantial increase in patient debt that is being driven by consumers selecting high deductible health insurance plans then not being able to pay. The additional challenge is that many hospitals and healthcare providers do not have the means to quickly and accurately determine which patients qualify for charity programs, which are able to pay for care, and which patients need payment plans to help them soften the blow from an unforeseen healthcare event. To help address the problem, Experian provides hospitals, medical offices and clinics with unique data and analytics to provide insight into each patient’s financial situation. By leveraging healthcare-specific predictive models, Experian enables healthcare organizations to easily and efficiently determine which patients qualify for financial assistance programs. In short, Experian is using its data for good by helping make patients aware that they qualify for federal benefits or financial assistance, and effectively pairing them with the right program. From a provider standpoint, the data and insight that we provide not only enables them to determine which patients meet the requirements for Medicaid and other grant or charity programs, but also allows them to do so during the registration process, saving them time and effort on the back-end. Gaining insight into a patient’s financial situation also enables healthcare organizations to minimize or avoid potential bad debt, and improve reimbursement rates by connecting patients with financial programs or setting up a payment plan that fits within their current budget. The bottom line is, in order for healthcare organizations to continue to exist and assist patients in need, it’s important for them to remain financially secure. When healthcare organizations are better able to identify the difference between the patients who can pay versus those that are truly in need of and qualify for financial assistance, everybody wins. The patient doesn’t have to worry about a financial burden that they can’t afford and the healthcare organization can operate without the threat of closure. In order to protect their financial well-being, it’s important for healthcare organizations to identify those patients who qualify for financial assistance and those who can afford treatment. Dan Johnson, Experian’s Executive Vice President of Healthcare Strategy, discusses how big data can help answer that question.

A recent study conducted by Experian showed that a majority of vacationers overspend their budgets and rely on credit cards to provide extra funds. At the extreme end, more than half of millennial vacationers (52 percent) lean heavily on their credit cards, racking up vacation debt they’ll be repaying long after their trip comes to an end. The study also found that vacationers are only too happy to take a holiday from their normal good identity protection behaviors, as well. Whether a preventative action before the trip or a check-in after vacation ends, travelers are skipping easy opportunities to keep private information secure: for instance, only 38 percent of vacationers keep sensitive information protected in hotel safes while on holiday, and a disappointing 65 percent don’t have password protection enabled on their mobile phones. As summer begins, don’t give up on the strides you’ve made to spend responsibly and keep your private information secure. Staying in touch with your budget and protection practices year-round mean you won’t be off course when it’s time for a Labor Day weekend barbecue. View the complete survey findings and methodology here: Experian Summer Travel and Budgeting Survey Report, 2015 from Experian_US

Nowadays, whenever you hear news about the automotive industry, a negative tone tends to pop up. Whether it’s the increase in lending to subprime consumers, or the lengthening in loan terms, the stories lead one to believe that the industry is headed toward another “bubble.” However, that’s not necessarily the case. When we look at the data, the automotive finance market actually demonstrates a strong industry as a whole. Yes, it’s true that subprime lending is up. But, lending has increased across all risk tiers. In fact, loans to super prime consumers have actually seen the largest increase in volume compared to last year, approximately 8.5 percent. To take it a step further, the market share of loans to subprime consumers has decreased from a year ago. At its bare bones, what it means is that consumers are not only purchasing cars, but they are taking out loans to do so. Furthermore, given the percentage of loans extended to each risk tier, we see that lenders have not opened up their portfolios to increased risk. Both of which are positive indications of a strong market. We’ve also seen a steady increase in the length of loan terms. However, before anyone comes to any rash conclusions, it’s not as ominous a sign as it may seem. As cars and trucks have become more expensive to purchase, the easiest way for consumer to keep their monthly payments affordable has been to extend the life of their loans. That said, it’s critical for consumers to understand that by taking out a longer loan, they may need to hold onto the vehicle longer to avoid facing negative equity should they trade it in after a few years. An alternate route many consumers have taken to keep their monthly payments affordable has been leasing. In the first quarter of 2015, we saw leasing account for 30.2 percent of all new financed vehicles – its highest level on record. At the end of the day, consumers are continuing to purchase vehicles and that’s a positive sign for the industry. By gaining a deeper understanding of current automotive financing trends, lenders will be able to use the data and insights to their benefit by better meeting the needs of the marketplace and mitigating the risk of their portfolios. And if they do that, the good times can continue to roll for the industry.

The term big data tends to be overused in business today. While some refer to it as a technology and others a level of insight, it has come to embody many different data actions, from business intelligence, to analytics and data modelling. We have become so obsessed with big data that we think we have to have this level of insight as a requirement to running a successful business. And for the most part, that statement is true. Data has proliferated our society to the point that every decision is made with some influence of data. Certainly experience, gut instinct and advice play a critical role, but data has become one of our most constant advisors. We rely on information at a business level for location expansion, product fulfilment, customer loyalty and marketing. But, that same feeling also translates over to our habits as consumers when we rely on data to help make decisions on where to eat dinner, where to buy a home, what businesses to shop with, etc. Data has really changed the way we operate as a society. But as all of us jump onto this big data bandwagon, it is important to remember that big data is not always insightful. The speed at which information is gathered and the volumes we are dealing with today can make information more relevant, but it can also be riddled with errors. Big data has become too big for us to manage. There is a high degree of inaccurate information in businesses today. Recent Experian Data Quality research reveals that almost all businesses have a problem with their data and on average, U.S. businesses believe about 30 percent of their data is inaccurate. That is a shocking figure and shows the degree to which businesses trust their information. Without trust, business stakeholders certainly can’t perform big data analytical exercises and use them to make intelligent decisions about their business. But why is that trust lacking? We frequently see that data can be inaccurate, incomplete or just unconsolidated for a full understanding of the customer. It also can go against the conventional gut wisdom, leaving some executives to disregard it entirely. We tend to like data when it agrees with what we are already thinking. To develop trust around data, we need to realign our expectations. While a third of information may be inaccurate, what does that mean? Is the information you are actually trying to analyze inaccurate? Most of us do not touch the majority of our information assets for insight. So what does it matter if the information we are not accessing is accurate? We need to understand the true need for data in our business. We need to consider how to use data as a force for good. What it really boils down to is being able to access, use and trust data. Information does not have to be perfect for us to achieve that and we don’t have to be able to utilize every data set within our system. To make big data work, businesses need to look at their own needs and decide what is good enough for them. What are the benchmarks within their business that they need to meet to trust and access information for analysis? That means that organizations need to link data across channels and databases, put data governance practices in place and move quickly to ensure information can be used across not just IT, but also across various business stakeholders. In a world dominated by data and technology, we are being forced to adapt. We need to make decisions based on new information rather than purely gut instinct, but we have to make sure the information we are reviewing provides the right insight. Too much data can be problematic. We can get bogged down in it and become unable to make decisions. We have to sift out what actually makes sense to review and what we should discard. Big data doesn’t always have to be this massive effort. It needs to be small and manageable, fit for your business. No two big data efforts are the same. Be sure that as you consider big data within your organization, you are ensuring the accuracy of information and that the data makes sense for each particular project.