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All skip tracing data is the same, right? Not exactly. While there are many sources of consumer contact data available to debt collectors, the quality, freshness, depth and breadth can vary significantly. Just as importantly, what you ultimately do or don't do with the data depends on several factors such as: Whether or not the debt is worth your while to pursue How deep and fresh the data is What if no skip data is available, and, What happens if there is no new information available when you go to your skip-tracing vendor requesting new leads? So what's the best way for your company to locate debtors? What data sources are right for you? Check out my recent article in Collections and Credit Risk for some helpful advice, and be sure to check out our other debt collection industry blog posts for best practices, tips and tricks on ways to recover more debt, faster. What data sources do you find most beneficial to your business and why? Let us know by commenting below.

By: Maria Moynihan Fact:  In fiscal year 2011, the federal government allocated ~$608M to investigate and prosecute cases of alleged fraud in health care programs Fact:  Medicare and Medicaid related scams cost taxpayers more than $60B a year These statistics are profound, especially when so many truly need–and rightfully deserve–access to health benefits.  To make the facts a bit more tangible, how would you feel if you heard that neighbors of yours were submitting claims to Medicare for treatments that were never provided? In essence, you’ve got thieves for neighbors, don’t you? Thankfully, government agencies are responding. Even while being challenged with reduced budgets and limited resources; they are investing in efficient processes, advanced data, analytics and decisioning tools to improve their visibility into individuals at the point of application. By simply making adjustments to one or all of these areas, agencies can pinpoint whether or not individuals are who they say they are. Only with precision, relevancy, and efficiency of information, can fraud and abuse be curtailed. Below are a few examples of how to improve your eligibility systems or processes today. Or, simply download the Issue Brief, Beyond Traditional Eligibility Verification, for more detail. Use scores, models, and screening questions to assess a beneficiary’s true identity or level of identity fraud risk. Use income and asset estimation models to compare to stated income as a validation step in determination of benefits eligibility. Create a single system for automatic identification and verification of beneficiaries and businesses applying for service. Tighten controls around business identity to weed out fraud rings, syndicates and other forms of business fraud. The Bottom Line: Only with process, information, or system improvements, can government agencies move the needle on the growing and pressing issue of fraud and abuse.

By: Maria Moynihan Cyber Monday recently passed and I'm curious to know if you were one of the many who contributed to the $1.465 billion spend online that day?  ‘Tis the season - not only for increased online shopping, but for increased ID theft or risk of fraudulent activity. With a quick online search, you can find some good tips on how to protect your information.  Here’s a great read on password protection. Other sources offer added tips, like the below, when submitting information online: 1)  Ensure sensitive information is secure before submitting 2) Only access websites you know you can trust 3)  Be sure you are comfortable with the information your mobile device is asking you to provide in specific apps Beyond the holidays and even beyond the type of organization you are interacting with, these online tips apply. Government agencies for instance, encourage similar cautionary behavior when interacting with them. In fact, several have even implemented tools and processes to ensure the proper level of information security, authentication, and checking occur. Take the Social Security Administration for example. Here is an agency that implemented a secure process for individuals to access their benefits online. By incorporating a step to quickly and efficiently cross check an individual’s identity, the agency was able to validate information, ensuring people seeking access to their information are truly who they say they are. Watch a video to see how the Social Security Administration offers secure real-time access to individuals’ benefits. And, most importantly, keep these important information safety tips in mind every day and enjoy a stress-free and peaceful holiday!  

Research shows that investing in superior customer management easily can exceed returns of 20 percent in the first year of implementation.  A return that compounds in subsequent years as a results of customer-centric strategies that drive customer's loyalty, new customer referrals, and increased revenue opportunities.  Customer loyalty is a key driver that differentiates retail banks when trying to retain existing and attract new customers.   And cited by customers themselves as the way to win their business today.  Achieving superior customer management, however, can be expensive and operationally prohibitive; and let's not to forget to mention there are a number of different approaches that aim to meet such a standard, but fail because critical qualitative insights are not captured in back-end systems of record (SOR).  These "black-box" strategies struggle to be widely adopted across the enterprise and die a slow, internal political death - with wasted resources left on the floor.  It also leaves the customer feeling frustrated and dissatisfied, maybe even ready to flee. One such example was recently illustrated in an article in Credit Union Times.   Changing the retail bank's approach to adopt best practices in developing holistic customer-centric strategies is paramount to the improvement of the customer experiences, and the bottom line. Quantitative data alone can represent only a partial view of reality whereas holistic customer strategies exploit the full value of the enterprise by synthesizing customer knowledge from SOR with external off-your firm financial information and critical qualitative input from customer-facing staff.   Customer-facing staff are critical in the adoption of such strategies and need to be actively engaged to extract customer learnings that will lead to the modification and alignment of customer-level treatement strategy designs and predictive models with the real world.   A collaborative approach, blending art and science, ensures complete adoption across the enterprise and measurable customer experience improvements that can be monetized for shareholders through improved customer retention and new customer acquisitions.  Get access to details on the framework to design and deploy such customer-centric strategies. 

By: Maria Moynihan The public sector is not unlike the private sector when it comes to data. Both require accuracy and relevancy for optimized processes and decision-making. For government agencies, maintaining a holistic view of constituents is more important than ever. By linking data across department systems, governments improve operations, citizen profiling and overall record management.  No longer do agencies have to muddle through records of Maria Moynihan, Mari Moynihan, M Moynihan, and other variations of name or contact information when they all are truly one in the same. Unfortunately, without the right tools and know how, database maintenance, record deduplication, and account validation can be a daunting process.  Below are five critical steps to helping government agencies execute successful linkage of database records: Step 1: Engage stakeholders Data stewards are not mind readers. They work with finite data and rely on stakeholders to provide insight. Seek input from users across departments and functions. Step 2: Identify impacts and priorities Data errors and disparate data prevent stewards from amalgamating records and defining a master database. Focus on areas of strategic priority. Step 3: Create success criteria Look for and set quantifiable metrics for matching. Consider what data needs to be linked and what thresholds are acceptable given objectives. Step 4: Define new standards Create established workflows and guidelines for evaluating, merging and purging records. Step 5: Leverage matching technology Integrate robust deduplication tools to design multiple workflows and handle a variety of matching challenges. In short, without data stewards seeking input from commercial stakeholders, an understanding of the data impacts, and establishing a clear process including defined methodologies and technology for deduplication, government agencies will remain challenged in trying to figure out if Maria, Mari, and M are the same person in databases. Click here to see the full guide to Creating a Single View.  

With the constant (and improving!) changes in the consumer credit landscape, understanding the latest trends is vital for institutions to validate current business strategies or make adjustments to shifts in the marketplace.  For example, a recent article in American Banker described how a couple of housing advocates who foretold the housing crisis in 2005 are now promoting a return to subprime lending. Good story lead-in, but does it make sense for MY business?  How do you profile this segment of the market and its recent performance?  Are there differences by geography?  What other products are attracting this risk segment that could raise concerns for meeting a new mortgage obligation?   There is a proliferation of consumer loan and credit information online from various associations and organizations, but in a static format that still makes it challenging to address these types of questions. Fortunately, new web-based solutions are being made available that allow users to access and interrogate consumer trade information 24x7 and keep abreast of constantly changing market conditions.  The ability to manipulate and tailor data by geography, VantageScore® credit score risk segments and institution type are just a mouse click away.  More importantly, these tools allow users to customize the data to meet specific business objectives, so the next subprime lending headline is not just a story, but a real business opportunity based on objective, real-time analysis. Explore the features from one such tool available.  

Gone are the days when validating scoring models was a thing you did when you got around to it. Besides that fact that the OCC wants models validated at least once a year, it’s just good business sense to make sure your tools are working as expected. At a minimum, the OCC wants back testing, stress testing, benchmarking and sensitivity analysis, but there is another aspect to validations that needs to be taken into consideration. Most lenders do not rely exclusively on a scoring model of their decisioning (or at least they shouldn’t). Whether it’s a dual score strategy or attribute overlay, additional underwriting criteria is often used to help refine and optimize decision strategies. However, those same overlays need to be incorporated into the model validation process so that the results are not misleading. VantageScore® Solutions, LLC has just published a concise white paper offering excellent examples of how to make sure your overlay criteria are an integral part of the overall validation process, ensuring your effort here are yielding the right results. And while on the topic of model validation, next time I’ll review what to do when you have no idea what to test for. Stay tuned!

It comes as no surprise to anyone that cell phone usage continues to rise, while at the same time the usage of wire lines, or what used to be affectionately known as POTS (Plain Old Telephone Service), continues to decline. Some recent statistics, supplied by the CDC show that: 34% of all households are now wireless only 25 states have rates of primary wireless exceeding 50% Landline only households is now down to only 10.2% When you couple that with churn rates for cell phones that can exceed 40% a year, it becomes paramount to find a good source for cell numbers if you are trying to contact an existing customer or collect on an overdue bill. But where can debt collectors go to find reliable cell phone numbers? The cell phone providers won’t sell you a database, there is no such thing as 411 for cell phones, nor is it likely there will be one in the near future with the aforementioned 40%+ churn rates. Each cell phone service provider will continue to protect their customer base. There are a few large compilers of cell phone numbers; they mostly harvest these numbers from surveys and sources that capture the numbers as a part of an online service—think ringtones here! These numbers can be good, at least initially, if they came with an address which enables you to search for them. The challenge is that these numbers can grow stale relatively quickly. Companies that maintain recurring transactions with consumers have a better shot at having current cell numbers. Utilities and credit bureaus offer an opportunity to capture these self-reported numbers. At our company, over 40% of self-reported phones are cell phones. However, in most cases, you must have a defined purpose as governed by Gramm Leach Bliley (GLB) in order to access them. Of course, the defined purpose also goes hand in hand with the Telephone Consumer Protection Act (TCPA), which restricts use of automatic dialers and prohibits unsolicited calls via a cell phone. Conclusion? If you are trying to find someone’s cell number for debt collection purposes, I recommend using a resource more likely to receive updates on the owner of a cell over that of compilers who are working with one time event data. In today’s world, obtaining an accurate good cell number is a challenge and will continue to be. What cell phone number resources have been most effective for you?

Contributed by: David Daukus As the economy recovers from the recession, consumers are becoming more responsible with their credit card usage; credit card debts have not increased and delinquency rates have declined. Delinquency rates as a percentage of balances continue to decline with the short term 30-59 DPD period, now at 0.9%. With mixed results, where is the profit opportunity? Further studies from Experian-Oliver Wyman state that the average bankcard balance per consumer remained relatively flat at $4,170, but the highest credit tiers (using VantageScore® credit score A and B segments) saw average balances increase to $2,422 and $3,208, respectively. It's time to focus on what you have—your current portfolio—and specifically how to: Increase credit card usage in the prime segments Assign the right lines to your cardholders Understand who has the ‘right’ spend Risk score alone doesn't provide the most accurate insight into consumer accounts. You need to dig deeper into individual accounts to uncover behavioral trends to get the critical information needed to grow your portfolio:  Leading financial institutions are looking at consumer payment history, such as balance and utilization changes. These capture a consumer’s credit situation more accurately than a point in time view. When basic principles are applied to credit data, different consumer behaviors become evident and can be integrated into client strategies. For example, if two consumers have the same VantageScore® credit score, credit card balances, and payment status, does that mean they have the same current credit status? Not necessarily so. By looking at their payment history, you can determine which direction each is heading. Are they increasing their debt or are they paying down their debt? These differences reveal their riskiness and credit needs. Therefore, with payment history added to the mix, you can more accurately allocate credit lines between consumers and simultaneously reduce risk exposure. Spend is another important metric to evaluate to help grow your portfolio. How do you know if a consumer uses primary a credit card when making purchases? Wouldn’t you want to know the right amount of credit to provide based on the consumer’s need? Insight into consumer spending levels provides a unique understanding of a consumer’s credit needs. Knowing spend allows lenders to provide necessary high lines to the limited population of very high spenders, while reducing overall exposure by providing lower lines to low spenders. Spend data also reveals wallet share—knowing the total spend of your cardholder allows you to calculate their external spend. With wallet share data, you can capture more spend by adjusting credit lines or rewards that will entice consumers to spend more using your card. Once you have a more complete picture of a consumer, adjusting lines of credit and making the right offer is much easier. Take some of the risk out of managing your existing customers and finding new ones. What behavioral data have you found most beneficial in making lending decisions?   Source: Experian-Oliver Wyman Market Intelligence Reports

I'm here in Vegas at the Mobile2020 conference and I am fascinated by my room key. This is not the usual “insert in to the slot, wait for it turn green or hear it chime” key cards, these are “tap and hold to a door scanner till the door opens” RFID key card. It is befitting the event I am about to attend – Money2020 – the largest of its kind bringing together over 2000 mobile money aficionados, strategists and technologists from world over for a couple of days to talk about how payment modalities are shifting and the impact of these shifts to existing and emerging players. Away from all the excitement of product launches, I hope some will be talking about one of the major barriers for consumer adoption towards alternate payment modalities such as mobile – security and fraud.  I was in Costa Mesa last week and in the process of buying something for my wife with my credit card, triggered the card fraud alert. My card was declined and I had to use a different card to complete my transaction. As I was walking out, my smartphone registers a text alert from the card issuer – asking me to confirm that it was actually I who attempted the transaction. And If so, Respond by texting 1 – if Yes Or 2 – if No. All good and proper up till this point. If someone had stolen my card or my identity, this would have been enough to stop fraud from re-occurring. In this scenario the payment instrument and the communication device were separate – my plastic credit card and my Verizon smartphone. In the next couple of years, these two will converge, as my payment instrument and my smartphone will become one. At that point, will the card issuer continue to send me text alerts asking for confirmation? If instead of my wallet, my phone was stolen – what good will a text alert to that phone be of any use to prevent the re-occurrence of fraud? Further if one card was shut down, the thief could move to other cards with in the wallet – if, just as today, there are no frameworks for fraud warnings to permeate across other cards with in the wallet. Further, fraud liability is about to shift to the merchant with the 2013 EMV Mandate. In the recent years, there has been significant innovation in payments – to the extent that we have a number of OTT (Over the Top) players, unencumbered by regulation, who has been able to sidestep existing players – issuers and card networks, in positioning mobile as the next stage in the evolution of payments. Google, PayPal, Square, Isis (a Carrier consortium formed by Verizon, T-Mobile and AT&T), and a number of others have competing solutions vying for customer mind share in this emerging space. But when it comes to security, they all revert to a 4 digit PIN – what I call as the proverbial fig leaf in security. Here we have a device that offers a real-time context – whether it be temporal, social or geo-spatial – all inherently valuable in determining customer intent and fraud, and yet we feel its adequate to stay with the PIN, a relic as old as the payment rails these newer solutions are attempting to displace. Imagine what could have been – in the previous scenario where instead of reaching for my card, I reach for my mobile wallet. Upon launching it, the wallet, leveraging the device context, determines that it is thousands of miles away from the customer’s home and should score the fraud risk and appropriately ask the customer to answer one or more “out-of-wallet” questions that must be correctly answered. If the customer fails, or prefers not to, the wallet can suggest alternate ways to authenticate – including IVR. Based on the likelihood of fraud, the challenge/response scenario could include questions about open trade lines or simply the color of her car. Will the customer appreciate this level of pro-activeness on the issuer’s part to verify the legality of the transaction? Absolutely. Merchants, who so far has been on the sidelines of the mobile payment euphoria, but for whom fraud is a real issue affecting their bottom-line, will also see the value. The race to mobile payments has been all about quickly shifting spend from plastic to mobile, and incenting that by enabling smartphones to store and deliver loyalty cards and coupons. The focus need to shift, or to include, how smartphones can be leveraged to address and reduce fraud at the point-of-sale – by bringing together context of the device and a real-time channel for multi-factor authentication. It’s relevant to talk about Google Wallet (in its revised form) and Fraud in this context. Issuers have been up in arms privately and publicly, in how Google displaces the issuer from the transaction by inserting itself in the middle and settles with the merchant prior to firing off an authorization request to the issuer on the merchant’s behalf. Issuers are worried that this could wreak havoc with their inbuilt fraud measures as the authorization request will be masked by Google and could potentially result in issuer failing to catch fraudulent transactions. Google has been assuaging issuer’s fears on this front, but has yet to offer something substantial – as it clearly does not intent to revert to where it was prior – having no visibility in to the payment transaction (read my post here). This is clearly shaping up to be an interesting showdown – would issuers start declining transactions where Google is the merchant of record? And how much more risk is Google willing to take, to become the entity in the middle? This content is a re-post from Cherian's personal blog: http://www.droplabs.co/?p=625

Part 2:  Common myths about credit risk scores and how to educate consumers In light of what I've heard in the marketplace through the years, I wanted to provide some information to help 'debunk' some common myths about credit scores. Myth: There is only one credit score Reality: There are multiple credit scores that lenders can use to evaluate consumer credit worthiness. As noted in a recent New York Times article, there are 49 FICO score models. Make sure your customers know that an underwriting decision is based on more than just a credit score—multiple factors are evaluated to make a lending decision. The most important thing a consumer can do is ensure their credit report is accurate. Myth:  The probability of default remains constant for a credit score over time Reality:  The probability of default can shift dramatically based on macro-economic conditions. In 2005, a score of 700 in any given model, may have had a probability of default of 2 percent, while in 2009, the same score could have had a probability of default of 8 percent. This underscores the value of conducting an annual validation of the credit model you are using to ensure your institution is making the most accurate lending decisions based on your risk tolerance. One of the benefits of utilizing the VantageScore® model, is that VantageScore® Solutions, LLC, produces an annual validation so you can ensure your institution is adjusting your strategies to meet changing economic conditions. Myth:  If the underlying credit report is the same at each credit reporting company, I will have the same score at each company Reality:  Traditional credit scoring models are completely different at each credit reporting company, which leads to vastly different scores or probabilities of default based on the same information. As a risk manager, this is very frustrating, as I may not understand which score most accurately assess the consumer’s probability of default. The only model that is the same across all credit reporting agencies is the VantageScore® model, where this is a patented feature that ensures the lender receives a consistent score (probability of default) across all bureau platforms. I hope these brief examples help clear up some confusion about credit scores. In Part 3 of this series, I will outline how to evaluate the risk of traditionally unscoreable consumers. If you have any thoughts or experiences from a lending perspective, please feel free to share them below.   Courtesy Why You Have 49 Different FICO Scores in the August 27, 2012 issue of the New York Times

By: Kyle Aiman Let’s face it, debt collectors often get a bad rap.  Sure, some of it is deserved, but the majority of the nation’s estimated 157,000 collectors strive to do their job in a way that will satisfy both their employer and the debtor.  One way to improve collector/debtor interaction is for the collector to be trained in consumer credit and counseling. In a recent article published on Collectionsandcreditrisk.com, Trevor Carone, Vice President of Portfolio and Collection Solutions at Experian, explored the concept of using credit education to help debt collectors function more like advisors instead of accusers.  If collectors gain a better understanding of consumer credit – how to read a credit report, how items may affect a credit score, how a credit score is compiled and what factors influence the score – perhaps they can offer suggestions for improvement. Will providing past-due consumers with a plan to help improve their credit increase payments?  Read the article and let us know what you think!

By: Mike Horrocks It has been over a year that in Zuccotti Park the Occupy Wall Street crowd made their voices heard.  At the anniversary point of that movement, there has been a lot of debate on if the protest has fizzled away or is still alive and planning its next step.  Either way, it cannot be ignored that it did raise a voice in how consumers view their financial institutions and what actions they are willing to take i.e. “Bank Transfer Day”. In today’s market customer risk management must be balanced with retention strategies.  For example, here at Experian we value the voice of our clients and prospects and I personally lead our win/loss analysis efforts.  The feedback we get from our customers is priceless.   In a recent American Banker article, some great examples were given on how tuning into the voice of the consumer can turn into new business and an expanded market footprint. Some consumers however will do their talking by looking at other financial institutions or by slowly (or maybe rapidly) using your institution’s services less and less.   Technology Credit Union saw great results when they utilized retention triggers off of the credit data to get back out in front of their members with meaningful offers.     Maximizing the impact of internal data and spotting the customer-focused trends that can help with retention is even a better approach, since that data is taken at the “account on-us” level and can help stop risks before the customer starts to walk out the door. Phillip Knight, the founder of Nike once said, “My job is to listen to ideas”.  Your customers have some of the best ideas on how they can be retained and not lost to the competitors.  So, think how you can listen to the voice and the actions of your customers better, before they leave and take a walk in the park.

By: Maria Moynihan State and local governments responsible for growth may be missing out on an immediate and sizeable revenue opportunity if their data and processes for collections are not up to par. The Experian Public Sector team recently partnered with Governing Magazine to conduct a nationwide survey with state and local government professionals to better understand how their debt collections efforts are helping to address current revenue gaps. Interestingly enough, 81% stated that the economic climate has negatively impacted their collections efforts, either through reduced staff or reduced budgets, while 30% of respondents are actively looking for new technologies to aid in their debt collections processes. New technologies are always a worthwhile investment. Operational efficiencies will ultimately ensue, but those government organizations who are coupling this investment with improved data and analytics are even better positioned to optimize collections processes and benefit from growth in revenue streams. No longer does the public sector need to lag behind the private sector in debt recovery. With the total outstanding debt among the 50 states reaching an astounding size of approximately $631 billion dollars, why delay? Check out Experian's guide to improving debt collections efforts in the public sector. What is your agency doing to capitalize on revenue from overdue obligations?

By: Teri Tassara Negative liquidity, or owing more on your home than its value, has become a much too common theme in the past few years.  According to CoreLogic, 11 million consumers are underwater, representing 1 out of 4 homeowners in the nation.  The irony is with mortgage rates remaining at historic lows, consumers who can benefit the most from refinancing can’t qualify due to their negative liquidity situation. Mortgage Banker’s Association recently reported that approximately 74% of home loan volumes were mortgage re-finances in 2Q 2012.  Consumers who have been able to refinance to take advantage of the low interest rates already have, some even several times over.  But there is a segment of underwater consumers who are paying more than their scheduled amount in order to qualify for refinancing – which translates to growth opportunity in mortgage loan volume. Based on an Experian analysis of actual payment amount on mortgages, actual payment amount was reported on about 65% of open mortgages (actual payment amount is the amount the consumer paid the prior month).  And when the actual payment is reported, the study found that 82% of the consumers pay within their $100 of scheduled payment and 18% pay more than their scheduled amount. Actual payment amount information as reported on the credit file, used in combination with other analytics, can be a powerful tool to identify viable candidates for a mortgage refinance, versus those who may benefit from a loan modification offer.  Consumers methodically paying more than the scheduled payment amount may indicate that the consumer is trying to qualify for refinancing.  Conversely, if the consumer is not able to pay the scheduled payment about, that consumer may be an ideal candidate for a loan modification program.   Either way, actual payment amount can provide insight that can create a favorable situation for both the consumer and the lender, mitigating additional and unnecessary risk while providing growth opportunity. Find other related blog posts on credit and housing market trends.