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.  

Six states are the top producers of turkeys: Minnesota at 46 million, North Carolina at 36 million, Arkansas at 29 million, Missouri at 17.5 million, Virginia at 17 million and Indiana at 16.5 million. This accounts for nearly two-thirds of turkeys produced in the United States as of September 2012. The average wholesale price for frozen whole turkey during fourth-quarter 2012 is projected to range from $1.10 to $1.14 per pound -- similar to the 2011 fourth-quarter average price of $1.11 per pound. The average retail price for whole frozen turkeys in September 2012 was $1.62, about 6 cents lower than the average retail price for whole frozen turkeys in September 2011. Source: National Agricultural Statistics Service (NASS), Agricultural Statistics Board and United States Department of Agriculture (USDA).

According to a recent Ponemon Institute study, 44 percent of consumers who were notified about a data breach believed the breached company was hiding something. When data breaches occur, it is extremely important to be there for customers and to address their concerns. When companies hide a data breach, impacted consumers begin to suspect the breach is actually much worse than the company claims, and trust in the organization begins to wane. Find out more by downloading the data breach case study of lessons learned from the field.

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!

When validating a model in the presence of overlay criteria, it is important to remember that any metrics computed at the aggregate portfolio level will not be indicative of the model's true performance. While traditional validation methodologies and portfolio metrics may provide directional insight into model performance, the overlay strategy is an additional variable that must be accounted for in each step of the validation analysis. An effective validation should include: Establishment of an appropriate base line Piece-wise validation of overlay segments An overlay strategy analysis Do you have model validation questions? Learn more and transform your business goals with Experian's Analytical Consulting Services. Source: VantageScore® Solutions LLC white paper: Validating a Credit Score Model in Conjunction with Additional Underwriting Criteria. VantageScore® is owned by VantageScore Solutions, LLC.

By: Joel Pruis The commercial lending - traditional C&I, CRE and other - segment is one of the last areas to be “automated” or captured within an automated lending platform.   Many of us talk about the need to automate this segment but the discussion needs to start with the question of “What does it mean to automate originations in the commercial segment?  Let’s start to break this down and define it. Previously, we have covered how to define small business for your respective financial institution.  If you use that as a measurement of what is small business, the remaining segment would by default be your commercial segment.  It seems obvious but good to re-iterate to keep the context on commercial. What we are not planning to cover is the small business segment where there is relatively high application volume and low total dollar production.  If we compare small business and commercial across two major characteristics, we the distinction becomes more clear. The above chart represents the typical situation – the probable not the possible scenario.  For example, there are situations where the sales lead time is days not months for a commercial lending opportunity or a small business application can sometimes take over a year to get the application. I like analogies so let’s compare mass produced furniture vs. custom furniture to small business vs. commercial.  Mass produced furniture is high volume but low dollar per unit (small business) while the custom furniture is the low volume but high dollar per unit production (commercial). Basically, the furniture being mass produced has a low need for any customization with high demand and a low cost of production on a per unit basis.  Conversely, the custom furniture production has relatively low volume but a higher cost of production on a per unit basis. The custom furniture maker has no set designs, no set product line but rather examples of past work that has been done.  There are no set materials that are to be used and no set prescribed method for manufacturing any particular item. While one customer may want a dining room table that has leaves to expand the seating as needed, another may want a drop-leaf table or simply a static table top.  It is up to the customer to decide what the criteria is to best suit the need.  The talented furniture maker will provide his/her expertise to provide the best product/solution for the customer but the end result is ultimately up to the customer.  Such a design will be worked and potentially reworked multiple times before the right design in actually approved by the customer.  Once the design is approved, the work begins on creating the piece of furniture.  The creation may follow a standard set of procedures or may not.  The key is that there is no set way that must be followed in the creation.  The furniture maker will not wait until all material is available but rather can start on portions of the furniture (turning the legs, rough cutting the wood for the table top).  While there is likely an agreed upon delivery date, the success is dependent upon completing the furniture by that date, not following a set prescribed path to completion. It is possible to design and capture the small business origination process with its defined roles and responsibilities in a detailed process map.  The small business origination process can measure and monitor service level agreements and set expectations with the client around the entire process before the application is even taken.  Prescribed order and dependency around the activity and/or task-level process mapping can be accomplished in the small business origination process with a high degree of accuracy and consistency from one application to the next. The commercial loan origination process, however, cannot be captured with a high degree of accuracy and/or consistency.  Individual efforts can certainly be captured and specific service level agreements can be established.  For example, the spreading of financial statements can follow a prescribed methodology and service level agreements can be established.  However, attempts to establish service level agreements that when combined could adequately set expectations of total turnaround times, estimated completion times and prescribed methodologies would result in much lower compliance with such prescribed processes rendering it meaningless. Joel Pruis is a senior business consultant with Experian's Global Consulting Practice.  To learn more about strategy consulting and access more thought-leadership from our team, please visit www.experian.com/consultingservices.

As of Q2 2012, subprime borrowers are carrying the largest retail card balances, with an average card balance per account of $620 and $700 for VantageScore® credit score D and F tiers, respectively. The national average balance on a retail account is $329 — an increase of 39 percent over 2011. VantageScore® credit score A tier (super-prime) consumers carry the lowest average balance at $99 per account. Source: Experian-Oliver Wyman Market Intelligence Reports. VantageScore® is owned by VantageScore Solutions, LLC.

While technology undoubtedly has made accessing medical information much easier and faster, it also has also provided an increased potential for medical data breaches especially as health personnel begin to use unsecure mobile devices for personal and work use.  With an increase in health care employees using their own tablets and smartphones in the workplace, many healthcare companies are considering adopting a Bring Your Own Device (BYOD) policy.  However, many companies have failed to implement mobile data breach protection, breaking the HIPAA Security Rule which requires healthcare companies to perform a risk analysis of the processes by which they protect the confidentiality of electronic patient health information maintained by their organization.  Companies are required to use the information gathered from the analysis to take measures to ensure the confidentiality of patient data and to reduce risks to a reasonable level.  If companies don’t comply and there is a data security breach, they can be heavily fined by the U.S. Department of Health & Human Services. Just recently, a teaching hospital and medical practice associated with a large university was fined $1.5 million in a data breach of patient information when a laptop computer containing unencrypted data on 3,621 patients and research subjects was stolen.  Hospital and practice officials were found guilty of violating the HIPAA Security Rule by not implementing data protection and security on their mobile devices.  The loss of laptops, portable storage gadgets like thumb drives and cell phones have already cost insurance companies, drugstores, medical practices and even a government health and social services department, millions of dollars in fines. Unfortunately, this troubling trend doesn’t just affect the medical industry.  In August 2012, Coalfire (a firm that provides IT audit and risk assessment) surveyed 400 individuals across North America covering a variety of industries about their company’s mobile device security practices. The data revealed that many organizations lack policies addressing mobile cyber security threats. Download our Free Data Breach Response Guide Key statistics from the survey: 84 percent use the same smartphone for personal and work usage. 47 percent don’t have a password on their mobile phone. 51 percent said their companies cannot remotely wipe data from mobile devices if they are lost or stolen. 49 percent said their IT departments have not discussed mobile/cyber security with them. Clearly, companies are not doing enough to protect themselves and their employees from the expensive cost of a data breach.  As mobile devices become popular and less expensive, workers will naturally want to use them for their jobs.  Therefore, it is prudent for companies to adopt business data breach protection and security policies to protect not only their company data but also their pocketbook.

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?

Returns on investment from superior customer-centric strategies easily can exceed 20 percent in the first year of implementation. However, this number is compounded exponentially in subsequent years due to repeat business, new customer referrals and customer loyalty. Learn more about the design and deployment of holistic retail bank customer-centric strategies that synthesize critical information and qualitative banker insights. Source: Implementing differentiated customer-centric strategies: Retail-banker-friendly strategy development that resonates with your customers and shareholders, an Experian white paper.

During Q2 of 2012, home equity line of credit (HELOC) delinquency rates were the lowest in recent years. The delinquency rate fell below 1 percent for all performance categories: 30 to 59 days past due (DPD) fell to 0.88 percent; 60 to 89 DPD was at 0.42 percent and 90 to 180 DPD was at 0.99 percent. Source: Experian-Oliver Wyman Market Intelligence Reports.

Not surprisingly, bankcard utilization is the highest among subprime consumers with VantageScore D and F tiers having average bankcard utilization rates of 68% and 81% respectively. In comparison, VantageScore A tier (super prime) consumers had an average bankcard utilization rate of 6% and VantageScore B tier (prime) consumers had an average bankcard utilization rate of 15%. Join our panel of experts on October 23 to hear from industry experts on key regulations that are changing the way banks need to conduct business in order to grow and stay profitable. Source: Experian Oliver Wyman Market Intelligence Reports.

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