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By: Reggie Whitley After spending years working in bank fraud, one of the most difficult conversations to have with a consumer is “We can no longer successfully protect your accounts.” Identity theft is shockingly easy to commit. In most cases consumers are able to recover successfully from compromises thanks to the diligence of their financial institutions, the cooperation of retailers, and credit reporting services that assist in recovery from compromises. Problems arise when you have consumers who become attractive targets for various reasons – these could be relationships to others, high net worth, extensive products, or business ownership. These targets aren’t ‘one and done’ consumers for an identity criminal. For these consumers identity thieves will continue accessing their identities for months or even years. These consumers are often forced to migrate from banks or credit card companies because the identity crimes follow them and they become too expensive to protect. For these consumers, identity theft is a true nightmare. In the past year, fraud protection strategies and tools have emerged that will begin to reduce the risk of continued compromise these consumers face. Real time identity alerting tools have emerged to offer consumers a way to receive notification when their identities are being used, not just at a single institution, but across the financial landscape. Consumers now have the ability to receive SMS, Email, or Web notifications whenever their identity has been verified. If the consumer receives an alert on an banking account they just opened, they simply move on, no action is required. In the event that the alert is NOT something they generated, the consumer calls in, discusses with a fraud specialist and is connected to the generating bank or retailer to file a fraud report. Obviously, this service benefits any consumer who would like to monitor usage of their identity and detect fraud, but knowing first hand the horror stories extensively compromised consumers get caught in, tools like start to open a level of REAL TIME protection that hasn’t before existed. The benefit is truly across the board. Banks and retailers begin to realize savings when consumers engage them within minutes of fraud. This reduces the success of identity thieves, discouraging additional attempts. Finally, detecting this fraud reduces the extensive efforts needed to help a consumer clear up credit reports and file fraud reports. Perhaps in the near future instead of turning high risk consumers away, we can provide them with the ability to protect themselves and the industry from the nightmare situations that are still too frequent today.
The desire to return to portfolio growth is a clear trend in mature credit markets, such as the US and Canada. Historically, credit unions and banks have driven portfolio growth with aggressive out-bound marketing offers designed to attract new customers and members through loan acquisitions. These offers were typically aligned to a particular product with no strategy alignment between multiple divisions within the organization. Further, when existing customers submitted a new request for credit, they were treated the same as incoming new customers with no reference to the overall value of the existing relationship. Today, however, financial institutions are looking to create more value from existing customer relationships to drive sustained portfolio growth by increasing customer retention, loyalty and wallet share. Let’s consider this idea further. By identifying the needs of existing customers and matching them to individual credit risk and affordability, effective cross-sell strategies that link the needs of the individual to risk and affordability can ensure that portfolio growth can be achieved while simultaneously increasing customer satisfaction and promoting loyalty. The need to optimize customer touch-points and provide the best possible customer experience is paramount to future performance, as measured by market share and long-term customer profitability. By also responding rapidly to changing customer credit needs, you can further build trust, increase wallet share and profitably grow your loan portfolios. In the simplest sense, the more of your products a customer uses, the less likely the customer is to leave you for the competition. With these objectives in mind, financial organizations are turning towards the practice of setting holistic, customer-level credit lending parameters. These parameters often referred to as umbrella, or customer lending, limits. The challenges Although the benefits for enhancing existing relationships are clear, there are a number of challenges that bear to mind some important questions to consider: · How do you balance the competing objectives of portfolio loan growth while managing future losses? · How do you know how much your customer can afford? · How do you ensure that customers have access to the products they need when they need them · What is the appropriate communication method to position the offer? Few credit unions or banks have lending strategies that differentiate between new and existing customers. In the most cases, new credit requests are processed identically for both customer groups. The problem with this approach is that it fails to capture and use the power of existing customer data, which will inevitably lead to suboptimal decisions. Similarly, financial institutions frequently provide inconsistent lending messages to their clients. The following scenarios can potentially arise when institutions fail to look across all relationships to support their core lending and collections processes: 1. Customer is refused for additional credit on the facility of their choice, whilst simultaneously offered an increase in their credit line on another. 2. Customer is extended credit on a new facility whilst being seriously delinquent on another. 3. Customer receives marketing solicitation for three different products from the same institution, in the same week, through three different channels. Essentials for customer lending limits and successful cross-selling By evaluating existing customers on a periodic (monthly) basis, financial institutions can assess holistically the customer’s existing exposure, risk and affordability. By setting customer level lending limits in accordance with these parameters, core lending processes can be rendered more efficient, with superior results and enhanced customer satisfaction. This approach can be extended to consider a fast-track application process for existing relationships with high value, low risk customers. Traditionally, business processes have not identified loan applications from such individuals to provide preferential treatment. The core fundamentals of the approach necessary for the setting of holistic customer lending (umbrella) limits include: · The accurate evaluation of credit and default risk · The calculation of additional lending capacity and affordability · Appropriate product offerings for cross-sell · Operational deployment Follow my blog series over the next few months as we explore the essentials for customer lending limits and successful cross-selling.
By: Lloyd Parker Another Experian Vision Conference comes to a close today but not without a full morning of breakout sessions with compelling speakers and experts sharing real-world strategies for real opportunity and real growth. The conference concluded with an entertaining and thought-provoking speaker, Sir Ken Robinson, Ph.D., author of The Element: How Finding Your Passion Changes Everything and Out of our Minds: Learning to Be Creative, who shared with us ideas on how to cultivate innovation and change within organizations in order to grow with their environments and continue to thrive. We’d like to thank you for making this year’s event one of the best. And thank you for the confidence you give us all year round. We know the great responsibility that goes along with that and we are committed to helping your business succeed. Top Tweets of the Week #Vision2013 the slowest growing loan segment (actually it is negative) is HELOC @cumagazine@dougbenzine at -8% YOY.#engage — Mike Horrocks (@mikehorrocks) May 8, 2013 #vision2013 great credit union discussion at experian conference! — Doug Benzine (@DougBenzine) May 8, 2013 #Vision2013 @sirkenrobinson (1) we are living in a time of revolution (2) we have to think differently about talents (3) then act different — Mike Horrocks (@mikehorrocks) May 8, 2013 'Most adults don't know what their true aptitudes are' Sir Ken Robinson #vision2013 — Michele Raneri (@MLRaneri) May 8, 2013 #Vision2013 @sirkenrobinson Our kids are not trains, they are rockets ready to explore and we need to help them only light the fuse. #engage — Mike Horrocks (@mikehorrocks) May 8, 2013
By: Lloyd Parker James W. Paulsen, Ph.D., Chief Investment Strategist at Wells Capital Management kicked off day two at the Experian Vision 2013 Conference with an upbeat economic outlook for 2013 and what it means longer term, for the next generation. Paulsen is nationally recognized for his views on the economy and publishes his own commentary assessing economic and market trends through his newsletter, Economic and Market Perspective. Today he demonstrated to conference attendees how the United States is in a “gear” year and that the “new normal” has been going on for the past 25 years. His optimism predicts that for the next 10 years we’ll see an estimated 3% GDP growth. As mentioned by some on Twitter, “he makes statistics fun.” The morning was followed by more insightful breakout sessions and the launch of a new session format called, “Viewpoints” – fast paced, quick-hitting sessions that highlight new innovations, forward-thinking solutions and product demonstrations designed to satisfy the attendee’s desire to learn more. Networking activities filled the afternoon, and at the time of post the winners of the golf tournament had not yet been announced. Other highlights from the day Viewpoint: The art of portfolio analysis Maintaining a strong commercial portfolio starts with knowledge. In this session, new concepts are introduced and old concepts were questioned as we shared validated intelligence on which commercial triggers are best suited for effective portfolio management. Viewpoint: A 900% return on small-business marketing Here proven approaches were reviewed for targeting existing small-business customers and prospects for deposits and loans using available firmographic data, business credit scores and response models. Viewpoint: Transaction data signals – challenges and opportunities Experian’s R&D Data Lab shared team insights into how underutilized transaction data might be leveraged as well as how to overcome some of the technical and business challenges that arise. Viewpoint: Find time and money in your credit authorization process Attendees learned how to improve decision making and productivity by bringing together multiple sources of credit authorization information in Baker Hill Advisor®. Viewpoint: Commercial fraud – An ounce of prevention is worth a pound of cure Protecting personal identities is commonplace for most businesses. Commercial fraud may not be a primary concern, but one “rare” occurrence could mean a big loss to profits and reputation. Attendees learned how BizID can prevent fraud in business portfolios and help ensure that appropriate preventive measures are taken. Viewpoint: SaaS for intelligent customer decisioning – separating the hype from the reality A stroll down memory lane highlighted the hype and reality of technology over the last several decades and looked at the realities we face that make this space so difficult to predict. Attendees looked at criteria to help them decipher what’s working, what they can do about it and the critical points to focus on when looking at SaaS solutions. Top tweets: "USA is in a GEAR Year" expects 3% growth this year. #vision2013 #finserv — Patricia Hines (@PJHines) May 7, 2013 #Vision2013 @aitegroup 32% of mobile users think mobile is secure & 55% think it is somewhat secure. Banks need to #engage mobile banking. — Mike Horrocks (@mikehorrocks) May 7, 2013 @experianvision "Growth may surpass expectations this year. Confidence is being upwardly adjusted." Dr. James W. Paulsen. #vision2013 — Martha Staten (@Sauconyandsuds) May 7, 2013
By: Lloyd Parker There aren’t many things that energize me more than seeing our clients arrive for the Experian Vision 2013 Conference. Industry leaders from all over the world have joined us in Southern California to kick-off a full day of insightful topics. This year’s event sold out in record time and we have many first time attendees taking advantage of the opportunities to network and learn from industry peers. Today began with a welcome from Steve Wagner, President of Consumer Information Services followed by Victor Nichols, Chief Executive Officer, Experian North America and myself, Lloyd Parker, Group President Credit Services. We launched our key theme of Real Strategies, Real Growth, Real Opportunities, discussing the concept of “reality checks.” Reality check #1: Micro-targeting is required Identify market differences Understand your customer segments Adapt to specific needs of your empowered consumers Reality check #2: Managing risk Protect against risks that follow success Keep your door open for good business Focus on operational efficiencies Reality check #3: Optimizing engagement Utilize all the data of each customer Understand all of your customer touch points Manage customer strategies holistically A key theme of the day was the economic, regulatory and political changes impacting our economy and your customers. We had a conversation with Timothy F. Geithner, 75th U.S. Secretary of the Treasury, who shared his experiences as the principal architect of the President’s strategy to avert economic collapse and to reform the financial system. He also discussed international economic challenges and gave us his personal outlook on the economy. The afternoon featured many great speakers and industry experts across many topics that included hearing from many of our regulators on the topic of banking regulations; experts in the area of mobile payments and banking; along with many of our clients who shared their successful programs and experiences working across consumer and commercial portfolios and the customer lifecycle. Other highlights from the day Things overheard at the Roundtable Sessions: “You don’t need extensive touches for small loans, but let go of Excel,” Community bank topics “Loans are milk, deposits are steak,” Issues and opportunities within commercial risk management roles topic “Pent up demand will lead to overall positive auto market conditions near term,” Automotive hot topics “Keeping various systems in synch; Spend time early on implementation to define biz requirements,” Overcoming system operation challenges topic “Marketing to the underserved remains a challenge,” Issues and opportunities in consumer risk management roles topic “Using mobile to go paperless in commercial lending to improve convenience,” Mobile tools for business lending topic Top tweets: #vision2013 "Be relentlessly skeptical. Be humble about what you don't know." Former Secretary Timothy Geithner. — Martha Staten (@Sauconyandsuds) May 6, 2013 Experian CEO to Us bankers on current reg environment. "we have to get in compliance. We have to grow in compliance".#vision2013 — eric haller (@erichaller2) May 6, 2013 Great description of the current environment - "Economic Pinball" Victor Nichols #vision2013 #finserv — Patricia Hines (@PJHines) May 6, 2013 #vision2013.@experiancredit data lab is the "most unique initiative in the industry". The lab lets you #engage w/ untraditional data. — Mike Horrocks (@mikehorrocks) May 6, 2013 Only take risks you can understand, measure, and monitor. CRO round table. #vision2013 — alissa (@adh314) May 6, 2013 The phone is the new wallet. Apps are the new cards. #engage #vision2013 — Andrew Beddoes (@beddoesa712) May 6, 2013
a.wpbutton:hover {text-decoration: none !important;} Please select from the below list of recent Experian white papers to gain more insight into topics relevant to your business needs and goals. Converting Information to Intelligence - Current Trends in Mitigating Small-Business Risk Through Analytics Download Now As former Chrysler CEO Lee Iacocca put it, “Even a correct decision is wrong when taken too late.” Portfolio managers who oversee small-business risks know this well. They realize it when they make a decision about approving or rejecting a loan request and recognize later the correct decision would have been clearer if they could have weighed additional data and used improved analytics. This white paper presents some of these latest trends affecting the small-business lending landscape. Specifically, it illuminates how companies are using the new robust data sources and analytic tools – from consortium data to rapid model customization – to maximize their interactions with small-business clients with greater accuracy. Creating Value In Challenging Times: An Innovative Approach To Basel III Compliance Download Now In this paper, we will provide an introduction to Basel III regulation and discuss some of its impact on banks and the banking system. We also will present a real business case showing how organizations turn these regulatory challenges into business opportunities by optimizing their credit strategies. Turning the Tide - Managing Troubled Portfolios Download Now The economy may be recovering and the credit picture improving, but lending institutions still find themselves coping with some troubled portfolios. Plus, they always need to be prepared to identify high-risk accounts. What they can discover is that turning around a challenged loan portfolio requires taking just a few basic steps. This white paper explores how in Arizona Federal Credit Union reversed its misfortunes to emerge from the economic crisis prosperous and with $30 million in profits, illuminating what lenders can do to manage troubled portfolios and reverse poor performance. Get To Know Your Customers: Account Linking and Advanced Customer Management For Utility Providers Download Now In this paper, we will explore the practice of customer management and key capabilities to improve effectiveness in a complex business environment. It will specifically look at opportunities within the utilities marketplace for account linking and deploying customer-level decisions to the business to help drive portfolio performance retain and grow profitability and strengthen customer relationships. State of the U.S. Credit Markets - At Last, Signs of Real Recovery Download Now The economy’s recovery from the Great Recession may have started slowly, but it is accelerating – and it’s genuine. Economic indicators tell the story of improving business prospects. As the recovery begins to take shape, many consumers are now turning the corner with it and will present as viable candidates to grow your portfolio profitably. It’s difficult to find any solace in a recession, yet it can serve as an opportunity. 2012 will be the year for lenders to return to pre-recession strategies if they are to grow significantly. This economic rebound is real, and savvy lenders – just like those marathon runners and Tour de France bicyclists – recognize that it’s in the uphill stage of the race that the lead changes. Home Equity Indicators with New Credit Data Methods for Improved Mortgage Risk Analytics This whitepaper describes new improvements in local housing market indicators and analytics derived from local-area credit and local real estate information. In the run up to the U.S. housing downturn and financial crisis, perhaps the greatest single risk management shortfall was poorly predicted home prices and borrower home equity. Understanding Automotive Loan Charge-off Patterns Can Help Mitigate Lender Risk Loan delinquency rates are one of the most important statistics to track in the automotive finance industry. If consumers are not repaying loans on time, it puts billions of dollars at risk. When high dollar volumes are at risk, it is a negative for everyone in the lending world, including consumers, automotive retailers and lenders themselves. While conditions have improved considerably the past few years, lenders still need to remain vigilant about where delinquencies are most likely to occur. It’s an unavoidable fact that some loans will have to be charged off. Understanding where and how these charge offs occur provides important learning for the industry. Experian Automotive has found several clear patterns that can help lenders better understand the root cause of loan delinquencies. Strategic Customer Management for Business Banking Portfolios Download Now This white paper explores business banking customer management and the benefits that can be realized from introducing a strategic approach. It will look at the features of a leading-edge approach to business banking customer management and provide practical insights on key areas. Universe Expansion - Growth Strategies in the Evolving Consumer Market Download Now As the economy gains strength, lenders are engaging in an increasingly fierce competition to entice the best candidates to their portfolios and to grow their lending business. In waging this battle, however, many lenders are concentrating on the super-prime and prime consumer segments. Prospecting strategies currently in use often do not identify the right subpopulations within the near-prime segment. Specifically, there are prospects within the near-prime segment that exhibit low bad rates compared with the broader near-prime consumer base. It is imperative that lenders redefine their targeting/underwriting strategies to prospect and acquire in the near prime space. A variety of prospecting strategies are now available that compliment and expand on a lender’s current growth initiatives – now is the time to ensure that optimal strategies are in place and that opportunities within near-prime are not overlooked. Interested in more thought leadership? Visit our Business Resources page on Experian.com
By: Staci Baker Just before the holidays, the Fed released proposed rules, which implement Sections 165 and 166 of the Dodd-Frank Act. According to The American Bankers Association, “The proposals cover such issues as risk-based capital requirements, leverage, resolution planning, concentration limits and the Fed’s plans to regulate large, interconnected financial institutions and nonbanks.” How will these rules affect you? One of the biggest concerns that I have been hearing from institutions is the affect that the proposed rules will have on profitability. Greater liquidity requirements, created by both the Dodd-Frank Act and Basel III Rules, put pressure on banks to re-evaluate which lending segments they will continue to participate in, as well as impact the funds available for lending to consumers. What are you doing to proactively combat this? Within the Dodd-Frank Act is the Durbin Amendment, which regulates the interchange fee an issuer can charge a consumer. As I noted in my prior blog detailing the fee cap associated with the Durbin Amendment, it’s clear that these new regulations in combination with previous rulings will continue to put downward pressures on bank profitability. With all of this to consider, how will banks modify their business models to maintain a healthy bottom line, while keeping customers happy? Over my next few blog posts, I will take a look at the Dodd-Frank Act’s affect on an institution’s profitability and highlight best practices to manage the impact to your organization.
By: Joel Pruis Small Business Application Requirements The debate on what constitutes a small business application is probably second only to the ongoing debate around centralized vs. decentralized loan authority (but we will get to that topic in a couple of blogs later). We have a couple of topics that need to be considered in this discussion, namely: 1. When is an application an application? 2. Do you process an incomplete application? When is an application an application? Any request by a small business with annual sales of $1,000,000 or less falls under Reg B. As we all know because of this regulation we have to maintain proper records of when we received an application and when a decision on the application was made as well as communicated to the client. To keep yourself out of trouble, I recommend that there be a small business application form (paper or electronic) and that you have clearly stated the information required for a completed application in your small business application procedures. The form removes ambiguities in the application process and helps with the compliance documentation. One thing is for certain – when you request a personal credit bureau on the small business owner(s)/guarantor(s) and you currently do not have any credit exposure to the individual(s) – you have received an application and to this there is no debate. Bottom line is that you need to define your application and do so using objective criteria. Subjective criteria leaves room for interpretation and individual interpretation leaves doubt in the compliance area. Information requirements Whether or not you use a generic or custom small business scorecard or no scorecard at all, there are some baseline data segments that are important to collect on the small business applicant: · Requested amount and purpose for the funds · Collateral (if necessary based upon the product terms and conditions) · General demographics on the business o Name and location o Business Entity type (corporation, llc, partnership, etc.) o Product and/or service provided o Length of time in business o Current banking relationship · General demographics on the owners/guarantors o Names and addresses o Current banking relationship o Length of time with the business · External data reports on the business and/or guarantors o Business Report o Personal Credit Bureau on the owners/guarantors · Financial Statements (?) – we’ll talk about that in part II of this post. The demographics and the existing banking relationship are likely not causing any issues with anyone and the requested amount and use of funds is elementary to the process. Probably the greatest debate is around the collection of financial information and we are going to save that debate for the next post. The non-financial information noted above provides sufficient data to pull personal credit bureaus on the owners/guarantors and the business bureau on the actual borrower. We have even noted some additional data informing us the length of time the business has been in existence and where the banking relationship is currently held for both the business and the owners. But what additional information should be requested or should I say required? We have to remember that the application is not only to support the ability to render a decision but also supports the ability to document the loan and maybe even serve as a portion of the loan documentation. We need to consider the following: · How standardized are the products we offer? · Do we allow for customization of collateral to be offered? · Do we have standard loan/fee pricing? · Is automatic debit for the loan payments required? Optional? Not available? · Are personal guarantees required? Optional? We again go back to the 80/20 rule. Product standardization is beneficial and optimal when we have high volumes and low dollars. The smaller the dollar size of the request/relationship the more standardized we need to have our products and as a result our application can be more streamlined. When we do not negotiate rate, we do not need to have a space to note requested rate. When we do not negotiate on personal guarantees we always require the personal financial information be collected on all owners of the business (some exceptions for very small ownership interests). Auto-debit for the loan payments means we always need to have some form of a DDA account with our institution. I think you get the point that for the highest volume of applications we standardize and thus streamline the process through the removal of ambiguity. Do you process an incomplete application? The most common argument for processing an incomplete application is that if we know we are going to decline the application based upon information on the personal credit bureau, why go through the effort of collecting and spreading the financial information. Two significant factors make this argument moot: customer satisfaction and fair lending regulation. Customer satisfaction This is based upon the ease of doing business with the financial institution. More specifically the number of contact points or information requests that are required during the process. Ideally the number of contact points that are required once the applicant has decided to make a financing request should be minimal the information requirements clearly communicated up front and fully collected prior to rendering a decision. The idea that a quick no is preferable to submitting a full application actually is working to make the declination process more efficient than the actual approval process. So in other words we are making the process more efficient and palatable for those clients we do NOT consider acceptable versus those clients that ARE acceptable. Secondly, if we accept and process incomplete applications, we are actually mis-prioritizing the application volume. Incomplete applications should never be processed ahead of completed packages yet under the quick no objective, the incomplete application is processed ahead of completed applications simply based upon date and time of submission. Consequently we are actually incenting and fostering the submission of incomplete applications by our lenders. Bluntly this is a backward approach that only serves to make the life of the relationship manager more efficient and not the client. Fair lending regulation This perspective poses a potential issue when it comes to consistency. In my 10 years working with hundreds of financial institutions, only a very small minority of times have I encountered a financial institution that is willing to state with absolute certainty that a particular characteristic will cause an application to e declined 100% of the time. As a result, I wish to present this scenario: · Applicant A provides an incomplete application (missing financial statements, for example). o Application is processed in an incomplete status with personal and business bureaus pulled. o Personal credit bureau has blemishes which causes the financial institution to decline the application o Process is complete · Applicant B provides a completed application package with financial statements o Application is processed with personal and business bureaus pulled, financial statements spread and analysis performed o Personal credit bureau has the same blemishes as Applicant A o Financial performance prompts the underwriter or lender to pursue an explanation of why the blemishes occurred and the response is acceptable to the lender/underwriter. Assuming Applicant A had similar financial performance, we have a case of inconsistency due to a portion of the information that we “state” is required for an application to be complete yet was not received prior to rendering the decision. Bottom line the approach causes doubt with respect to inconsistent treatment and we need to avoid any potential doubt in the minds of our regulators. Let’s go back to the question of financial statements. Check back Thursday for my follow-up post, or part II, where we’ll cover the topic in greater detail.
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By: Joel Pruis Part I – New Application Volume and the Business Banker: Generating small business or business banking applications may be one of the hottest topics in this segment at this time. Loan demand is down and the pool of qualified candidates seems to be down as well. Trust me, I am not going to jump on the easy bandwagon and state that the financial institutions have stopped pursuing small business loan applications. As I work across the country, I have yet to see a financial institution that is not actively pursuing small business loan applications. Loan growth is high on everyone’s priority and it will be for some time. But where have all the applicants gone? Based upon our data, the trend in application volume from 2006 to 2010 is as follows: Chart displays 2010 values: So at face value, we see that actually, overall applications are down (1,032 in 2006 to 982 in 2010) while the largest financial institutions in the study were actually up from 18,616 to 25,427. Furthermore the smallest financial institutions with assets less than $500 million showed a significant increase from 167 to 276. An increase of 65% from the 2006 levels! But before we get too excited, we need to look a little further. When we are talking about increasing application volume we are focusing on applications for new exposure or a new extension of credit and not renewals. The application count in the above chart includes renewals. So let’s take a look at the comparison of New Request Ratio between 2006 and 2010. Chart displays 2010 values: So using this data in combination with the total application count we get the following measurements of new application volume in actual numbers. So once we get under the numbers, we see that the gross application numbers truly don’t tell the whole story. In fact we could classify the change in new application volume as follows: So why did the credit unions and community banks do so well while the rest held steady or dropped significantly? The answer is based upon a few factors: In this blog we are going to focus on the first – Field Resources. The last two factors – Application Requirements and Underwriting Criteria – will be covered in the next two blogs. While they have a significant impact on the application volume and likely are the cause of the application volume shift from 2006 to 2010, each represents a significant discussion that cannot be covered as a mere sub topic. More to come on those two items. Field Resources pursuing Small Business Applications The Business Banker Focus. Focus. Focus. The success of the small business segment depends upon the focus of the field pursuing the applications. As we move up in the asset size of the financial institution we see more dedicated field resources to the Small Business/Business Banking segment. Whether these roles are called business bankers, small business development officers or business banking specialists, the common denominator is that they are dedicated to the small-business/ business banking space. Their goals depend on their performance in this segment and they cannot pursue other avenues to achieve their targets or goals. When we start to review the financial institutions in the less than $20B segment, the use of a dedicated business banker begins to diminish. Marketing segments and/or business development segmentation is blurred at best and the field resource is better characterized as a Commercial Lender or Commercial Relationship Manager. The Commercial Lender is tasked with addressing the business lending needs across a particular region. Goals are based upon total dollars generated and there is no restriction outside of the legal or in house lending limit of the specific financial institution. In this scenario, the notion of any focus on small business is left to the individual commercial lender. You will find some commercial lenders that truly enjoy and devote their efforts to the small business/business banking space. These individuals enjoy working with the smaller business for a variety of reasons such as the consultative approach (small businesses are hungry for advice while the larger businesses tend to get their advice elsewhere) or the ability to use one’s lending authority. Unfortunately while your financial institution may have such commercial lenders (one’s that are truly working solely in the small business or business banking segment) to change that individual’s title or formally commit them to working only in the small business/business banking segment is often perceived as a demotion. It is this perception that continues to hinder the progress of financial institutions with assets between $500 million and $20 billion from truly excelling in the small business/business banking space. Reality is that the best field resource to generate the small business/business banking application volume available to your financial institution is through the dedicated individual known as the Business Banker. Such an individual is capable of generate up to 250 applications (for the truly high performing) per year. Even if we scale this back to 150 applications in a given year for new credit volume at an average request of $106,929 (the lowest dollar of the individual peer groups), the business banker would be generating total application dollars of $16,039,350. If we imply a 50% approval/closure rate, the business banker would be able to generate a total of $8,019,675 in new credit exposure annually. Such exposure would have the potential of generating a net interest margin of $240,590 assuming a 3% NIM. Not too bad.
By: Joel Pruis Basic segmentation strategy for business banking asks the following questions: - Is there a uniform definition of small business across the industry? - How should small business be defined? Sales size of the applicant? Exposure to the financial institution? - Is small business/business banking a retail or commercial line of business? No One Size Fits All The notion of a single definition for small business for any financial institution is inappropriate as the intent for segmentation is to focus marketing efforts, establish appropriate products to support the segment, develop appropriate delivery methods and use appropriate risk management practices. For the purpose of this discussion we will restrict our content to developing the definition of the segment and high level credit product terms and conditions to support the segment. The confusion on how to define the segment is typically due to the multiple sources of such definitions. The Small Business Administration, developers of generic credit risk scorecards (such as Experian), marketing firms and the like all have multiple ways to define small business. While they all have a different method of defining small business, the important factor to consider is that each definition serves the purpose of the creator. As such, the definition of small business should serve the purpose of the specific financial institution. A general rule of thumb is the tried and true 80/20 rule. Assess your financial institution’s business purpose portfolio by rank ordering individual relationships by total dollar exposure. Using the 80/20 rule, determine the smallest 80% of the number of relationships by exposure. Typically the result is that the largest 20% of relationships will cover approximately 80% of the total dollars outstanding in your business purpose portfolio. Conversely the smallest 80% of relationships will cover only about 20% of the total dollars outstanding. Just from this basic analysis we can see the primary need for segmentation between the business banking and the commercial (middle market, commercial real estate, etc.) portfolios. Assuming we do not segment we have a significant imbalance of effort vs. actual risk. Meaning if we treat all credit relationships the same we are spending up to 80% of our time/resources on only 20% of our dollar risk. Looking at this from the other direction we are only spending 20% of our credit resources assessing 80% of our total dollar risk. Obviously this is a very basic analysis but any way that you look at it, the risk assessment (underwriting and portfolio management) must be “right-sized” in order to provide the appropriate risk management while working to maximize the return on such portfolio segments. The realities of the credit risk assessment practices without segmentation is that the small business segment will be managed by exception, at best. Given the large number of relationships and the small impact that the small business segment has on traditional credit quality metrics such as past dues and charge offs, the performance of the small business portfolio can, in fact, be hidden. Such metrics focus on percentage of dollars that are past due or charged off against the entire portfolio. Since the largest dollars are in the 20% of relationships, it will take a significant number of individual small business relationships being delinquent or charged off before the overall metric would become alarming. Working with our clients in defining small business, one of the first exercises that we recommend is assessing the actual delinquency and charge off rates in the newly defined small business/business banking portfolio. Simply put, determine the total dollars that fit the new definition and apply the charge-offs by borrowers that meet the definition that have occurred over the past 12 months divided by total outstanding in the new portfolio segment. Similarly determine the current dollars past due of relationships meeting the definition of small business divided by the total outstanding of said segment. Such results typically are quite revealing and will at least provide a baseline for which the financial institution can measure improvement and/or success. Without such initial analysis, we have witnessed financial institutions laying blame on the new underwriting and portfolio management processes for such performance when it existed all along but was never measured. So basically our first attempt to define the segment has created a total credit exposure limit. Such limits should be used to determine the appropriate underwriting and portfolio management methods (both of which we will discuss further subsequent blogs), but this type of a definition does little to support a business development effort as the typical small business does not always borrow nor can we accurately assess the potential dollar exposure of any given business until we actually gather additional data. Thus for business development purposes we establish the definition of small business primarily by sales size. Looking at the data from your existing relationships, your financial institution can get an accurate indication of the maximum sales size that should be considered in the business development efforts. As a result we have our business development definition by sales size of a given company and our underwriting and portfolio management defined by total exposure. You may be thinking that such definitions are not always in sync with each other and you would be correct. You will have some companies with total sales under your definition that borrower more than your total exposure limits while companies with total exposure that falls under small business but the total sales of such companies may exceed the business development limit. It is impossible to catch every possibility and to do so is an exercise in futility. Better that you start with the basics of the segmentation and then measure the new applications that exceed the total exposure or the relationships meeting the total exposure cap but exceed the sales limitation. During the initial phase, judgment on a case by case basis will need to be used. Questions such as: Is the borrower that exceeds our sales limitations likely to need to borrow more in the near future? Is the exposure of the borrower that meets our sales size requirement likely to quickly reduce its exposure to meet our definition? Will our underwriting techniques be adequate to assess the risk of this relationship? Will our portfolio monitoring methods be sufficient to assess the changes in the risk profile after it has been booked? Will the relationship management structure be sufficient to support such a borrower? As you encounter these situations it will become obvious to the financial institution the frequency and consistency of such exceptions to the existing definition and prompt adjustments and/or exclusions. But to try and create the exclusions before collecting the data or examining the actual application volumes is where the futility lies. Best to avoid the futility and act only on actual data. Further refinement of the segment definition will also be based on the above assessment. Additional criteria will be added such as: Industry segments (Commercial Real Estate, for example) Product types (construction lending) Just know that the definition will not stay static. Based upon the average credit request changes from 2006 to 2010, changes can and will be significant. The following graph represents the average request amounts from 2010 data compared to the dollar amounts from 2006 (noted below the chart). So remember that where you start is not where you have to stay. Keep measuring, keep adjusting and your segmentation strategy will serve you very well. Look for my next post on generating small business applications. Specifically I’ll cover who should be involved in the outbound marketing efforts of your small business segment. I look forward to your continued comments, challenges and debate as we continue our discussion around small business/business banking. And if you're interested, I'm hosting a 3-part Webinar series, Navigating Through The Challenges Affecting Portfolio Performance, that will evaluate how statistics and modeling, combined with strategies from traditional credit management, can create a stronger methodology and protect your bottom line.
By: John Straka For many purposes, national home-price averages, MSA figures, or even zip code data cannot adequately gauge local housing markets. The higher the level of the aggregate, the less it reflects the true variety and constant change in prices and conditions across local neighborhood home markets. Financial institutions, investors, and regulators that seek out and learn how to use local housing market data will generally be much closer to true housing markets. When houses are not good substitutes from the viewpoint of most market participants, they are not part of the same housing market. Different sizes and types and ages of homes, for example, may be in the same county, zip code, block, or even right next door to each other, but they are generally not in the same housing market when they are not good substitutes. This highlights the importance of starting with detailed granular information on local-neighborhood home markets and homes. To be sure, greater granularity in neighborhood home-market evaluation requires analysts and modelers to deal with much more data on literally hundreds of thousands of neighborhoods in the U.S. It is fair to ask if zip-code level data, for example, might not be generally sufficient. Most housing analysts and portfolio modelers, in fact, have traditionally assumed this, believing that reasonable insights can be gleaned from zip code, county-level, or even MSA data. But this is fully adequate, strictly speaking, only if neighborhood home markets and outcomes are homogenous—at least reasonably so—within the level of aggregation used. Unfortunately, even at zip-code level, the data suggests otherwise. Examples All of the home-price and home-valuation data for this report was supplied by Collateral Analytics. I have focused on zip7s, i.e. zip+2s, which are a more granular neighborhood measure than zip codes. A Hodrick-Prescott (H-P) Filter was applied by Collateral Analytics to the raw home-price data in order to attenuate short-term variation and isolate the six-year trends. But as we’ll see this dampening still leaves an unrealistically high range of variation within zip codes, for reasons discussed below. Fortunately there is an easy way to control for this, which we’ll apply for final estimates of the range of within-zip variation in home-price outcomes. The three charts below show the H-P filtered 2005-2011 percent changes in home-price per square foot of living area within three different types of zip codes in San Diego county. Within the first type of zip code, 92319 in this case, the home-price changes in recent years have been relatively homogenous, with a range of -56% to -40% home-price change across the zip7s (i.e., zip+2s) in 92319. But the second type of zip code, illustrated by 92078, is more typical. In this type of case the home-price changes across the zip7s have varied much more. The 2055-2011 zip7 %chg in home prices within 92078 have varied by over 40 percentage points, from -51% to -10%. In the third type of zip code, less frequent but surprisingly common, the home-price changes across the zip7s have had a truly remarkable range of variation. This is illustrated here by zip code 92024 in which the home price outcomes have varied from -51% to +21%, or a 71 percentage point range of difference—and this is not the zip code with the maximum range of variation observed! All of the San Diego County zip codes are summarized in the bar chart below. Nearly two-thirds of the zip codes, 65%, have more than 30 percentage points within-zip difference in the 2005-2011 zip7 %changes in home prices. 40% have more than a 40 percentage point range of different home-price outcomes, 23% have more than a 50 percentage point range, and 13% have more than a 70 percentage point range of differences. The average range of the zip7 within-zip code differences is a 37 percentage point median, 41 percentage-point mean. These high numbers are surprising, and are most likely unrealistically high. Summary of Within-Zip (Zip+2 level) Ranges of Variation in Home-Price Changes in San Diego: Percentage of Zips by Range Across Zip+2s in Home Price/Living Area %Change 2005-2011 Controlling for Factors Inflating the Range of Variation Such sizable differences within a typical single zip code clearly suggest materially different neighborhood home markets. While this qualitative conclusion is supported further below, the magnitudes of the within-zip variation in home-price changes shown above are quite likely inflated. There is a tendency for a limited number of observations in various zip7s to create statistical “noise” outliers, and the inclusion of distressed property sales here can create further outliers, with cases of both limited observations and distress sales particularly capable of creating more negative outliers that are not representative of the true price changes for most homes and their true range of variation within zip codes. (My earlier blog on June 29th discussed the biases from including distressed property sales while trying to gauge general price trends for most properties.) Fortunately, I’ve been able to access a very convenient way to control for these factors by using the zip7 averages of Collateral Analytics’ AVM (Automated Valuation Model) values rather than simply the home price data summarized above. These industry-leading AVM home valuations have been designed, in part, to filter out statistical noise problems. The bar chart below shows the still significant zip7 ranges within San Diego County zip codes using the AVM values, but the distribution is now shifted considerably, and more realistically, to a much smaller share of the zip codes with remarkably high zip7 variation. Compared with the chart above, now just 1% of the zips have a zip7 range greater than 60 percentage points, 5% greater than 50, and 11% greater than 40, but there are still 36% greater than 30. To be sure, this distribution, and the average range of zip7 differences—which is now a 25 percentage-point median, 26 percent age-point mean—do show a considerable range of local home market variation within zip codes. It seems fair to conclude that the typical zip code does not contain the uniformity in home price outcomes that most housing analysts and modelers have tended to simply assume. The difference between the effects on consumer wealth and behavior of a 10% home price decline, for example, vs. a 35 to 50% decline, would seem to be sizable in most cases. This kind of difference within a zip code is not at all unusual in these data. How About a Different Type of Urban Area—More Uniform? It might be thought that the diversity of topography, etc., across San Diego County (from the sea to the mountains) makes its variation of home market outcomes within zip codes unusually high. To take a quick gauge of this hypothesis, let’s look at a more topographically uniform urban area: Columbus, Ohio. When I informally polled some of my colleagues asking what their prior belief would be about the within-zip code variation in home price outcomes in Columbus vs. San Diego County, there was unanimous agreement with my prior belief. We all expected greater within-zip uniformity in Columbus. I find it interesting to report here that we were wrong. Both the H-P filtered raw home-price information and the AVM values from Collateral Analytics show relatively greater zip7 variation within Columbus (Franklin County) zip codes than in San Diego County. The bar chart below shows the best-filtered, most attenuated results, the AVM values. 5% of the Columbus zips have a zip7 range greater than 70 percentage points, 8% greater than 60, 23% greater than 50, 35% greater than 40, and 65% greater than 30. The average range of zip7 within-zip code differences in Columbus is a 35 percentage point median, 38 percentage-point mean. Conclusion These data seem consistent with what experienced appraisers and real estate agents have been trying to tell economists and other housing analysts, investors, and financial institutions and policymakers for quite a long time. Although they have quite reasonable uses for aggregate time-series and forecasting purposes, more aggregate-data based models of housing markets actually miss a lot of the very real and material variation in local neighborhood housing markets. For home valuation and many other purposes, even models that use data which gets down to the zip code level of aggregation—which most analysts have assumed to be sufficiently disaggregated—are not really good enough. These models are not as good as they can or should be. These facts are indicative of the greater challenge to properly define local housing markets empirically, in such a way that better data, models, and analytics can be more rapidly developed and deployed for greater profitability, and for sooner and more sustainable housing market recoveries. I thank Michael Sklarz for providing the data for this report and for comments, and I thank Stacy Schulman for assistance in this post.
By: Mike Horrocks Let’s all admit it, who would not want to be Warren Buffet for a day? While soaking in the tub, the “Sage of Omaha” came up with the idea to purchase shares of Bank of America and managed to close the deal in under 24 hours (and also make $357 million in one day thanks to an uptick in the stock). Clearly investor opinions differ when picking investments, so what did Buffet see that was worth taking that large of a risk? In interviews Buffet simply states that he saw the fundamentals of a good bank (once they fix a few things), that will return his investment many times over. He has also said that he came to this conclusion based on years of seeing opportunities where others only see risk. So what does that have to do with risk management? First, ask yourself as you look at your portfolio of customers what ones are you “short-selling” and risk losing and what customers are you investing into and expect Buffet-like returns on in the future? Second, ask yourself how are you making that “investment” decision on your customers? And lastly, ask yourself how confident you are in that decision? If you’re not employing some mode of segmentation today on your portfolio stop and make that happen as soon as you are done reading this blog. You know what a good customer looks like or looked like once upon a time. Admit to yourself that not every customer looks as good as they used to before 2008 and while you are not “settling”, be open minded on who you would want as a customer in the future. Amazingly, Buffet did not have Bank of America’s CEO Brian Moynihan’s phone number when he wanted to make the deal. This is where you are heads and shoulders above Garot’s Steak House’s favorite customer. You have deposit information, loan activity and performance history, credit data, and even the phone number of your customers. This gives you plenty of data and solutions to build that profile of what a good customer looks like – thereby knowing who to invest in. The next part is the hardest. How confident are you in your decision that you will put your money on it? For example, my wife invested in Bank of America the day before Warren put in his $5 billion. She saw some of the same signs that he did in the bank. However, the fact that I am writing this blog is an indicator that she clearly did not invest to the scale that Warren did. But what is stopping you from going all in and investing in your customers’ future? If the fundamentals of your customer segmenting are sound, any investment today into your customers will come back to you in loyalty and profits in the future. So at the risk of conjuring up a mental image, take the last lesson from Warren Buffet’s tub soaking investment process and get up and invest in those perhaps risky today, yet sound tomorrow customers or run the risk of future profits going down the drain.
By: Kari Michel The way medical debts are treated in scores may change with the introduction of June 2011, Medical Debt Responsibility Act. The Medical Debt Responsibility Act would require the three national credit bureaus to expunge medical collection records of $2,500 or less from files within 45 days of their being paid or settled. The bill is co-sponsored by Representative Heath Shuler (D-N.C.), Don Manzullo (R-Ill.) and Ralph M. Hall (R-Texas). As a general rule, expunging predictive information is not in the best interest of consumers or credit granters -- both of which benefit when credit reports and scores are as accurate and predictive as possible. If any type of debt information proven to be predictive is expunged, consumers risk exposure to improper credit products as they may appear to be more financially equipped to handle new debt than they truly are. Medical debts are never taken into consideration by VantageScore® Solutions LLC if the debt reporting is known to be from a medical facility. When a medical debt is outsourced to a third-party collection agency, it is treated the same as other debts that are in collection. Collection accounts of lower than $250, or ones that have been settled, have less impact on a consumer’s VantageScore® credit score. With or without the medical debt in collection information, the VantageScore® credit score model remains highly predictive.
By: Mike Horrocks The realities of the new economy and the credit crisis are driving businesses and financial institutions to better integrate new data and analytical techniques into operational decision systems. Adjusting credit risk processes in the wake of new regulations, while also increasing profits and customer loyalty will require a new brand of decision management systems to accelerate more precise customer decisions. There is a Webinar scheduled for Thursday that will insightfully show you how blending business rules, data and analytics inside a continuous-loop decisioning process can empower your organization - to control marketing, acquisition and account management activities to minimize risk exposure, while ensuring portfolio growth. Topics include: What the process is and the key building blocks for operating one over time Why the process can improve customer decisions How analytical techniques can be embedded in the change control process (including data-driven strategy design or optimization) If interested check out more - there is still time to register for the Webinar. And if you just want to see a great video - check out this intro.