Tag: credit attributes

By: Teri Tassara In my blog last month, I covered the importance of using quality credit attributes to gain greater accuracy in risk models.  Credit attributes are also powerful in strengthening the decision process by providing granular views on consumers based on unique behavior characteristics.  Effective uses include segmentation, overlay to scores and policy definition – across the entire customer lifecycle, from prospecting to collections and recovery. Overlay to scores – Credit attributes can be used to effectively segment generic scores to arrive at refined “Yes” or “No” decisions.  In essence, this is customization without the added time and expense of custom model development.  By overlaying attributes to scores, you can further segment the scored population to achieve appreciable lift over and above the use of a score alone. Segmentation – Once you made your “Yes” or “No” decision based on a specific score or within a score range, credit attributes can be used to tailor your final decision based on the “who”, “what” and “why”.  For instance, you have two consumers with the same score. Credit attributes will tell you that Consumer A has a total credit limit of $25K and a BTL of 8%; Consumer B has a total credit limit of $15K, but a BTL of 25%.   This insight will allow you to determine the best offer for each consumer. Policy definition - Policy rules can be applied first to get the desirable universe.  For example, an auto lender may have a strict policy against giving credit to anyone with a repossession in the past, regardless of the consumer’s current risk score. High quality attributes can play a significant role in the overall decision making process, and its expansive usage across the customer lifecycle adds greater flexibility which translates to faster speed to market.  In today’s dynamic market, credit attributes that are continuously aligned with market trends and purposed across various analytical are essential to delivering better decisions.  

Previously, we looked at the various ways a dual score strategy could help you focus in on an appropriate lending population. Find your mail-to population with a prospecting score on top of a risk score; locate the riskiest of all consumers by layering a bankruptcy score with your risk model. But other than multiple scores, what other tools can be used to improve credit scoring effectiveness? Credit attributes add additional layers of insight from a risk perspective. Not everyone who scores an 850 represent the same level of risk once you start interrogating their broader profile. How much total debt are they carrying? What is the nature of it - is it mortgage or mostly revolving? A credit score may not fully articulate a consumer as high risk, but if their debt obligations are high, they may represent a very different type of risk than from another consumer with the same 850 score.  Think of attribute overlays in terms of tuning the final score valuation of an individual consumer by making the credit profile more transparent, allowing a lender to see more than just the risk odds associated with the initial score. Attributes can also help you refine offers. A consumer may be right for you in terms of risk, but are you right for them? If they have 4 credit cards with $20K limits each, they’re likely going to toss your $5K card offer in the trash. Attributes can tell us these things, and more. For example, while a risk score can tell us what the risk of a consumer is within a set window, certain credit attributes can tell us something about the stability of that consumer to remain within that risk band. Recent trends in score migration – the change in a level of creditworthiness of a consumer subsequent to generation of a current credit score – can undermine the most conservative of risk management policies. At the height of the recession, VantageScore® Solutions LLC studied the migration of scores across all risk bands and was able to identify certain financial management behaviors found within their credit files. These behaviors (signaling, credit footprint, and utility) assess the consumer’s likelihood of improving, significantly deteriorating, or maintaining a stable score over the next 12 months.  Knowing which subgroup of your low-risk population is deteriorating, or which high risk groups are improving, can help you make better decision today.

Last month, I wrote about seeking ways to ensure growth without increasing risk.  This month, I’ll present a few approaches that use multiple scores to give a more complete view into a consumer’s true profile. Let’s start with bankruptcy scores. You use a risk score to capture traditional risk, but bankruptcy behavior is significantly different from a consumer profile perspective. We’ve seen a tremendous amount of bankruptcy activity in the market. Despite the fact that filings were slightly lower than 2010 volume, bankruptcies remain a serious threat with over 1.3 million consumer filings in 2011; a number that is projected for 2012.  Factoring in a bankruptcy score over a traditional risk score, allows better visibility into consumers who may be “balance loading”, but not necessarily going delinquent, on their accounts. By looking at both aspects of risk, layering scores can identify consumers who may look good from a traditional credit score, but are poised to file bankruptcy. This way, a lender can keep their approval rates up and lower risk of overall dollar losses. Layering scores can be used in other areas of the customer life cycle as well. For example, as new lending starts to heat up in markets like Auto and Bankcard, adding a next generation response score to a risk score in your prospecting campaigns, can translate into a very clear definition of the population you want to target. By combining a prospecting score with a risk score to find credit worthy consumers who are most likely to open, you help mitigate the traditional inverse relationship between open rates and credit worthiness. Target the population that is worth your precious prospecting resources. Next time, we’ll look at other analytics that help complete our view of consumer risk. In the meantime, let me know what scoring topics are on your mind.

As our newly elected officials begin to evaluate opportunities to drive economic growth in 2011, it seems to me that the role of lenders in motivating consumer activity will continue to be high on the list of both priorities and actions that will effectively move the needle of economic expansion. From where I sit, there are a number of consumer segments that each hold the potential to make a significant impact in this economy. For instance, renters with spotless credit, but have not been able or confident enough to purchase a home, could move into the real estate market, spurring growth and housing activity. Another group, and one I am specifically interested in discussing, are the so called ‘fallen angels’ - borrowers who previously had pristine track records, but have recently performed poorly enough to fall from the top tiers of consumer risk segments. I think the interesting quality of ‘fallen angels’ is not that they don’t possess the motivation needed to push economic growth, but rather the supply and opportunity for them to act does not exist. Lenders, through the use of risk scores and scoring models, have not yet determined how to easily identify the ‘fallen angel’ amongst the pool of higher-risk borrowers whose score tiers they now inhabit. This is a problem that can be solved though – through the use of credit attributes and analytic solutions, lenders can uncover these up-side segments within pools of potential borrowers – and many lenders are employing these assets today in their efforts to drive growth. I believe that as tools to identify and lend to untapped segments such as the ‘fallen angels’ develop, these consumers will inevitably turn out to be key contributors to any form of economic recovery.  

By: Kari Michel Lenders want to find new customer through more informed credit risk decisions and use new types of data relationships to cross-sell.   The strategic goals of any company are to get more customers and revenue while reducing costs on the operating side and the credit loss side.  Some of the ways to meet these goals are to improve operating efficiency in creating and managing credit attributes, which represent the building blocks of how lenders make customer decisions. Lenders face many challenges in leveraging data from multiple credit and non-credit sources (e.g. credit bureaus) and maintaining data attributes across multiple systems. Furthermore, a lack of access to raw data makes it difficult to create effective, predictive attributes. Simply managing the discrepancies between specifications and code can become a very time consuming effort.  Maintaining a common set of attributes used in many types of scorecards and decision types often becomes difficult.  As a result, there is a heavy reliance on external people and technical resources to find the right tools to try and pull the data sources and attributes together. In an ideal situation, a lender should be able to easily access raw data elements across multiple sources and aggregate the data into meaningful attributes. Experian can offer these capabilities through its Attribute Toolbox product, allowing one or more systems to access a common set of standard analytics.  A set of highly predictive attributes, Premier Attributes, are available and offers a much more effective solution  for managing standard attributes across an enterprise.  With the use of these tools, lenders can decrease maintenance costs by quickly integrating data and analytics into existing business architecture to make profitable decisions.  

A recent article in the Boston Globe talked about the lack of incentive for banks to perform wide-scale real estate loan modifications due to the lack of profitability for lenders in the current government-led program structure. The article cited a recent study by the Boston Federal Reserve that noted up to 45 percent of borrowers who receive loan modifications end up in arrears again afterwards. On the other hand, around 30 percent of borrowers cured without any external support from lenders - leading them to believe that the cost and effort required modifying delinquent loans is not a profitable or not required proposition. Adding to this, one of the study’s authors was quoted as saying “a lot of people you give assistance to would default either way or won’t default either way.” The problem that lenders face is that although they have the knowledge that certain borrowers are prone to re-default, or cure without much assistance – there has been little information available to distinguish these consumers from each other.  Segmenting these customers is the key to creating a profitable process for loan modifications, since identification of the consumer in advance will allow lenders to treat each borrower in the most efficient and profitable manner. In considering possible solutions, the opportunity exists to leverage the power of credit data, and credit attributes to create models that can profile the behaviors that lenders need to isolate. Although the rapid changes in the economy have left many lenders without a precedent behavior in which to model, the recent trend of consumers that re-default is beginning to provide lenders with correlated credit attributes to include in their models. Credit attributes were used in a recent study on strategic defaulters by the Experian-Oliver Wyman Market Intelligence Reports, and these attributes created defined segments that can assist lenders with implementing profitable loan modification policies and decisioning strategies.  

By: Kari Michel   Lenders are looking for ways to improve their collections strategy as they continue to deal with unprecedented consumer debt, significant increases in delinquency, charge-off rates and unemployment and, declining collectability on accounts. Improve collections To maximize recovered dollars while minimizing collections costs and resources, new collections strategies are a must. The standard assembly line “bucket” approach to collection treatment no longer works because lenders can not afford the inefficiencies and costs of working each account equally without any intelligence around likelihood of recovery. Using a segmentation approach helps control spend and reduces labor costs to maximize the dollars collected. Credit based data can be utilized in decision trees to create segments that can be used with or without collection models. For example, below is a portion of a full decision tree that shows the separation in the liquidation rates by applying an attribute to a recovery score This entire segment has an average of 21.91 percent liquidation rate. The attribute applied to this score segment is the aggregated available credit on open bank card trades updated within 12 months. By using just this one attribute for this score band, we can see that the liquidation rates range from 11 to 35 percent. Additional attributes can be applied to grow the tree to isolate additional pockets of customers that are more recoverable, and identify segments that are not likely to be recovered. From a fully-developed segmentation analysis, appropriate collections strategies can be determined to prioritize those accounts that are most likely to pay, creating new efficiencies within existing collection strategies to help improve collections.

By: Wendy Greenawalt In the last installment of my three part series dispelling credit attribute myths, we’ll discuss the myth that the lift achieved by utilizing new attributes is minimal, so it is not worth the effort of evaluating and/or implementing new credit attributes. First, evaluating accuracy and efficiency of credit attributes is hard to measure. Experian data experts are some of the best in the business and, in this edition, we will discuss some of the methods Experian uses to evaluate attribute performance. When considering any new attributes, the first method we use to validate statistical performance is to complete a statistical head-to-head comparison. This method incorporates the use of KS (Kolmogorov–Smirnov statistic), Gini coefficient, worst-scoring capture rate or odds ratio when comparing two samples. Once completed, we implement an established standard process to measure value from different outcomes in an automated and consistent format. While this process may be time and labor intensive, the reward can be found in the financial savings that can be obtained by identifying the right segments, including: • Risk models that better identify “bad” accounts and minimizing losses • Marketing models that improve targeting while maximizing campaign dollars spent • Collections models that enhance identification of recoverable accounts leading to more recovered dollars with lower fixed costs Credit attributes Recently, Experian conducted a similar exercise and found that an improvement of 2-to-22 percent in risk prediction can be achieved through the implementation of new attributes. When these metrics are applied to a portfolio where several hundred bad accounts are now captured, the resulting savings can add up quickly (500 accounts with average loss rate of $3,000 = $1.5M potential savings). These savings over time more than justify the cost of evaluating and implementing new credit attributes.  

By: Wendy Greenawalt In the second installment of my three part series, dispelling credit attribute myths, we will discuss why attributes with similar descriptions are not always the same. The U.S. credit reporting bureaus are the most comprehensive in the world. Creating meaningful attributes requires extensive knowledge of the three credit bureaus’ data. Ensuring credit attributes are up-to-date and created by informed data experts.  Leveraging complete bureau data is also essential to obtaining long-term strategic success. To illustrate why attributes with similar names may not be the same let’s discuss a basic attribute, such as “number of accounts paid satisfactory.” While the definition, may at first seem straight forward, once the analysis begins there are many variables that must be considered before finalizing the definition, including: Should the credit attributes include trades currently satisfactory or ever satisfactory? Do we include paid charge-offs, paid collections, etc.? Are there any date parameters for credit attributes? Are there any trades that should be excluded? Should accounts that have a final status of "paid” be included? These types of questions and many others must be carefully identified and assessed to ensure the desired behavior is captured when creating credit attributes. Without careful attention to detail, a simple attribute definition could include behavior that was not intended.  This could negatively impact the risk level associated with an organization’s portfolio. Our recommendation is to complete a detailed analysis up-front and always validate the results to ensure the desired outcome is achieved. Incorporating this best practice will guarantee that credit attributes created are capturing the behavior intended.  

By: Wendy Greenawalt This blog kicks off a three part series exploring some common myths regarding credit attributes. Since Experian has relationships with thousands of organizations spanning multiple industries, we often get asked the same types of questions from clients of all sizes and industries. One of the questions we hear frequently from our clients is that they already have credit attributes in place, so there is little to no benefit in implementing a new attribute set. Our response is that while existing credit attributes may continue to be predictive, changes to the type of data available from the credit bureaus can provide benefits when evaluating consumer behavior. To illustrate this point, let’s discuss a common problem that most lenders are facing today-- collections. Delinquency and charge-off continue to increase and many organizations are having difficulty trying to determine the appropriate action to take on an account because consumer behavior has drastically changed regarding credit attributes. New codes and fields are now reported to the credit bureaus and can be effectively used to improve collection-related activities. Specifically, attributes can now be created to help identify consumers who are rebounding from previous account delinquencies. In addition, lenders can evaluate the number and outstanding balances of collection or other types of trades.  This can be achieved while considering the percentage of accounts that are delinquent and the specific type of accounts affected after assessing credit risk. The utilization of this type of data helps an organization to make collection decisions based on very granular account data.  This is done while considering new consumer trends such as strategic defaulters. Understanding all of the consumer variables will enable an organization to decide if the account should be allowed to self-cure.  If so, immediate action should be taken or modification of account terms should be contemplated. Incorporating new data sources and updating attributes on a regular basis allows lenders to react to market trends quickly by proactively managing strategies.  

When reviewing offers for prospective clients, lenders often deal with a significant amount of missing information in assessing the outcomes of lending decisions, such as: Why did a consumer accept an offer with a competitor? What were the differentiating factors between other offers and my offer, i.e. what were their credit score trends? What happened to consumers that we declined? Do they perform as expected or better than anticipated? What were their credit risk models? While lenders can easily understand the implications of the loans they have offered and booked with consumers, they often have little information about two important groups of consumers: 1. Lost leads: consumers to whom they made an offer but did not book 2. Proxy performance: consumers to whom financing was not offered, but where the consumer found financing elsewhere. Performing a lost lead analysis on the applications approved and declined, can provide considerable insight into the outcomes and credit performance of consumers that were not added to the lender’s portfolio. Lost lead analysis can also help answer key questions for each of these groups: How many of these consumers accepted credit elsewhere? What were their credit attributes? What are the credit characteristics of the consumers we're not booking? Were these loans booked by one of my peers or another type of lender? What were the terms and conditions of these offers? What was the performance of the loans booked elsewhere? Who did they choose for loan origination? Within each of these groups, further analysis can be conducted to provide lenders with actionable feedback on the implications of their lending policies, possibly identifying opportunities for changes to better fulfill lending objectives. Some key questions can be answered with this information: Are competitors offering longer repayment terms? Are peers offering lower interest rates to the same consumers? Are peers accepting lower scoring consumers to increase market share? The results of a lost lead analysis can either confirm that the competitive marketplace is behaving in a manner that matches a lender’s perspective.  It can also shine a light into aspects of the market where policy changes may lead to superior results. In both circumstances, the information provided is invaluable in making the best decision in today’s highly-sensitive lending environment.

By: Wendy Greenawalt When consulting with lenders, we are frequently asked what credit attributes are most predictive and valuable when developing models and scorecards. Because we receive this request often, we recently decided to perform the arduous analysis required to determine if there are material differences in the attribute make up of a credit risk model based on the portfolio on which it is applied. The process we used to identify the most predictive attributes was a combination of art and sciences -- for which our data experts drew upon their extensive data bureau experience and knowledge obtained through engagements with clients from all types of industries. In addition, they applied an empirical process which provided statistical analysis and validation of the credit attributes included. Next, we built credit risk models for a variety of portfolios including bankcard, mortgage and auto and compared the credit attribute included in each. What we found is that there are some attributes that are inherently predictive regardless for which portfolio the model was being developed. However, when we took the analysis one step further, we identified that there can be significant differences in the account-level data when comparing different portfolio models. This discovery pointed to differences, not just in the behavior captured with the attributes, but in the mix of account designations included in the model. For example, in an auto risk model, we might see a mix of attributes from all trades, auto, installment and personal finance…as compared to a bankcard risk model which may be mainly comprised of bankcard, mortgage, student loan and all trades.  Additionally, the attribute granularity included in the models may be quite different, from specific derogatory and public record data to high level account balance or utilization characteristics. What we concluded is that it is a valuable exercise to carefully analyze available data and consider all the possible credit attribute options in the model-building process – since substantial incremental lift in model performance can be gained from accounts and behavior that may not have been previously considered when assessing credit risk.  

By: Wendy Greenawalt On any given day, US credit bureaus contain consumer trade data on approximately four billion trades. Interpreting data and defining how to categorize the accounts and build attributes, models and decisioning tools can and does change over time, due to the fact that the data reported to the bureaus by lenders and/or servicers also changes. Over the last few years, new data elements have enabled organizations to create attributes to identify very specific consumer behavior. The challenge for organizations is identifying what reporting changes have occurred and the value that the new consumer data can bring to decisioning. For example, a new reporting standard was introduced nearly a decade ago which enabled lenders to report if a trade was secured by money or real property. Before the change, lenders would report the accounts as secured trades making it nearly impossible to determine if the account was a home equity line of credit or a secured credit card. Since then, lender reporting practices have changed and, now, reports clearly state that home equity lines of credit are secured by property making it much easier to delineate the two types of accounts from one another. By taking advantage of the most current credit bureau account data, lenders can create attributes to capture new account types.  They can also capture information (such as: past due amounts; utilization; closed accounts and derogatory information including foreclosure; charge-off and/or collection data) to make informed decisions across the customer life cycle.