Credit Attributes

--by Kelly Kent

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 

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. 

 

-- 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

Today, most lenders evaluate tri-bureau credit data when making lending decisions. Credit attributes are the building blocks for creating models, scorecards, segmentation and policy rules. Why is creating tri-bureau attributes so difficult? The main challenges are assessing the bureau data that is available, deriving meaningful information from that data and then equalizing or minimizing the differences inherent to the data available from the credit bureaus.

While this process may seem straight forward, defining an industry designation or a series of attributes within that industry can take months of analysis and careful consideration of trade-offs. Missing even one data element can have a major impact to lending decisions and the portfolio mix of an organization.

For example, let’s look at a very basic attribute like total number of trades. When creating this attribute, an organization has to decide what constitutes a trade. For instance, is a collection account a trade that should be included in the count? Again, this may seem trivial, but could have a significant impact to the risk associated with a consumer when combined with other credit data.

Whether credit attributes are created and managed internally or purchased from an attribute provider, the process of defining and leveling credit bureau data across bureaus requires significant time and resources. Therefore, ensuring the attributes used are statistically accurate and predictive is vital to the long-term success of an organization.
 

-- By Wendy Greenawalt

The US has the most extensive credit bureau data in the world. The available credit data is vast and very complex making it difficult to synthesize the data across bureaus. Transforming tri-bureau data into informed decisions is challenging for most financial institutions. Due to this, many organizations rely on a highly skilled team of credit data experts to create and manage their credit attributes.

Creating or modifying tri-bureau credit attributes requires extensive credit data knowledge. It’s similar to making a cake. Everyone knows it takes certain ingredients to bake a cake but if the measurements are not precise then the cake will not taste good and may even be flat in the middle. Similarly, not knowing all the nuances to bureau data can produce inaccurate results. For an organization to accurately develop tri-bureau attributes, it requires years of analyzing available bureau data, creating attribute definitions and testing the attributes to validate them for accuracy.

This data expertise already exists within the credit bureaus and can easily be leveraged to ensure that the underlying data is accurately evaluated across all bureaus. Data intelligence can assist organizations in interpretation, translation, and manipulation of bureau data, helping them utilize the information to make smarter and more informed decisions. Examples of data intelligence can include tri-bureau attribute leveling, creation of custom attributes, system migrations and auditing of scorecards and/or attributes to validate analytical accuracy. In my next blog I will discuss the specific challenges lenders face when creating tri-bureau and custom attributes.