Analytics & Credit Decisions

-- 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 Tracy Bremmer

Preheat the oven to 350 degrees. Grease the bottom of your pan. Mix all of your ingredients until combined. Pour mixture into pan and bake for 35 minutes. Cool before serving.

Model development, whether it is a custom or generic model, is much like baking. You need to conduct your preparatory stages (project design), collect all of your ingredients (data), mix appropriately (analysis), bake (development), prepare for consumption (implementation and documentation) and enjoy (monitor)!  

This blog will cover the first three steps in creating your model! 

Project design involves meetings with the business users and model developers to thoroughly investigate what kind of scoring system is needed for enhanced decision strategies. Is it a credit risk score, bankruptcy score, response score, etc.? Will the model be used for front-end acquisition, account management, collections or fraud?

Data collection and preparation evaluates what data sources are available and how best to incorporate these data elements within the model build process. Dependent variables (what you are trying to predict) and the type of independent variables (predictive attributes) to incorporate must be defined. Attribute standardization (leveling) and attribute auditing occur at this point. The final step before a model can be built is to define your sample selection.

Segmentation analysis provides the analytical basis to determine the optimal population splits for a suite of models to maximize the predictive power of the overall scoring system. Segmentation helps determine the degree to which multiple scores built on an individual population can provide lift over building just one single score.

Join us for our next blog where we will cover the next three stages of model development:  scorecard development; implementation/documentation; and scorecard monitoring. 
 

-- By Kari Michel

In my last blog I gave an overview of monitoring reports for new account acquisition decisions listing three main categories that reports typically fall into:  (1) population stability; (2) decision management; (3) scorecard performance.

Today, I want to focus on population stability.   Applicant pools may change over time as a result of new marketing strategies, changes in product mix, pricing updates, competition, economic changes or a combination of these. Population stability reports identify acquisition trends and the degree to which the applicant pool has shifted over time, including the scorecard components driving the shift in custom credit scoring models. 

Population stability reports include:

• Actual versus expected score distribution
• Actual versus expected scorecard characteristics distributions (available with custom models)
• Mean applicant scores
• Volumes, approval and booking rates

These types of reports provide information to help monitor trends over time, rather than spikes from month to month.  Understanding the trends allows one to be proactive in determining if the shifts warrant changes to lending policies or cut-off scores.

Population stability is only one area that needs to be monitored; in my next blog I will discuss decision management reports.

 

-- 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.
 

-- by Kelly Kent

The title of this edition, ‘The risk within the risk’ is a testament to the amount of information that can be gleaned from an assessment of the performances of vintage pools.

Vintage pools offer numerous perspectives of risk. They allow for a deep appreciation of the effects of loan maturation, and can also point toward the impact of external factors, such as changes in real estate prices, origination standards, and other macroeconomic factors, by highlighting measurable differences in vintage to vintage performance.

What is a vintage pool?

By the Experian definition, vintage pools are created by taking a sample of all consumers who originated loans in a specific period, perhaps a certain quarter, and tracking the performance of the same consumers and loans through the life of each loan.

Vintage pools can be analyzed for various characteristics, but three of the most relevant are:

* Vintage delinquency, which allows for an understanding of the repayment trends within each pool;

* Payoff trends, which reflect the pace at which pools are being repaid; and

* Charge-off curves, which provide insights into the charge-off rates of each pool.

The credit grade of each borrower within a vintage pool is extremely important in understanding the vintage characteristics over time, and credit scores are based on the status of the borrower just before the new loan was originated. This process ensures that the new loan origination and the performance of the specific loan do not influence the borrower’s credit score. By using this method of pooling and scoring, each vintage segment contains the same group of loans over time – allowing for a valid comparison of vintage pools and the characteristics found within.

Once vintage pools have been defined and created, the possibilities for this data are numerous...

-- 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 Kelly Kent

In recent months, the topics of stress-testing and loss forecasting have been at the forefront of the international media and, more importantly, at the forefront of the minds of American banking executives. The increased involvement of the federal government in managing the balance sheets of the country’s largest banks has mixed implications for financial institutions in this country.

On one hand, some banks have been in the practice of building macroeconomic scenarios for years and have tried and tested methods for risk management and loss forecasting. On the other hand, in financial institutions where these practices were conducted in a less methodical manner, if at all, the scrutiny placed on capital adequacy forecasting has left many looking to quickly implement standards that will address regulatory concerns when their number is called.

For those clients to whom this process is new, or for those who do not possess a methodology that would withstand the examination of federal inspectors, the question seems to be – where do we begin?

I think that before you can understand where you’re going, you must first understand where you are and where you have been. In this case, it means having a detailed understanding of key industry and peer benchmarks and your relative position to those benchmarks. 

Even simple benchmarking exercises provide answers to some very important questions.

• What is my risk profile versus that of the industry?
• How does the composition of my portfolio differ from that of my peers?
• How do my delinquencies compare to those of my peers? How has this position been changing?

By having a thorough understanding of one’s position in these challenging circumstances, it allows for a more educated foundation upon which to build assessments of the future.
 

-- 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.

 

-- By Kari Michel

What is your credit risk score?  Is it 300, 700, 900 or something in between?  In order to understand what it means, you need to know which score you are referencing.  Lenders use many different scoring models to determine who qualifies for a loan and at what interest rate. For example, Experian has developed many scores, such as VantageScore®..  Think of VantageScore® as just one of many credit scores available in the marketplace.

While all credit risk models have the same purpose, to use credit information to assess risk, each credit model is unique in that each one has its own proprietary formula that combines and calculates various credit information from your credit report.  Even if lenders used the same credit risk score, the interpretation of risk depends on the lender, and their lending policies and criteria may vary.

Additionally, each credit risk model has its own score range as well.  While the score range may be relatively similar to another score range, the meaning of the score may not necessarily be the same.   For example, a 640 in one score may not mean the same thing or have the same credit risk as a 640 for another score.  It is also possible for two different scores to represent the same level of risk. If you have a good credit score with one lender, you will likely have a good score with other lenders, even if the number is different.