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Effectively Managing Risk in the New Economy

Technology Solutions for Enterprise-wide
Account Management

 

By Lee Grice and Andrew Skillen

As macroeconomic conditions have deteriorated over the last two years, the need for improved risk-management tools has never been greater. The U.S. consumer is under considerable payment stress.
One of the more concerning changes in the credit industry is a significant increase in consumers that are walking away from their primary mortgage obligations. These so-called “rational defaults” involve consumers whose mortgage balances exceed the value of their homes. The defaults occur with little or no advance warning, and often come as a result of consumers’ intentions to maintain good credit standing on the rest of their debt obligations.
Instead of the old hierarchy of mortgage/auto/card, the new order is auto/card/mortgage. This has troubling implications for credit lenders’ management of portfolio risks. Traditionally, credit grantors have used early-stage delinquency for lower-priority credit obligations as a harbinger for increased risk in a consumer portfolio. But as consumer behavior changes relative to payment of credit obligations, existing policies and processes built on the old payment hierarchy must be re-examined, and give rise to the need for better account management solutions.

Best Practices in Account Management
Some of the ongoing priorities around account management include:
Identify customers that are an increasing risk to your business
Improve retention rates by identifying customers who are attrition risks
Better management of spending limits and credit lines
Consistent policy treatment across multiple channels
Better prioritization of monitoring and collection strategies
Technology helps lenders more effectively manage risk within their portfolio, in four key areas, which should be orchestrated together for maximum benefit:
1.  Implement rapid change             management.
2.  Build a comprehensive view of the         customer.
3.  Leverage behavioral and credit data         together.
4.  Make use of advanced modeling             techniques.

Implement Rapid Change Management
Making policy changes to more quickly adapt to changing consumer payment priorities is of strategic importance, but is not without challenges:
Implementing a change to policies often requires IT engagement.
Testing of new business rules to support policy changes is difficult.
Lack of modern infrastructure and resources lead to slow strategy
implementation.

A best practices way to gain more control over change management is by using a business rules management system (BRMS). Traditionally, when new strategy decisions are made, and credit policy changes need to be updated in the automated system, the IT department must go in and make code changes. It can take weeks or months for credit policy changes to get updated in the system(s). This is no longer feasible in today’s fast-paced business environment.
A BRMS breaks down the walls between the risk manager and IT by allowing the risk manager to go into the system and easily edit the business rules that reflect an organization’s credit policies, allowing business users to update and test their changes to score cuts or policy rules in a matter of hours.
A BRMS rides on top of your existing processes, so it complements the investment in your existing infrastructure. Implementing a business rules management system that integrates with your existing account management processes can give you the flexibility to implement policy changes much more quickly.

Build a Comprehensive
View of the Customer

A full understanding of each existing customer is essential to managing risk and typically includes aspects such as product and account level profitability, total exposure, usage patterns, payment patterns and marketing information. Most lenders encounter several challenges in getting the full picture:
All relationships with customers are often not understood, as data exists in siloed parts of the organization.
Customer performance data often resides in multiple disparate systems.
Existing account management activities are often not comprehensive and may only effectively measure a subset of the overall risk.
Data warehousing projects designed to create a more comprehensive view of the consumer are expensive, time consuming, and often fall short of
expectations.


A comprehensive view of the customer is critical in building an effective account management strategy. A complete understanding of all relationships with an individual consumer or household is needed to identify and rank overall risk. This risk assessment helps prioritize the treatments that are applied against the segmented accounts.
Let’s look at a simple theoretical example of four hypothetical customers. In this example, a retail lending institution provides multiple retail lending products (secured and unsecured) as well as credit card products. Within their credit card portfolio, these four consumers each have a current credit card balance of $5,000 and an available credit limit of $8,000. But their most recent payment data shows very different characteristics for these seemingly similar accounts. Customer 1 paid $100, customers 2 and 4 paid the balance in full, and customer 3 missed a payment. Actionable treatment plans might include a phone call to customer 3, while customers 2 and 4 are high utilization that may be cross-sell candidates, and 1 is a revolver.
But focusing on utilization, the following picture emerges: Customer 1 made no new purchases, customers 2 and 3 purchased $5,000 each, and customer 4 purchased $2,500. The purchase information helps segment customers that carry a balance from those that do not, and segments high-utilization customers from low-utilization customers.
Let’s add the other products that the customers have with the bank – an existing home equity account and total deposit accounts, including checking, savings and CDs (see chart, top).
The additional account information shows that customer 3 has traditionally been a very good customer. Is the missed payment an isolated instance or a harbinger for future risk? Let’s convert the HELOC data to a utilization percent, which is a derived attribute from available information (see chart, bottom).
The detail on the consumer’s other retail products offers both potential risks and opportunities that may exist within the portfolio. It is critical to be able to see how a customer is performing across all your products, to allow for an accurate assessment of total contingent liability at risk.
As the credit climate improves, lenders can leverage a more comprehensive view of the customer to better weigh individual risk, and better assess the overall ROI associated with an account. This comprehensive risk view allows for using a scalpel, not a hatchet, to reduce credit lines.


Leverage Behavioral and Credit Data Together
To gain even more insight, we can analyze how customers are meeting their credit obligations with other lenders.
Appending this additional information, which is contained within the credit file, provides even more strategic analysis for effectively managing portfolio risk.
Integrating additional credit information into a single customer view presents challenges:
Account management solutions can limit the amount of data to be managed within the system.
Integration to third-party credit information can be problematic due to the nature of the information and the IT support required to update such
information.
Many customers are only leveraging internal data assets so the implications of adding and working with credit data is not understood within the organization.
Retrieving and loading other data assets, such as credit information, in a timely fashion may represent challenges.

There are significant benefits of incorporating credit assets into an account management solution. For our example we’ll use the VantageScore® and two months of historical score information. Also, we’ve appended the number of 30/60/90 days past due on all credit obligations (see chart at top).
Changing the HELOC information to a utilization percentage provides yet deeper insight. Additional attribution, such as showing the relative change from current and previous months’ HELOC percentage and VantageScore, would also provide additional insight.
While customer 3 did miss this month’s payment, this consumer does not appear to be under financial stress and the missed payment appears to be an isolated instance. Also, while customers 2 and 4 have very similar characteristics regarding credit card behavior and deposit balances, there is considerable difference regarding the risk associated with each account.
Lenders can access and incorporate other, nontraditional data sources that also provide deeper insight into the borrower’s capacity to pay, including employment information and income information.
By incorporating new data sources such as employer-reported consumer income and employment information, lenders will have deeper insight, fully understanding a consumer’s debt load relative to income. Lenders can improve their credit line strategy, treatment prioritization, and debt recovery strategies due to insight not only into a consumer’s willingness to pay, but also their capacity to pay.

Make Use Of Advanced Modeling Techniques
However, simply appending data does not allow for predicting the future behavior of each individual account. And for large portfolios, using a subjective determination may be effective but it’s largely dependent on personal experience and may be too time consuming. So, a generally accepted best practice is to leverage modeling techniques to derive the most power from all the available data. Modeling allows for using all the current information to best predict future actions.
Leveraging modeling techniques is not without challenges, which can include:
Model development is a complex process involving specific intellectual property.
Access to data, both performance data to be modeled and historical information required to develop a model, can be problematic.
Existing account management solutions may hinder the implementation of custom models.

Users can leverage models from two categories: generic risk models and customer-specific models. Our examples used the VantageScore, a generic industry score that was developed to predict the likelihood of an account being 90-plus days past due within a 24-month performance time period. The use of generic scores has many positive benefits – they are readily available, are extremely effective at rank-ordering risk, and can be validated against customer populations to determine how well they can predict other performance measures.
Customer-specific behavior scores and models, when available, provide an even more powerful analytical tool. They use specific account information, including the performance to be modeled, in order to develop the model. These customer-specific models are typically more effective because they are built on the same general population that a financial institution can expect to serve. Your solutions provider can help you build and test new predictive models, and provide you with technology for easily deploying these new models within your automated risk-decisioning process. The ability to test and deploy new models more quickly is another key element to improving the timeliness of your change management.

Effective Account
Management Solutions

The following are components, features and capabilities that form part of a best practices account management solution (see chart, next page).
The role of technology is to streamline and accelerate the process of applying lessons learned into your production environment. With these tools, you can more accurately segment your portfolio, and automate the application of new credit strategies to reflect changing market conditions and organization strategies.
Finally, since the implications of the above items are information technology centric, sound principles should be used in selecting a flexible solution. These principles include evaluation of scalability, modularity, support costs, operations, infrastructure impact, and the ability to enhance and complement existing solutions.


Summary
Any size institution can benefit from accessing the latest data sources and analytics to gain deeper insight into the borrower’s capacity to pay. While technology plays a critical role in risk management, technology alone will not win the battle for reduced risk and increased profitability. Systems and software must be coupled with time-tested, best practices to effectively protect your portfolio. In addition to helping lenders select and implement appropriate technologies, a superior solutions provider will help you to incorporate risk management best practices into your overall workflow processes. An effective solutions provider offers expertise in tools and insight into best practices to allow users to apply their own expertise in effectively managing risk within their own portfolio.
EDITOR’S NOTE: This article is an abridged version of a white paper of the same title, issued in April 2009. To access the full version, please visit www.equifax.com/consumer/risk.             

Lee Grice is a vice president of product management with Equifax’s Technology and Analytical Services group, which includes risk management and business process management (BPM) software solutions. Andrew Skillen is the director of technology marketing at Equifax. Equifax offers the data, analytics, technology and professional services that lenders need to upgrade their account-management processes to the next-generation solutions outlined in this article. For more information on Equifax solutions, please call (888) 987-1687 – ext. 5, or visit www.equifax.com/consumer/risk.


Posted on Monday, April 05, 2010 (Archive on Sunday, July 04, 2010)
Posted by Scott  Contributed by Scott
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