Tag: Decisioning

The value of a good decision can generate $150 or more in customer net present value, while the cost of a bad decision can cost you $1,000 or more.  For example, acquiring a new and profitable customer by making good prospecting and approval and pricing decisions and decisioning strategies may generate $150 or much more in customer net present value and help you increase net interest margin and other key metrics.  While the cost of a bad decision (such as approving a fraudulent applicant or inappropriately extending credit that ultimately results in a charge-off) can cost you $1,000 or more. Why is risk management decisioning important? This issue is critical because average-sized financial institutions or telecom carriers make as many as eight million customer decisions each year (more than 20,000 per day!).  To add to that, very large financial institutions make as many as 50 billion customer decisions annually.  By optimizing decisions, even a small 10-to-15 percent improvement in the quality of these customer life cycle decisions can generate substantial business benefit. Experian recommends that clients examine the types of decisioning strategies they leverage across the customer life cycle, from prospecting and acquisition, to customer management and collections.  By examining each type of decision, you can identify those opportunities for improvement that will deliver the greatest return on investment by leveraging credit risk attributes, credit risk modeling, predictive analytics and decision-management software.        

Well, here we are nearly at the beginning of November and the Red Flags Rule has been with us for nearly two years and the FTC’s November 1, 2009 enforcement date is upon us as well (I know I’ve said that before).  There is little value in me chatting about the core requirements of the Red Flags Rule at this point.  Instead, I’d like to shed some light on what we are seeing and hearing these days from our clients and industry experts related to this initiative: Red Flags Rule responses clients 1. Most clients have a solid written and operational Identity Theft Prevention Program in place that arguably meets their interpretation of the Red Flags Rule requirements. 2. Most clients have a solid written and operational Identity Theft Prevention Program in place that creates a boat-load of referrals due to the address mismatches generated in their process(es) and the requirement to do something with them. 3. Most clients are now focusing on ways in which to reduce the number of referrals generated and procedures to clear the remaining referrals via a cost-effective and automated manner…of course, while preventing fraud and staying compliant to Red Flags Rule. In 2008, a key focus at Experian was to help educate the market around the Red Flags Rule concepts and requirements. The concentration in 2009 has nearly fully shifted to assisting the market in creating risk-based authentication programs that leverage holistic views of a consumer, flexible tools that are pointed to a consumer based on that person’s authentication and risk profile. There is also an overall decisioning strategy that balances risk, compliance, and resource constraints. Spirit of Red Flags Rule The spirit of the Red Flags Rule is intended to ensure all covered institutions are employing basic identity theft prevention procedures (a pretty good idea).  I believe most of these institutions (even those that had very robust programs in place years before the rule was introduced) can appreciate this requirement that brings all institutions up to speed.  It is now, however, a matter of managing process within the realities of, and costs associated with, manpower, IT resources, and customer experience sensitivities.  

In my previous two blog postings, I’ve tried to briefly articulate some key elements of and value propositions associated with risk-based authentication.  In this entry, I’d like to suggest some best-practices to consider as you incorporate and maintain a risk-based authentication program. 1. Analytics – since an authentication score is likely the primary decisioning element in any risk-based authentication strategy, it is critical that a best-in-class scoring model is chosen and validated to establish performance expectations.  This initial analysis will allow for decisioning thresholds to be established.  This will also allow accept and referral volumes to be planned for operationally.  Further more, it will permit benchmarks to be established which follow on performance monitoring that can be compared. 2. Targeted decisioning strategies – applying unique and tailored decisioning strategies (incorporating scores and other high-risk or positive authentication results) to various access channels to your business just simply makes sense.  Each access channel (call center, Web, face-to-face, etc.) comes with unique risks, available data, and varied opportunity to apply an authentication strategy that balances these areas; risk management, operational effectiveness, efficiency and cost, improved collections and customer experience.  Champion/challenger strategies may also be a great way to test newly devised strategies within a single channel without taking risk to an entire addressable market and your business as a whole. 3. Performance Monitoring – it is critical that key metrics are established early in the risk-based authentication implementation process.  Key metrics may include, but should not be limited to these areas: • actual vs. expected score distributions; • actual vs. expected characteristic distributions; • actual vs. expected question performance; • volumes, exclusions; • repeats and mean scores; • actual vs. expected pass rates; • accept vs. referral score distribution; • trends in decision code distributions; and • trends in decision matrix distributions. Performance monitoring provides an opportunity to manage referral volumes, decision threshold changes, strategy configuration changes, auto-decisioning criteria and pricing for risk based authentication. 4. Reporting – it likely goes without saying, but in order to apply the three best practices above, accurate, timely, and detailed reporting must be established around your authentication tools and results.  Regardless of frequency, you should work with internal resources and your third-party service provider(s) early in your implementation process to ensure relevant reports are established and delivered. In my next posting, I will be discussing some thoughts about the future state of risk based authentication.  

In a recent article, www.CNNMoney.com reported that Federal Reserve Chairman, Ben Bernanke, said that the pace of recovery in 2010 would be moderate and added that the unemployment rate would come down quite slowly, due to headwinds on ongoing credit problems and the effort by families to reduce household debt.’ While some media outlets promote an optimistic economic viewpoint, clearly there are signs that significant challenges lie ahead for lenders. As Bernanke forecasts, many issues that have plagued credit markets will sustain themselves in the coming years. Therefore lenders need to be equipped to monitor these continued credit problems if they wish to survive this protracted time of distress. While banks and financial institutions are implementing increasingly sophisticated and thorough processes to monitor fluctuations in credit trends, they have little intelligence to compare their credit performance to that of their peers.  Lenders frequently cite that they are concerned about their lack of awareness or intelligence regarding the credit performance and status of their peers.  Marketing intelligence solutions are important for management of risk, loan portfolio monitoring and related decisioning strategies. Currently, many vendors offer data on industry-wide trends, but few vendors provide the information needed to allow a lender to understand its position relative to a well-defined group of firms that it considers its peers. As a result, too many lenders are performing benchmarking using data sources that are biased, incomplete, inaccurate, or that lack the detail necessary to derive meaningful conclusions. If you were going to measure yourself personally against a group to understand your comparative performance, why would you perform that comparison against people who had little or nothing in common with you? Does an elite runner measure himself against a weekend warrior to gauge his performance? No; he segments the runners by gender, age, and performance class to understand exactly how he stacks up. Today’s lending environment is not forgiving enough for lenders to make broad industry comparisons if they want to ensure long-term success. Lenders cannot presume they are leading the pack, when, in fact, the race is closer than ever.  

The term “risk-based authentication” means many things to many institutions.  Some use the term to review to their processes; others, to their various service providers.  I’d like to establish the working definition of risk-based authentication for this discussion calling it:  “Holistic assessment of a consumer and transaction with the end goal of applying the right authentication and decisioning treatment at the right time.” Now, that “holistic assessment” thing is certainly where the rubber meets the road, right? One can arguably approach risk-based authentication from two directions.  First, a risk assessment can be based upon the type of products or services potentially being accessed and/or utilized (example: line of credit) by a customer.  Second, a risk assessment can be based upon the authentication profile of the customer (example: ability to verify identifying information).  I would argue that both approaches have merit, and that a best practice is to merge both into a process that looks at each customer and transaction as unique and therefore worthy of  distinctively defined treatment. In this posting, and in speaking as a provider of consumer and commercial authentication products and services, I want to first define four key elements of a well-balanced risk based authentication tool: data, detailed and granular results, analytics, and decisioning. 1.  Data: Broad-reaching and accurately reported data assets that span multiple sources providing far reaching and comprehensive opportunities to positively verify consumer identities and identity elements. 2.  Detailed and granular results: Authentication summary and detailed-level outcomes that portray the amount of verification achieved across identity elements (such as name, address, Social Security number, date of birth, and phone) deliver a breadth of information and allow positive reconciliation of high-risk fraud and/or compliance conditions.  Specific results can be used in manual or automated decisioning policies as well as scoring models, 3.  Analytics:  Scoring models designed to consistently reflect overall confidence in consumer authentication as well as fraud-risk associated with identity theft, synthetic identities, and first party fraud.  This allows institutions to establish consistent and objective score-driven policies to authenticate consumers and reconcile high-risk conditions.  Use of scores also reduces false positive ratios associated with single or grouped binary rules.  Additionally, scores provide internal and external examiners with a measurable tool for incorporation into both written and operational fraud and compliance programs, 4.  Decisioning: Flexibly defined data and operationally-driven decisioning strategies that can be applied to the gathering, authentication, and level of acceptance or denial of consumer identity information.  This affords institutions an opportunity to employ consistent policies for detecting high-risk conditions, reconcile those terms that can be changed, and ultimately determine the response to consumer authentication results – whether it be acceptance, denial of business or somewhere in between (e.g., further authentication treatments). In my next posting, I’ll talk more specifically about the value propositions of risk-based authentication, and identify some best practices to keep in mind.      

By: Wendy Greenawalt In my last blog post I discussed the value of leveraging optimization within your collections strategy. Next, I would like to discuss in detail the use of optimizing decisions within the account management of an existing portfolio. Account Management decisions vary from determining which consumers to target with cross-sell or up-sell campaigns to line management decisions where an organization is considering line increases or decreases.  Using optimization in your collections work stream is key. Let’s first look at lines of credit and decisions related to credit line management. Uncollectible debt, delinquencies and charge-offs continue to rise across all line of credit products. In response, credit card and home equity lenders have begun aggressively reducing outstanding lines of credit.    One analyst predicts that the credit card industry will reduce credit limits by $2 trillion by 2010. If materialized, that would represent a 45 percent reduction in credit currently available to consumers. This estimate illustrates the immediate reaction many lenders have taken to minimize loss exposure. However, lenders should also consider the long-term impacts to customer retention, brand-loyalty and portfolio profitability before making any account management decision. Optimization is a fundamental tool that can help lenders easily identify accounts that are high risk versus those that are profit drivers. In addition, optimization provides precise action that should be taken at the individual consumer level. For example, optimization (and optimizing decisions) can provide recommendations for: • when to contact a consumer; • how to contact a consumer; and • to what level a credit line could be reduced or increased... …while considering organizational/business objectives such as: • profits/revenue/bad debt; • retention of desirable consumers; and • product limitations (volume/regional). In my next few blogs I will discuss each of these variables in detail and the complexities that optimization can consider.  

By: Kari Michel This blog completes my discussion on monitoring new account decisions with a final focus: scorecard monitoring and performance.  It is imperative to validate acquisitions scorecards regularly to measure how well a model is able to distinguish good accounts from bad accounts. With a sufficient number of aged accounts, performance charts can be used to: • Validate the predictive power of a credit scoring model; • Determine if the model effectively ranks risk; and • Identify the delinquency rate of recently booked accounts at various intervals above and below the primary cutoff score. To summarize, successful lenders maximize their scoring investment by incorporating a number of best practices into their account acquisitions processes: 1. They keep a close watch on their scores, policies, and strategies to improve portfolio strength. 2. They create monthly reports to look at population stability, decision management, scoring models and scorecard performance. 3. They update their strategies to meet their organization’s profitability goals through sound acquisition strategies, scorecard monitoring and scorecard management.

By: Kari Michel This blog is a continuation of my previous discussion about monitoring your new account acquisition decisions with a focus on decision management. Decision management reports provide the insight to make more targeted decisions that are sound and profitable. These reports are used to identify: which lending decisions are consistent with scorecard recommendations; the effectiveness of overrides; and/or whether cutoffs should be adjusted. Decision management reports include: • Accept versus decline score distributions • Override rates • Override reason report • Override by loan officer • Decision by loan officer Successful lending organizations review this type of information regularly to make better lending policy decisions.  Proactive monitoring provides feedback on existing strategies and helps evaluate if you are making the most effective use of your score(s). It helps to identify areas of opportunity to improve portfolio profitability. In my next blog, I will discuss the last set of monitoring reports, scorecard performance.  

By: Tracy Bremmer In our last blog (July 30), we covered the first three stages of model development which are necessary whether developing a custom or generic model.  We will now discuss the next three stages, beginning with the “baking” stage:  scorecard development. Scorecard development begins as segmentation analysis is taking place and any reject inference (if needed) is put into place. Considerations for scorecard development are whether the model will be binned (divides predictive attributes into intervals) or continuous (variable is modeled in its entirety), how to account for missing values (or “false zeros”), how to evaluate the validation sample (hold-out sample vs. an out-of-time sample), avoidance of over-fitting the model, and finally what statistics will be used to measure scorecard performance (KS, Gini coefficient, divergence, etc.). Many times lenders assume that once the scorecard is developed, the work is done.   However, the remaining two steps are critical to development and application of a predictive model:  implementation/documentation and scorecard monitoring.   Neglecting these two steps is like baking a cake but never taking a bite to make sure it tastes good. Implementation and documentation is the last stage in developing a model that can be put to use for enhanced decisioning. Where the model will be implemented will determine the timeliness and complexity for when the models can be put into practice. Models can be developed in an in-house system, a third-party processor, a credit reporting agency, etc. Accurate documentation outlining the specifications of the model will be critical for successful implementation and model audits. Scorecard monitoring will need to be put into place once the model is developed, implemented and put into use. Scorecard monitoring evaluates population stability, scorecard performance, and decision management to ensure that the model is performing as expected over the course of time. If at any time there are variations based on initial expectations, then scorecard monitoring allows for immediate modifications to strategies. With all the right ingredients, the right approach, and the checks and balances in place, your model development process has the potential to come out “just right!”  

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: Tracy Bremmer It’s not really all about the credit score. Now don’t get me wrong, a credit score is a very important tool used in credit decision making; however there’s so much more that lenders use to say “accept” or “decline.” Many lenders segment their customer/prospect base prior to ever using the score. They use credit-related attributes such as, “has this consumer had a bankruptcy in the last two years?” or “do they have an existing mortgage account?” to segment out consumers into risk-tier buckets. Lenders also evaluate information from the application such as income or number of years at current residence. These types of application attributes help the lender gain insight that is not typically evaluated in the traditional risk score. For lenders who already have a relationship with a customer, they will look at their existing relationships with that customer prior to making a decision. They’ll look at things like payment history and current product mix to better understand who best to cross-sell, up-sell, or in today’s economy, down-sell. In addition, many lenders will run the applicant through some type of fraud database to ensure the person really is who they say they are. I like to think of the score as the center of the decision, with all of these other metrics as necessary inputs to the entire decision process. It is like going out for an ice cream sundae and starting with the vanilla and needing all the mix-ins to make it complete.