Fraud and Identity Solutions

Introduction to our bloggers

Heather Grover
Senior Director of Product Management, Fraud and Identity Solutions, Experian Decision Analytics

Heather Grover is Senior Director of Product Management for Experian’s Fraud and Identity Solutions group. In this role, she is responsible for the management, development and strategic direction of all Precise IDSM, authentication, database and analytic product offerings. Prior to this role, she was responsible for authentication and product direction for the authentication product suite and Precise ID platform.

Grover has been with Experian for eight years, during which time she has held increasingly responsible positions in various Experian business units. Before joining the Fraud and Identity Solutions group, she served as Senior Manager of Internet Delivery for Experian’s Credit Information Services business unit. Prior to joining Experian, Grover served as a Senior Marketing Producer for AltaVista. She also has held product marketing and product management positions at Ingram Micro, Mattel and Dun & Bradstreet Information Publishing.

Grover holds a Bachelor of Arts in business administration from California State University, Fullerton, and a Master of Business Administration from the University of California, Irvine.

Dr. Alex Barclay
Principal Scientist, Fraud and Identity Solutions, Experian Decision Analytics

Dr. Alex Barclay is a Principal Scientist leading the Fraud Analytic Consulting team within Experian’s Fraud and identity Solutions group. Prior to joining Experian in 2006, he worked for HNC Software and Fair Isaac Corporation for more than six years. He has experience with both application and transactional fraud across financial services, telecommunications, online commerce and health care. Prior to returning to graduate school, Dr. Barclay worked in satellite operations for the U.S. Defense Department at Lockheed Corporation.

Dr. Barclay earned a Bachelor of Science in computational mathematics from the University of California at Santa Cruz and both a Master of Science and a Doctor of Philosophy in applied mathematics from the University of California at San Diego.

Monica Bellflower
Product Manager, Fraud and Identity Solutions, Experian Decision Analytics

Monica Bellflower is a Product Manager for Experian Decision Analytics’ Fraud and Identity Solutions, accountable for identifying and evaluating new market opportunities as well as developing and executing long-term product strategy. She currently manages a number of fraud products, including Authentication Services levels 1, 2 and 3; Credit Card Verification; and Knowledge IQSM. By collecting competitive intelligence and responding appropriately to threats in the marketplace, Bellflower builds a business case and go-to-market strategies for select opportunities — creating and managing the product road map and enhancements along the way.

Since joining Experian in 2007, Bellflower has assumed increasing responsibilities within the product team, taking on the added challenges of leading various training sessions and presenting at Experian’s Vision Conference. Prior to assuming her role at Experian, Bellflower was a Senior Project Manager for a privately held software company, where she managed all phases of large-scale client projects, from sales to postproduction support. She also has served as the Lending Relations Manager and International Regional Business Manager for a large technology equipment lessor.

Bellflower holds a Master of Business Administration with an emphasis in marketing from California Lutheran University.

Keir Breitenfeld
Director, Experian Decision Analytics

Keir is Director of Product Management within Experian's Decision Analytics team, with specific responsibility for Experian's core consumer authentication products, Precise ID and Knowledge IQ and Commercial Fraud Insight.  Via these, and other offerings, clients are able to confidently verify consumer identities in non-face-to-face interactions, while detecting and preventing fraudulent applications and transactions.  
 
Keir is a former naval officer with an additional ten years of industry experience in the credit card, electronic funds transfer, fraud prevention, and product management arenas.  He graduated from Auburn University and earned a Master of Business Administration from the Fuqua School of Business at Duke University.



Matt Ehrlich
Product Manager, Fraud and Identity Solutions, Experian Decision Analytics

Matt Ehrlich is a Product Manager for Experian Decision Analytics’ Fraud and Identity Solutions. His current responsibilities include managing the National Fraud DatabaseSM and co-managing Precise IDSM. He brings extensive analyst and project management experience to the development and management of these core Experian products.

Prior to joining Experian, Ehrlich was a Customer Relations Manager, overseeing the client implementation of software products. As a Business Analyst, he also managed cross-functional initiatives for a software development team and optimized network efficiencies. Ehrlich earned a Bachelor of Arts in English, with a minor in Business Administration, from the University of California at Los Angeles.


Andrew Gulledge
Strategic Consultant Expert, Fraud and Identity Solutions, Experian Decision Analytics

A highly skilled social scientist, researcher and published author, Andrew Gulledge brings considerable knowledge and experience to his role as a Strategic Consultant Expert for Experian’s Fraud and Identity Solutions group. As an expert in knowledge-based authentication, Gulledge works closely with the company’s corporate clients to help them improve their risk-management strategies as they relate to fraud prevention using a consultative-based approach.

In the past 12 months, Gulledge has undertaken more than 50 knowledge-based authentication reviews for Experian’s clients to ensure that they remain at the forefront of this technology. In addition, Gulledge has been instrumental in developing a performance monitoring package that enables our clients to receive periodic updates and recommendations on how to continually optimize the tool’s use. He has been with Experian since 2007.

Gulledge earned a Bachelor of Science in psychology from Brigham Young University and a Master of Science from Loma Linda University.

Kristan Keelan
Product Marketing Manager, Fraud and Identity Solutions, Experian Decision Analytics

Kristan Keelan is a Product Marketing Manager within Experian Decision Analytics’ Fraud and Identity
Solutions, working closely with clients and their fraud migration activities within account origination and existing portfolio management. She plays a key role in the expansion of commercial fraud capabilities across business lines.

Keelan benefits from various industry risk-management experiences, including mitigating transactional fraud, optimizing business rule performance, and meeting regulatory compliance for companies such as Fidelity National Information Services and Mercer Consulting.

Keelan holds a Bachelor of Science in risk management from Florida State University. 

Ken Pruett
Global Fraud Consultant, Fraud and Identity Solutions, Experian Decision Analytics

Ken Pruett is the Global Fraud Consultant for Experian Decision Analytics. He provides consultative expertise related to fraud and authentication to both current and potential clients. Pruett brings more than 11 years of strategic selling experience dealing with advanced software, decision analytics and fraud products and services, preceded by nine years of insurance industry client side experience.
Prior to joining Experian in 2005, Pruett was National Sales Manager for TransUnion LLC, responsible for managing and implementing a fraud risk solutions platform. He also has extensive managerial experience in the insurance industry in the special investigation area.

Pruett earned a Bachelor of Science in business management from California State University, Chico.

Chris Ryan
Director of Strategic Client Development,Fraud and Identity Solutions,Experian Decision Analytics

Chris Ryan is Director of Strategic Client Development for Experian Decision Analytics’ Fraud and Identity Solutions. He works closely with our larger clients to understand their needs and challenges, analyzing key areas to help reduce fraud losses and improve their business. During his 11-year career in fraud prevention technology, Ryan has supported clients through the development and use of products and services to thwart application fraud, transaction fraud and ATM/Debit fraud.

Ryan has vast experience working with credit and debit card issuers, retail banks and merchants, student loan providers and credit unions. His commitment and dedication to the fraud prevention industry have resulted in great respect among colleagues, clients and industry experts.

Ryan holds a Bachelor of Arts in communication from the University of Delaware.

Kennis Wong
Director of Product Management, Fraud and Identity Solutions, Experian Decision Analytics

Kennis Wong is Director of Product Management for Experian’s Fraud and Identity Solutions group, part of the Decision Analytics business unit. Wong leads fraud scoring and analytics products and strategy development. Prior to this role, Wong led the strategy development efforts for alternative data assets at Experian. In addition, her time at Experian has been spent focusing on analytical consulting and the development of business credit products and an Internet delivery road map.

Before joining Experian, Wong was a Product Manager leading the development of various
Web-based products. As a Product Manager and Technical Consultant, she also managed consulting engagements and system implementations.

Wong earned a Bachelor of Science in management sciences and information systems from Pennsylvania State University and a Master of Business Administration from the University of Southern California.

Round 1 – Pick your corner

---by Monica Bellflower

There seems to be two viewpoints in the market today about knowledge based authentication: one positive, one negative.  Depending on the corner you choose, you probably view it as either a tool to help reduce identity theft and minimize fraud losses, or a deficiency in the management of risk and the root of all evil.  The opinions on both sides are pretty strong, and biases “for” and “against” run pretty deep.

One of the biggest challenges in discussing knowledge based authentication as part of an organization’s identity theft prevention program, is the perpetual confusion between dynamic out-of-wallet questions and static “secret” questions.  At this point, most people in the industry agree that static secret questions offer little consumer protection.  Answers are easily guessed, or easily researched, and if the questions are preference based (like “what is your favorite book?”) there is a good chance the consumer will fail the authentication session because they forgot the answers or the answers changed over time.

Dynamic knowledge based authentication, on the other hand, presents questions that were not selected by the consumer.  Questions are generated from information known about the consumer – concerning things the true consumer would know and a fraudster most likely wouldn’t know.  The questions posed during knowledge based authentication sessions aren’t designed to “trick” anyone but a fraudster, though a best in class product should offer a number of features and options.  These may allow for flexible configuration of the product and deployment at multiple points of the consumer life cycle without impacting the consumer experience.

The two are as different as night and day.  Do those who consider “secret questions” as knowledge based authentication consider the password portion of the user name and password process as KBA, as well?  If you want to hold to strict logic and definition, one could argue that a password meets the definition for knowledge based authentication, but common sense and practical use cause us to differentiate it, which is exactly what we should do with secret questions – differentiate them from true knowledge based authentication.

Knowledge based authentication can provide strong authentication or be a part of a multifactor authentication environment without a negative impact on the consumer experience.  So, for the record, when we say knowledge based authentication we mean dynamic, out of wallet questions, the kind that are generated “on the fly” and delivered to a consumer via “pop quiz” in a real-time environment; and we think this kind of knowledge based authentication does work.  As part of a risk management strategy, knowledge based authentication has a place within the authentication framework as a component of risk based authentication… and risk based authentication is what it is really all about.

--by Keir Breitenfeld
 
Many compliance regulations such the Red Flags Rule, USA Patriot Act, and ESIGN require specific identity elements to be verified and specific high risk conditions to be detected. However, there is still much variance in how individual institutions reconcile referrals generated from the detection of high risk conditions and/or the absence of identity element verification. With this in mind, risk-based authentication, (defined in this context as the “holistic assessment of a consumer and transaction with the end goal of applying the right authentication and decisioning treatment at the right time") offers institutions a viable strategy for balancing the following competing forces and pressures:

• Compliance – the need to ensure each transaction is approved only when compliance requirements are met;
• Approval rates – the need to meet business goals in the booking of new accounts and the facilitation of existing account transactions;
• Risk mitigation – the need to minimize fraud exposure at the account and transaction level.

A flexibly-designed risk-based authentication strategy incorporates a robust breadth of data assets, detailed results, granular information, targeted analytics and automated decisioning. This allows an institution to strike a harmonious balance (or at least something close to that) between the needs to remain compliant, while approving the vast majority of applications or customer transactions and, oh yeah, minimizing fraud and credit risk exposure and credit risk modeling.

 Sole reliance on binary assessment of the presence or absence of high risk conditions and identity element verifications will, more often than not, create an operational process that is overburdened by manual referral queues. There is also an unnecessary proportion of viable consumers unable to be serviced by your business. Use of analytically sound risk assessments and objective and consistent decisioning strategies will provide opportunities to calibrate your process to meet today’s pressures and adjust to tomorrow’s as well.
 
 
 

--by Kennis Wong

It's true that intent is difficult to prove. It's also true that financial situations change. That's why financial institutions have not, yet, successfully fought off first-party fraud. However, there are some tell-tale signs of intent when you look at the consumer's behavior as a whole, particularly across all his/her financial relationships.

For example, in a classic bust out case, you would see that the consumer, with pristine credit history, applies for more and more credit cards while maintaining a relatively low balance and utilization across all issuers. If you graph the number of credit cards and number of credit applications over time, you would see two hockey-stick lines. When the accounts go bad, they do so at almost the same time. This pattern is not always apparent at the time of origination, that's why it's important to monitor frequently for account review and fraud database alerts.

On the other hand, consumers with financial difficulties have different patterns. They might have more credit lines over time, but you would see that some credit lines may go delinquent while others don't. You might also see that consumers cure some lines after delinquencies…you can see their struggle of trying to pay.

Of course the intent "pattern" is not always clear. When dealing with fraudsters in fraud account management, even with the help of the fraud database, fraud trends and fraud alert, change their behaviors and use new techniques.

--by Matt Ehrlich

On Friday, October 30th, the FTC again delayed enforcement of the “Red Flags” Rule – this time until June 1, 2010 – for financial institutions and creditors subject to the FTC’s enforcement.   Here’s the official release: http://www.ftc.gov/opa/2009/10/redflags.shtm. 

But this doesn’t mean, until then, businesses get a free pass.  The extension doesn’t apply to other federal agencies that have enforcement responsibilities for institutions under their jurisdiction.  And the extension also doesn’t alleviate an institution’s need to detect and respond to address discrepancies on credit reports.

Red Flag compliance

Implementing best practices to address the identity theft under the Red Flags Rule is not just the law, it’s good business.  The damage to reputations and consumer confidence from a problem gone unchecked or worse yet – unidentified – can be catastrophic.  I encourage all businesses – if they haven’t already done so – to use this extension as an opportunity to proactively secure a Red Flags Rule to ensure Red Flag compliance.  It’s an investment in protecting their most important asset – the customer.

--by Keir Breitenfeld

As I wrote in my previous posting, a key Red Flags Rule challenge facing many institutions is one that manages the number of referrals generated from the detection of Red Flags conditions.  The big ticket item in referral generation is the address mismatch condition.

Identity Theft Prevention Program
I’ve blogged previously on the subject of risk-based authentication and risk-based pricing, so I won’t rehash that information.  What I will suggest, however, is that those institutions who now have an operational Identity Theft Prevention Program (if you don’t, I’d hurry up) should continue to explore the use of alternate data sources, analytics and additional authentication tools (such as knowledge-based authentication) as a way to detect Red Flags conditions and reconcile them all within the same real-time transaction.

Referral rates
Referral rates stemming from address mismatches (a key component of the Red Flags Rule high risk conditions) can approach or even surpass 30 percent.  That is a lot.  The good news is that there are tools which employ additional data sources beyond a credit profile to “find” that positive address match.  The use of alternate data sources can often clear the majority of these initial mismatches, leaving the remaining transactions for treatment with analytics and knowledge-based authentication and Identity Theft Prevention Program.

Whatever “referral management” process you have in place today, I’d suggest exploring risk-based authentication tools that allow you to keep the vast majority of those referrals out of the hands of live agents, and distanced from the need to put your customers through the authentication wringer.  In the current marketplace, there are many services that allow you to avoid high referral costs and risks to customer experience.  Of course, we think ours are pretty good.

--by Keir Breitenfeld

Well, here we are at the beginning of November and The Red Flags Rule has been with us for nearly two years now.  And to add to that, the FTC’s November 1, 2009 enforcement date has passed (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 client comments

1. Most clients have a solid written and operational Identity Theft Prevention Program 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..

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 of Red Flags Rule concepts 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.

--by Matt Ehrlich

In my last entry, I talked about the challenges clients face in trying to meet multiple and complex regulatory requirements, such as FACT Act’s Red Flags Rule and the USA Patriot Act.  While these regulations serve both different and shared purposes, there are some common threads between the two:

1. You must consider the type of accounts and methods of account opening: The type of account offered - credit or deposit, consumer or business – as well as the method of opening – phone, online, or face-to-face – has a bearing on the steps you need to take and the process that will be established.

2. Use of consumer name, address, and identification number:The USA Patriot Act requires each of these – plus date of birth – to open a new account.  Red Flags stops short of “requiring” these for new account openings, but it consistently illustrates the use of these Personally Identifiable Information (PII) elements as examples of reasonable procedures to detect red flags.

3. Establishing identity through non-documentary verification:Third party information providers, such as a credit reporting agency or data broker, can be used to confirm identity, particularly in the case where the verification is not done in person.

Knowing what’s in common means you can take a look at where to leverage processes or tools to gain operational and cost efficiencies and reduce negative impact on the customer experience.  For example, if you’re using any authentication products today to comply with the USA Patriot Act and/or minimize fraud losses, the information you collect from consumers and authentication steps you are already taking now may suffice for a large portion of your Red Flags Identity Theft Prevention Program. 

And if you’re considering fraud and compliance products for account opening or account management – it’s clear that you’ll want something flexible that, not only provides identity verification, but scales to the compliance programs you put in place, and those that may be on the horizon.

--by Matt Ehrlich

While the FACT Act’s Red Flags Rule seems to capture all of the headlines these days, it’s just one of a number of compliance challenges that banks, credit unions, and a myriad of other institutions face on a daily basis.  And meeting today’s regulatory requirements is more complicated than ever.  Risk managers and compliance officers are asked to consider many questions, including:

1. Do FACTA Sections 114 and 315 apply to me?
2. What do I have to do to comply?
3. What impact does this have on the customer’s experience?
4. What is this going to cost me in terms of people and process?

Interpretation of the law or guideline – including who it applies to and to whom it does not - varies widely.  Which types of businesses are subject to the Red Flags Rule?  What is a “covered account?”  If you’re not sure, you’re not alone - it’s a primary reason why the Federal Trade Commission (FTC) continues to postpone enforcement of the rule, while this healthy debate continues.

And by the way, FTC – it’s almost November 1st…aren’t we about due for another delay? But we’re not talking about just protecting consumers from identity theft and reducing fraud and protecting themselves using the Identity Theft Prevention Program.

The USA Patriot Act and “Know Your Customer” requirements have been around much longer, but there are current challenges of interpretation and practical application when it comes to identifying customers and performing due diligence to deter fraud and money laundering.  Since Customer Identification Programs require procedures based on the bank’s own “assessment of the relevant risks,” including types of accounts opened, methods of opening, and even the bank’s “size, location, and customer base,” it’s safe to say that each program will differ slightly – or even greatly.

So it’s clear there’s a lack of specificity in the regulations of the Red Flags Rule which cause heartburn for those tasked with compliance…but are there some common themes and requirements across the two?  The short answer is Yes.  In my next post, I’ll talk about the elements in common and how authentication products can play a part in addressing both.

-- by Keir Breitenfeld

In my previous three postings, I’ve covered basic principles that can define a risk-based authentication process, associated value propositions, and some best-practices to consider.

Finally, I’d like to briefly discuss some emerging informational elements and processes that enhance (or have already enhanced) the notion of risk-based authentication in the coming year.  For simplicity, I’m boiling these down to three categories:

1. Enterprise Risk Management – As you’d imagine, this concept involves the creation of a real-time, cross channel, enterprise-wide (cross business unit) view of a consumer and/or transaction.  That sounds pretty good, right?  Well, the challenge has been, and still remains, the cost of developing and implementing a data sharing and aggregation process that can accomplish this task.  There is little doubt that operating in a more silo’d environment limits the amount of available high-risk and/or positive authentication data associated with a consumer…and therefore limits the predictive value of tools that utilize such data.  It is only a matter of time before we see more widespread implementation of systems designed to look at a single transaction, an initial application profile, previous authentication results, or other relationships a consumer may have within the same organization -- and across all of this information in tandem.  It’s simply a matter of the business case to do so, and the resources to carry it out.

2. Additional Intelligence – Beyond some of the data mentioned above, some additional informational elements emerging as useful in isolation (or, even better, as a factor among others in a holistic assessment of a consumer’s identity and risk profile) include these areas:  IP address vs. physical address comparisons; device ID or fingerprinting; and biometrics (such as voice verification).  While these tools are being used and tested in many organizations and markets, there is still work to be done to strike the right balance as they are incorporated into an overall risk-based authentication process.  False positives, cost and implementation challenges still hinder widespread use of these tools from being a reality.  That should change over time, and quickly to help with the cost of credit risk.

3. Emerging Verification Techniques – Out-of-band authentication is defined as the use of two separate channels, used simultaneously, to authenticate a customer.  For example: using a phone to verify the identity of that person while performing a Web transaction.  Similarly, many institutions are finding success in initiating SMS texts as a means of customer notification and/or verification of monetary or non-monetary transactions.  The ability to reach out to a consumer in a channel alternate to their transaction channel is a customer friendly and cost effective way to perform additional due diligence.

--by Kennis Wong

In Part 1 of Generic fraud score, we emphasized the importance of a risk-based approach when it comes to fraud detection. Here are some further questions you may want to consider.

What is the performance window?

When a model is built, it has a defined performance window. That means the score is predicting a certain outcome within that time period. For example, a traditional risk score may be predicting accounts that are decreasing in twenty-four months. That score may not perform well if your population typically worsens in two months. This question is particularly important when it relates to scoring your population. For example, if a bust-out score has a performance window of three months, and you score your accounts at the time of acquisition, it would only catch accounts that are busting-out within the next three months. As a result, you should score your accounts during periodic account reviews in addition to the time of acquisition to ensure you catch all bust-outs.  Therefore, bust out fraud is an important indicator. 

Which accounts should I score?

While it’s typical for creditors to use a fraud score on every applicant at the time of acquisition, they may not score all their accounts during review. For example, they may exclude inactive accounts or older accounts assuming those with a long history means less likelihood of fraud. This mistake may be expensive. For instance, the typical bust-out behavior is for fraudsters to apply for cards way before they intend to bust out. This may be forty-eight months or more. So when you think they are good and profitable customers, they can strike and leave you with seriously injury. Make sure that your fraud database is updated and accurate.  As a result, the recommended approach is to score your entire portfolio during account review. 

How often do I validate the score?

The answer is very often -- this may be monthly or quarterly. You want to understand whether the score is working for you – do your actual results match the volume and risk projections? Shifts of your score distribution will almost certainly occur over time. To meet your objectives over the long run, continue to monitor and adjust cutoffs.  Keep your fraud database updated at all times.

--- by Kennis Wong

In this blog entry, we have repeatedly emphasized the importance of a risk-based approach when it comes to fraud detection. Scoring and analytics are essentially the heart of this approach.

However, unlike the rule-based approach, where users can easily understand the results, (i.e. was the S.S.N. reported deceased? Yes/No; Is the application address the same as the best address on the credit bureau? Yes/No), scores are generated in a black box where the reason for the eventual score is not always apparent even in a fraud database.

Hence more homework needs to be done when selecting and using a generic fraud score to make sure they satisfy your needs. Here are some basic questions you may want to ask yourself:

What do I want the score to predict?
This may seem like a very basic question, but it does warrant your consideration. Are you trying to detect these areas in your fraud database? First-party fraud, third-party fraud, bust out fraud, first payment default, never pay, or a combination of these? These questions are particularly important when you are validating a fraud model. For example, if you only have third-party fraud tagged in your test file, a bust out fraud model would not perform well. It would just be a waste of your time.

What data was used for model development?
Other important questions you may want to ask yourself include:  Was the score based on sub-prime credit card data, auto loan data, retail card data or another fraud database? It’s not a definite deal breaker if it was built with credit card data, but, if you have a retail card portfolio, it may still perform well for you. If the scores are too far off, though, you may not have good result. Moreover, you also want to understand the number of different portfolios used for model development. For example, if only one creditor’s data is used, then it may not have the general applicability to other portfolios.

-- by Keir Breitenfeld

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.

-- by Keir Breitenfeld
 
In my last blog posting, I presented the foundational elements that enable risk-based authentication.  These include data, detailed and granular results, analytics and decisioning.  The inherent value of risk-based authentication can be summarized as delivering an holistic assessment of a consumer and/or transaction with the end goal of applying the right authentication and decisioning treatment at the right time.  The opportunity, especially, to minimize fraud losses using fraud analytics as part of your assessment is significant.

What are some residual values of risk-based authentication? 

1. Minimized fraud losses involves the use of fraud analytics, and a more comprehensive view of a consumer identity (the good and the bad), in combination with consistent decisioning over time.  This analysis will outperform simple binary rules and more subjective decisioning.

2. Improved consumer experience.  By applying the right authentication and  treatment at the right time, consumers are subjected to processes that are proportional to the risk associated with their identity profile.  This means that lower-risk consumers are less likely to be put through more arduous courses of action, preserving a streamlined and often purely “behind the scenes” authentication process for the majority of consumers and potential consumers.  In other words, you are saving the pain for the bad guys -- and that can be a good thing.

3. Operational efficiencies can be successful with the implementation of a well-designed program. Much of the decisioning can be done without human intervention and subjective contemplation.  Use of score-driven policies affords businesses the opportunity to use automated authentication processes for the majority of their applicants or account management cases.  Fewer human resources will be required which usually means lower costs.  Or, it can mean the human resources you possess are more appropriately focused on the applications or transactions that warrant such attention.

4. Measurable performance is critical because understanding the past and current performance of risk-based authentication policies allows for the adjustment over time of such policies.  These adjustments can be made based on evolving fraud risks, resource constraints, approval rate pressures, and compliance requirements, just to name a few.  Given its importance, Experian recommends performance monitoring for our clients using our authentication products. 

In my next posting, I’ll discuss some best practices associated with implementing and managing a risk-based authentication program.

Red Flags Rule and commercial accounts

-- by Kristan Keelan

Most financial institutions are well underway in complying with the FTC’s ID Theft Red Flags Rule by:

1.  Identifying covered accounts  
2.  Determining what red flags need to be monitored
3.  Implementing a risk based approach 

However, one of the areas that seems to be overlooked in complying with the rule is the area of commercial accounts.  Did your institution include commercial accounts when identifying covered accounts?  You’re not alone if you focused only on consumer accounts initially.

Keep in mind that commercial credit and deposit accounts also can be included as covered accounts when there is a “reasonably foreseeable risk” of identity theft to customers or to safety and soundness.

Start by determining if there is a reasonably foreseeable risk of identity theft in a business or commercial account, especially in small business accounts.   Consider the risk of identity theft presented by the methods used to open business accounts, the methods provided to access business accounts, and previous experiences with identity theft on a business account.

I encourage you to revisit your institution’s compliance program and review whether commercial accounts have been examined closely enough.

-- by Kristan Keelan

What do you think of when you hear the word “fraud”?  Someone stealing your personal identity?  Perhaps the recent news story of the five individuals indicted for gaining more than $4 million from 95,000 stolen credit card numbers?  It’s unlikely that small business fraud was at the top of your mind.   Yet, just like consumers, businesses face a broad- range of first- and third-party fraud behaviors, varying significantly in frequency, severity and complexity. Business-related fraud trends call for new fraud best practices to minimize fraud.

First let’s look at first-party fraud.  A first-party, or victimless, fraud profile is characterized by having some form of material misrepresentation (for example, misstating revenue figures on the application) by the business owner without  that owner’s intent or immediate capacity to pay the loan item.  Historically, during periods of economic downturn or misfortune, this type of fraud is more common.  This intuitively makes sense — individuals under extreme financial pressure are more likely to resort to desperate measures, such as misstating financial information on an application to obtain credit.  

Third-party commercial fraud occurs when a third party steals the identification details of a known business or business owner in order to open credit in the business victim’s name.  With creditors becoming more stringent with credit-granting policies on new accounts, we’re seeing seasoned fraudsters shift their focus on taking over existing business or business owner identities.

Overall, fraudsters seem to be migrating from consumer to commercial fraud.   I think one of the most common reasons for this is that commercial fraud doesn’t receive the same amount of attention as consumer fraud.  Thus, it’s become easier for fraudsters to slip under the radar by perpetrating their crimes through the commercial channel.   Also, keep in mind that businesses are often not seen as victims in the same way that consumers are.  For example, victimized businesses aren’t afforded the protections that consumers receive under identity theft laws, such as access to credit information.   These factors, coupled with the fact that business-to-business fraud is approximately three-to-ten times more “profitable” per occurrence than consumer fraud, play a role in leading fraudsters increasingly toward commercial fraud.
 

-- by Keir Breitenfeld

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 Kennis Wong

As I said in my last post, when consumers and the media talk about fraud and fraud risk, they are usually referring to third-party frauds. When financial institutions or other organizations talk about fraud and fraud best practices, they usually refer to both first- and third-party frauds.

The lesser-known fraud cousin, first-party fraud, does not involve stolen identities. As a result, first-party fraud is sometimes called victimless fraud. However, being victimless can’t be further from the truth. The true victims of these frauds are the financial institutions that lose millions of dollars to people who intentionally defraud the system.

First-party frauds happen when someone uses his/her own identity or a fictitious identity to apply for credit without the intention to fulfill their payment obligation. As you can imagine, fraud detection of this type is very difficult. Since fraudsters are mostly who they say they are, you can’t check the inconsistencies of identities in their applications. The third-party fraud models and authentication tools will have no effect on first-party frauds.

Moreover, the line between first-party fraud and regular credit risk is very fuzzy. According to Wikipedia, credit risk is the risk of loss due to a debtor's non-payment of a loan or other line of credit. Doesn’t the definition sound similar to first-party fraud? In practice, the distinction is even blurrier. That’s why many financial institutions are putting first-party frauds in the risk bucket.

But there is one subtle difference: that is the intent of the debtor.  Are the applicants planning not to pay when they apply or use the credit?  If not, that’s first-party fraud. To effectively detect frauds of this type, fraud models need to look into the intention of the applicants.
 

-- by Kennis Wong

When consumers and the media talk about fraud and fraud risk, nine out of ten times they are referring to third-party frauds. When financial institutions or other organizations talk about fraud, fraud best practices, or their efforts to minimize fraud, they usually refer to both first- and third-party frauds.

The difference between the two fraud types is huge.

Third-party frauds happen when someone impersonates the genuine identity owner to apply for credit or use existing credit. When it’s discovered, the victim, or the genuine identity owner, may have some financial loss -- and a whole lot of trouble fixing the mess. Third-party frauds get most of the spotlight in newspaper reporting primarily because of large-scale identity data losses. These data losses may not result in frauds per se, but the perception is that these consumers are now more susceptible to third-party frauds.

Financial institutions are getting increasingly sophisticated in using fraud models to detect third-party frauds at acquisition. In a nutshell, these fraud models are detecting frauds by looking at the likelihood of applicants being who they say they are. Institutions bounce the applicants’ identity information off of internal and external data sources such as: credit; known fraud; application; IP; device; employment; business relationship; DDA; demographic; auto; property; and public record. The risk-based approach takes into account the intricate similarities and discrepancies of each piece of data element.

In my next blog entry, I’ll discuss first-party fraud.
 

-- By Ken Pruett

Earlier this week I blogged about some of the other types of frauds that impact our customers such as “never pay” and “bust out” fraud. Today I want to touch a bit on some of the third party fraud scenarios that are often top of mind with our customers: identity theft; synthetic identities; and account takeover.  

Identity Theft
Identity theft usually occurs during the acquisition stage of the customer life cycle. Simply put, identity theft is the use of stolen identity information to fraudulently open up a new account.  These accounts do not have to be just credit card related. For example, there are instances of people using others identities to open up wireless phone and utilities accounts 

Recent fraud trends show this type of fraud is on the rise again after a decrease over the past several years.  A recent Experian study found that people who have better credit scores are more likely to have their identity stolen than those with very poor credit scores. It does seem logical that fraudsters would likely opt to steal an identity from someone with higher credit limits and available purchasing power.  This type of fraud gets the majority of media attention because it is the consumer who is often the victim (as opposed to a major corporation). 

Fraud changes over time and recent findings show that looking at data from a historical perspective is a good way to help prevent identity theft.  For example, if you see a phone number being used by multiple parties, this could be an indicator of a fraud ring in action.  Using these types of data elements can make your fraud models much more predictive and reduce your fraud referral rates. 

Synthetic Identities
Synthetic Identities are another acquisition fraud problem.  It is similar to identity theft, but the information used is fictitious in nature.  The fraud perpetrator may be taking pieces of information from a variety of parties to create a new identity.  Trade lines may be purchased from companies who act as middle men between good consumers with good credit and perpetrators who creating new identities.   This strategy allows the fraud perpetrator to quickly create a fictitious identity that looks like a real person with an active and good credit history. 

Most of the trade lines will be for authorized users only.  The perpetrator opens up a variety of accounts in a short period of time using the trade lines. When creditors try to collect, they can’t find the account owners because they never existed.  As Heather Grover mentioned in her blog, this fraud has leveled off in some areas and even decreased in others, but is probably still worth keeping an eye on.  One concern on which to focus especially is that these identities are sometimes used for bust out fraud. 

The best approach to predicting this type of fraud is using strong fraud models that incorporate a variety of non-credit and credit variables in the model development process.  These models look beyond the basic validation and verification of identity elements (such as name, address, and social security number), by leveraging additional attributes associated with a holistic identity -- such as inconsistent use of those identity elements.

Account Takeover
Another type of fraud that occurs during the account management period of the customer life cycle is account takeover fraud.  This type of fraud occurs when an individual uses a variety of methods to take over an account of another individual. This may be accomplished by changing online passwords, changing an address or even adding themselves as an authorized user to a credit card.  

Some customers have tools in place to try to prevent this, but social networking sites are making it easier to obtain personal information for many consumers.  For example, a person may have been asked to provide the answer to a challenge question such as the name of their high school as a means to properly identify them before gaining access to a banking account.  Today, this piece of information is often readily available on social networking sites making it easier for the fraud perpetrators to defeat these types of tools. 

It may be more useful to use out of wallet, or knowledge-based authentication and challenge tools that dynamically generate questions based on credit or public record data to avoid this type of fraud.