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" ...fraud losses are hidden..., so these hidden fraud losses are either not addressed or addressed incorrectly."

If a tree falls in a forest and no one is around to hear it, does it make a sound? You have probably heard this riddle which presents the situation of a reality (the tree falling) and a human observation (hearing a sound). Did the sound the tree made when it fell really exist when no one was around to hear it? Can something exist without it being recognized by the world? Is recognition necessary for existence?

There are many parallels between this riddle and mortgage fraud. The same riddle might be phrased: if a mortgage fraud occurred, and no one found it, did it really happen? Do the facts that there is no standard definition of mortgage fraud, no reporting requirement of fraud loss and no formal recognition of it in financial statements mean that it never happens? Mortgage fraud, like the tree falling in the forest, cannot make a sound if it is not formally defined, measured and recognized financially by the industry.

The conundrum – It’s buried in the losses

One of the primary issues with mortgage fraud is not reporting it as a separate financial loss to the institution; rather, it is only considered a contributing (unmeasured and unknown) factor to recognized loss. Losses due to mortgage fraud typically find their way into delinquency, default and foreclosure loss categories. As a result, statistics on mortgage fraud are inferred based on loss levels and assumptions of the ratio of fraud that may exist in those traditional loss categories. So, mortgage losses are never known, at best they are sometimes just estimated. Estimates are a great start, but most companies won’t spend money on fraud prevention until they can justify the bottom line reductions in fraud losses they will achieve. In the worst cases, it leads to a belief within an organization that there are no losses due to fraud. This is the conundrum – fraud losses are hidden (not reported or measured), so these hidden fraud losses are either not addressed or addressed incorrectly. Since the losses are rolling up under credit losses financially, we often see credit treatments applied to fraud losses with little success.

Define fraud

To measure fraud, it must first be defined. The words “material misrepresentation, misstatement or omission” are often used to describe mortgage fraud. Good words, but do they ever become truly operational in an organization? In most cases fraud relies heavily on two basic assumptions: 1) the misrepresentation is found, and 2) the analyst or underwriter perceives it to be material. To create an operational definition, an institution needs to go beyond merely defining it and create a standard for fraud. The definition of fraud needs to be bolstered with an operational process that finds and prevents the fraud each day.

An example is included in the following:

The Definition - Any misrepresentation, misstatement, or omission relied upon by an underwriter, lender or investor to fund, purchase, or insure a loan which could materially impact the performance of the loan.

The Operational Matrix – The matrix below indicates what a lender might do to find and prevent the material misrepresentation in loan packages.

Now establish a starting point, a baseline

Measuring the historical levels of fraud for an institution is the second step. This is called baselining. Having an historical baseline of fraud-related losses is imperative to being able to improve it. A baseline is created by reviewing on a loan by loan basis traditional loss categories such as foreclosure, early pay defaults, scratch and dent sales, and repurchase requests and classifying them as “fraud” or “credit” losses. This is often done using text analysis of notes within the loan data made during and after underwriting using data mining tools and often including extensive manual review of loan data and some loan files. This information is then used to calculate losses for the last two years. BasePoint has done this for numerous lenders to benchmark their fraud losses.

Sample Benchmark Report

Baseline reports such as these can help a lender understand their fraud losses and the lost opportunity that they face because of poor fraud prevention practices.

Establish reporting – know fraud.

Institutions need to create a monthly fraud reporting process. With continuous fraud monitoring institutions can justify fraud prevention programs, and improve their processes through expansion of solutions and techniques that are successful. And by knowing what fraud was missed, institutions can focus resources on additional areas to drive down the cost of fraud.

Measurements should include, at a bare minimum, the following statistics:

1. monthly fraud loss and cases prevented
2. monthly fraud loss and cases funded
3. monthly fraud to originated ratios (basis point calculations)
4. monthly losses and basis point ratios by loan type and channel

There are a substantial number of other measurements that will help an institution know their fraud exposure, however, these are the most common. These statistics should become a standard of knowledge for the entire organization. Metrics around the tools and operational resources to detect fraud should also be captured to ensure optimal performance.

BasePoint typically recommends some basic fraud measurements to understand the true performance of a lender’s fraud tools and processes.

1. Fraud Detection Rate – measures the total percentage of fraud dollars that is detected prior to funding as a percentage of all fraud that occurred. Lenders that detect a higher percentage of all of their losses prior to funding reduce their loss exposure. Good tools and processes will detect a majority of a lender’s losses prior to funding while only selecting a small percentage of the overall loan production for a secondary review.


2. False Positive Ratio – measures the ratio of fraud to non-fraud cases that are worked. Solutions and processes with high false positive ratios are inaccurate and place a burden on the operations group. They also create a high degree of inefficiency in the loan funding process as large percentages of loans are selected for secondary review to find only a small percentage of fraud. A good fraud process and solution should have a false positive ratio of less than 10:1 to be truly effective with greater than 50-60% detection levels of the total fraud.


3. Review Rate – measures the total percentage of applications that are flagged by the process or tool for a fraud review. A good fraud tool will find a majority of the fraud in a very small review population. Typically a lender will review less than 10% of their applications for fraud, but find a majority of their fraud dollar exposure in that population.

The trees are falling whether you hear them or not

A tree still falls whether someone is around to hear it or not. The same is true with mortgage fraud. It happens whether it is measured or not and it still results in unnecessary foreclosures, defaults and delinquencies. If we choose to “hear” that reality, then we as an industry can take steps to improve that reality going forward. By measuring fraud we can reduce it. By reducing it we can substantially lower foreclosure, default and delinquency rates.

Author

Frank McKenna is Co-founder and Chief Fraud Strategist for BasePoint Analytics based in Carlsbad, CA. He may be reached at (760) 602-4971 x104 or via email at FMcKenna@BasePointAnalytics.com

Frank McKenna, Co-founder and Chief Fraud Strategist of BasePoint Analytics

Frank helped develop and introduce advanced predictive technology to detect mortgage fraud. Frank's vast experience in fraud management has enabled him to identify unique and effective tools to manage lender risk through pattern analysis and evaluating other parties in the transaction, such as mortgage brokers.

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