Thoughts from the Office



PointPredictive Enhances DealerTrace

SAN DIEGO, Calif., April 13, 2017 – PointPredictive Inc. today announced the general availability of an enhanced version of DealerTrace™, a comprehensive consortium-based analytic solution designed to provide auto lenders with a dealer-level, holistic view of fraud and early payment default risk.  This solution allows most lenders to detect dealer fraud up to six months earlier than currently possible with existing tools.

“Dealer fraud is an industry-level problem that affects all auto lenders,” said Frank McKenna, Chief Fraud Strategist at PointPredictive. “The enhanced DealerTrace service uses the power of the Auto Fraud Consortium to provide all participating lenders with unique cross-industry insights that allow them to identify their riskiest dealers and then take proactive and appropriate action to improve their overall loan portfolio quality.”

Participating DealerTrace lenders contribute their loan-level application information to the Auto Fraud Consortium on a monthly basis and provide information about fraudulent or early payment default loans as it becomes available.  PointPredictive analyzes and combines information on a dealer-by-dealer basis across the consortium and provides each participating DealerTrace lender with a rank-ordered list of its highest risk dealers every month.  A lender may also register for enhanced alerting to be notified when information about one of its dealers is provided by another DealerTrace lender.

DealerTrace leverages the underlying pattern-recognition technology of the fraud and early payment default detection analytics of Auto Fraud Manager to create dealer-centric risk assessments based on lender-reported application and outcome data.  Fuzzy-matching techniques are used to recognize the same dealer – perhaps operating under different names or with slightly different demographic characteristics – across multiple participating lenders.  This allows DealerTrace to form a cross-lender consortium-level view of each dealer’s risk characteristics that is beyond what any individual dealer could do on its own.  It also enables participating lenders to benchmark their book of loans with a dealer against the consortium’s view of that dealer – this is very useful to detect instances where a dealer may be routing its riskier loans to certain lenders.

“We are excited to release this updated and improved version of DealerTrace as the second solution offering for our Auto Fraud Consortium members,” stated Tim Grace, Chief Executive Officer of PointPredictive. “By pooling data at an industry level, PointPredictive helps lenders aggregate their fraud knowledge, identify new fraud patterns more quickly, and collaborate to reduce fraud losses throughout the auto lending lifecycle.”

To learn more about DealerTrace or the Auto Fraud Consortium, contact [email protected].


San Diego Business Journal – Company Uses AI To Stay Ahead of Loan Fraud

San Diego Business Journal Article — Outstanding auto loans in the United States last year topped a record $1 trillion.

San Diego startup PointPredictive Inc. aims to help auto lenders keep more of the revenue they expect to earn from those loans by identifying potentially fraudulent applications before automobiles leave dealers’ lots.

The predictive analytics company, founded in 2014 by CEO Tim Grace, chief fraud strategist Frank McKenna and head of strategic alliances Joe Jackson, sorts through vast amounts of data from lenders to determine which applications have signs of fraud. Using machine learning techniques — a branch of artificial intelligence — the company’s models adapt over time as more data is gathered and analyzed.

Today, the company says it works with five of the top 10 and 17 of the top 30 U.S. auto lenders, as ranked by the number of applications they receive. The companies share historical data with PointPredictive, which uses the information to teach its software to recognize signs of potential misdoings by loan applicants or dealers.

“You hit one spot and they pop up somewhere else. If you cover one gap, they’re going to cheat a new way,” McKenna said. “That’s why they need models that can learn the new patterns.”

Neural Networks

San Diego is a hotbed for predictive analytics talent thanks to the company to which PointPredictive traces its roots. Hecht-Nielsen Neurocomputer Corp., later HNC Software, was launched in 1986 by University of California, San Diego professor Robert Hecht-Nielsen.

HNC developed software that used neural networks — computer systems inspired by the human brain — to help spot fraudulent credit card transactions. Grace and McKenna were among its employees. Another HNC alum, Greg Gancarz, is PointPredictive’s head of analytics and scoring.

In 2002, HNC was snapped up by Fair Isaac Co. (the company behind FICO credit scores) for $810 million.



PointPredictive Launches Auto Fraud Manager

PointPredictive Inc. announced today the launch of Auto Fraud Manager – a real-time predictive pattern recognition scoring solution providing a bundled suite of predictive scores that can instantly assess the risk of First and Early Payment Default, Fraud Misrepresentation and Dealer Risk on each automotive loan application so lenders can stop fraud before underwriting and funding a loan that will likely lead to a loss.

With the launch of Auto Fraud Manager, PointPredictive is addressing the growing problem of auto lending fraud which they currently estimate will reach nearly $6 billion in 2017.    PointPredictive research indicates that most of that fraud risk is hidden in losses categorized as First Payment Default or Early Payment Default – terms that lenders use to indicate loans where the borrowers fail to make the first payment or stop making payments within the first six months of the loan, respectively.

“Our research indicates that as many as 70% of auto loans that default without a single payment have some material misrepresentation in the application,” says Frank McKenna, PointPredictive’s Chief Strategy Officer.  “The overwhelming majority of those defaulted loans with material misrepresentation are submitted by a small fraction of the auto dealers a lender works with.  As few as 3% of a lender’s dealers represent 100% of the risk on some portfolios we have examined.”

Auto Fraud Manager is unique in that it identifies three distinct risks – Fraud Risk, First/Early Payment Default Risk and Dealer Risk – in a single solution so that lenders can make an informed lending decision in real-time while the loan application is being processed and before the car leaves the dealership.  Current solutions for verifying employment or verifying income are often not available in real-time, are not able to provide lenders with instant assessments, and often result in high false positives, poor customer experiences, and costly underwriting times.  Auto Fraud Manager provides an instant (real-time) full application assessment that shows lenders the probable material misrepresentations on loan applications that will likely lead to a loss.  The lender determines their risk tolerance levels and can accept, reject, or order verification solutions depending on their specific loan and dealer risk levels.

As lenders receive applications from a dealer, Auto Fraud Manager scans the application instantly for patterns of fraud risk such as income manipulation, employment fabrication, evidence of straw borrower, collateral inflation, fraud ring activity, and dealer fraud as well as likelihood of first or early payment default.   The lender can use that information to take proactive steps to stop the fraud.   Recent tests indicate that Auto Fraud Manager can reduce risk losses at lenders by 50% or more and improve their dealer performance by more accurately targeting their riskiest loan providers early, before losses are incurred.

Auto Fraud Manager scoring models leverage a consortium approach to learn patterns of fraud and risk across the automotive industry – spanning millions of loans and a variety of lenders.  PointPredictive uses specialized machine learning and pattern recognition algorithms that have been proven successful in fraud and risk solutions for the financial services industry.  Payment cards, mortgage lending, telecommunication, and insurance are already using similar solutions to reduce their fraud losses by 50% or more. For auto lending, we can show that this approach detects the highest percentage of risky loans at the lowest false positives to provide the highest dollar loss reductions and the best customer experience.

“We’re seeing great results from Auto Fraud Manager,” adds Tim Grace, CEO of PointPredictive. “In retrospective tests on lender-provided historical loan data, we have shown that if Auto Fraud Manager had been used at application time, the lenders could have realized 50% reductions in fraud losses at very low false positive rates.  In addition, many lenders are taking advantage of our ability to score large numbers of loans quickly and sending us the last two years of their auto loan applications.  We score these loans and help them identify which loans lead to losses that we would have identified before funding and which loans are classified as credit losses in their portfolio but are actually instances of fraud or misrepresentation.”

For further information on Auto Fraud Manager (including retrospective portfolio scoring or production pilots), contact Kathleen Waid at [email protected]


Auto Fraud Will Reach $6 Billion in 2017

SAN DIEGO, Feb. 02, 2017 (GLOBE NEWSWIRE) — PointPredictive announced today the publication of a white paper detailing new analysis confirming that auto lending fraud risk has been rising for several years, but remains hidden in credit losses. With 2016 auto lending originations soaring to historically high levels, the downstream impacts are now revealing themselves in higher fraud losses. PointPredictive estimates the annual value of auto loan originations that contain some element of misrepresentation may be as high as $6 billion in 2017, which is twice as much as 2016 estimates.

The white paper, based on analysis of historical loan performance across several different portfolios, reveals that auto lending fraud can be broken into three separate categories: known fraud that lenders have been able to identify, hidden fraud that ends up misclassified as early or first payment default, and systemic fraud perpetrated by unscrupulous car dealers or representatives at car dealers.  Each of these categories is a significant contributor to the increasing fraud losses that we are forecasting for the auto lending industry in 2017.

“Our analysis revealed that hidden fraud is the largest category of auto fraud risk as it is often mistakenly categorized with all of the other credit losses,” says Frank McKenna, Chief Strategy Officer of PointPredictive.  “Early Payment Defaults range between 1 percent and 3 percent of originated loans for a typical auto lender.  We are finding that up to 70 percent of those loans that default on the first payment or within the first six months after funding have fraudulent misrepresentation in the original application.  This is a primary contributor to the increase in auto lending fraud risk we are forecasting for 2017.”

Misrepresentation on the application of a borrower’s identity, income, or employment, as well as other key factors such as the price or condition of the vehicle, has a material impact on the performance of a loan.

“Credit scores or credit policies cannot be relied on to identify or prevent these losses, and identity scores only identify a small percentage of these types of misrepresentation losses.  A predictive, full application fraud score is necessary to allow the lender to prevent a significant percentage these losses,” adds Tim Grace, CEO of PointPredictive.

The white paper also provides insight into the role that car dealers play in various ‘systemic fraud’ schemes.  For most lenders, less than 3 percent of dealers are providing loans that are responsible for 100 percent of their known fraud and early payment default risk.  Often, the frauds at these dealers can be traced to a rogue finance manager or other key employee embedded in the finance office that works with fraud rings or identity thieves to facilitate the delivery of fraudulent applications to lenders.  On the positive side, more than 97 percent of car dealers represent very low risk to lenders as they have never been associated with a single instance of fraud.

To receive a copy of the white paper contact Kathleen Waid at [email protected].


PointPredictive Releases Mortgage Fraud Score

PointPredictive Inc. announced today the launch of a new mortgage fraud score that provides an enhanced, real-time assessment of misrepresentation risk allowing lenders to reduce the number of applications currently selected for detailed fraud reviews by investigators.

This score should be requested in parallel with, and used in combination with, credit scores to ensure that the lender’s underwriting policies are informed by the likelihood of misrepresentation early in the loan process.  PointPredictive research confirms that in the absence of a fraud and misrepresentation score, credit scores alone cannot accurately predict repayment of all loans.

Most mortgage lenders have processes that are bogged down by reviewing inaccurate and unnecessary fraud alerts after much of the credit underwriting has been completed.  This slows down the lending approval process and ultimately does not deliver the consumer experience or the loss mitigation lenders want.  The service launched today helps to solve that problem by reducing fraud-related false positives by at least 40% while providing enhanced real-time targeting of misrepresentation risk that would lead to future financial losses due to default and repurchase.

“When we started PointPredictive, our vision was to leverage new predictive modeling techniques to help industries evolve,” says Joe Jackson, Head of Partner Relations.  “Mortgage lending is one of our key markets because while there is so much data available during the lending process, it is not used optimally to identify fraud risk instantly so it can be resolved.”

This new cloud-based, predictive scoring service relies on information readily available at the time of the application and can score more than three thousand (3,000) applications per second.  The mortgage lender receives real-time notification of any potential misrepresentation along with an indication of what types of information will need to be validated with the borrower.

These features allow lenders to insert this service directly in their time-sensitive processing flows, streamlining most loans for faster approval while only flagging the most risky loans for costly and slower fraud prevention efforts.

“Most mortgage applications do not have a high risk of default or repurchase due to applicant misrepresentation and their processing path can be streamlined.  Our new model provides an objective and statistically sound way to help lenders do that,” says Tim Grace, CEO of PointPredictive.   “We use sophisticated machine learning models that provide lenders a “Go / No-Go” decision about misrepresentation review in milliseconds.  With the launch of programs by many leading lenders designed to simplify and expedite the customer’s application processing experience, this new scoring service is now critical for the mortgage industry.”

PointPredictive is offering a proof of concept evaluations to lenders to demonstrate the new model’s effectiveness.  As part of the proof of concept, a lender can score historical applications (with known outcomes) to understand the model’s accuracy in identifying fraud risks that led to a financial loss.  Given the speed of the scoring service, we can review one to two years of a lender’s full application volumes very quickly. PointPredictive will also provide participating lenders with a detailed analysis of the statistical soundness of the model including review rates, detection rates and false positive rates of the model.

If you would like more information about this new predictive service or the proof of concept program, please contact [email protected]


Joel Bock, Highly Accomplished Fraud Data Scientist, Joins PointPredictive

When it comes to fighting fraud with data science, Joel Bock is one of the best.   And this week, Joel made the decision to join our team of Ph.D. educated fraud scientist working in our offices here in beautiful San Diego.

Most recently Joel spent the last several years tackling difficult fraud problems like Tax Return Fraud which skyrocketed to over $8 billion annually starting in 2013.

By leveraging neural network modeling and gradient boosting techniques on hundreds of millions of tax returns, Joel was able to create sophisticated detection models which were able to pick off fraudulent tax returns like a sniper.    With over 250 million tax returns being filed each and every year, that’s the type of thing that can have a big impact real quick.

He must have done something right as tax return fraud is down a whopping 50% in the last two years.

Working With Business

Joel chose PointPredictive because of the unique working environment where business consultants work hand in hand with fraud data scientist to build better models.

Fraud Models that are built as a tight collaboration between business and science always deliver better results with higher fraud detection and lower false positives.


Getting to Work

Joel is hitting the ground running and will be initially applying his fraud fighting skills by boosting our auto lending model performance and helping us fine tune new models that will be announced in the next month.

It’s great to have you on board Joel!


PointPredictive Launches Fraud Model Validation Service for Mortgage

SAN DIEGO, CA–(Marketwired – January 06, 2017) – PointPredictive, Inc. today launched its new Independent Model Validation and Risk Review service to help lenders comply with the Office of the Comptroller of the Currency (OCC) SR 11-7 Guidance on Model Risk Management, a guidance requiring banking organizations to evaluate the soundness of their risk models and monitor models over time to ensure they are performing as intended.

The new service delivers independent validation of current risk models on an annual basis and provides lenders a service to test multiple vendor models side-by-side to determine which model or combination of models works best on their lending portfolio to mitigate losses. The service focuses primarily on model-based Fraud Solutions and Automated Valuation Models (AVMs) that are widely used by lenders today.

“Our experience in modeling is extensive and spans the mortgage, automotive, and banking industries. Our scientists and fraud experts have built sophisticated machine learning models and garnered leading market share in detecting mortgage application fraud, mortgage backed securities fraud, and credit/debit card fraud as well as automotive application fraud, early payment default risk and dealer fraud. This puts us in a unique position to help lenders validate their current models,” says Tim Grace, CEO of PointPredictive. “It can be a daunting task for lenders to validate their models annually, and many do not have the experience in fraud modeling to understand how well a model is working. Now, they can cost-effectively outsource that task to our fraud experts and PhD scientists.”

The service provides lenders with three components:

1. Annual Validation of Mortgage Fraud Models and AVMs — Assist lenders in confirming that the models are appropriately implemented and are being used and performing as intended by using historical data and measuring the statistical soundness of the models.

2. Benchmark Model Testing — Assist lenders during vendor evaluations by analyzing model results, benchmarking those results and providing an independent assessment as to which model(s) or combination of models perform best and are statistically sound.

3. Gap Validation — A business review by PointPredictive fraud and business consultants to validate that the models are being used optimally within their operations area(s).

“The OCC guidance indicates that model validation should be conducted independently by someone not having a vested interest in whether a model is determined to be valid,” explains Joe Jackson, Head of Partner Relations for PointPredictive. “As a proven trusted provider, we are being approached by lenders to do this validation because we are unbiased and our cross-industry visibility enables us to recommend approaches to operationalize the outcomes.”

The Model Validation Service is available immediately to the mortgage industry and inquiries can be made to Kathleen Waid at [email protected].


PointPredictive Finds Fraud in Auto and Mortgage Lending Follow Similar Risk Patterns

PointPredictive, Inc. a leading provider of machine learning fraud solutions, today announced the results of new study that determined high levels of fraud on early payment auto loan defaults. The study found scoring applications for auto loans with models that were built to detect fraud resulted in lenders finding 50% more early default than traditional credit scores.

In 2007, BasePoint Analytics™ found that between 30% and 70% of mortgage loans that defaulted within the first six months contained serious misrepresentations on the original application. These misrepresentations on borrowers income, employment, collateral or even intent to occupy had a material impact on the performance of the loan but were often considered “hidden fraud” since they were never detected in the application process.

“Our analysis and experience suggest that many auto loans that default within the first six months have fraud misrepresentation on the loan application, indicates Tim Grace, CEO at PointPredictive, “when we ran our fraud pattern recognition models on the application information provided on loans that defaulted early, the models were finding strong evidence of fraud. This is the same type of behavior we saw in mortgage prior to the mortgage meltdown”

PointPredictive Auto Fraud Models analyze each application and alert lenders when it appears that there might be misrepresentation on the application related to the income, employment, collateral, borrower or dealer. While built to detect fraud, scientist were surprised to find that it did extraordinarily well in the detection of early payment default (auto loans that default within the first 6 months). In lender tests covering 1.4 million applications and 22,500 deals, the PointPredictive Fraud Score was able to detect 14x more of the total fraud experienced by the lenders than previously used tools and processes. The early payment default (EPD) score identified 4x more first pay defaults and the Dealer Score identified 2x more suspicious dealers.

Auto lending fraud, like mortgage fraud occurs when information on an auto loan application is intentionally misrepresented either by the borrower themselves, a sophisticated fraud ring, or in some cases an unscrupulous dealer. When information is manipulated and the lender does not know about it

they may underwrite the application assuming the information is valid. Intentional fraud presents a problem to auto lenders since loans that have misrepresentation are more likely to result in – early payment default – a term lenders use to indicate when no payments are ever made on the loan.

PointPredictive Auto Fraud Manager uses pattern recognition; a technique that scientist have perfected to detect fraud based on historical data mining. The solution works by analyzing historical patterns of fraud, early payment default and risky dealer activity and then scores each application as it comes in from a dealer. Lenders are automatically alerted when an individual application has a significant number of application anomalies or fraud patterns. The lender can review the application and take action before it is approved. Over time, if a particular dealer submits many applications with similar fraud patterns, the solution will alert them to that as well so they can take the appropriate action.

As part of the study, PointPredictive has published a whitepaper on the subject titled, “ You Can’t Fight Fraud with Credit Risk Tools”. The whitepaper is available by emailing [email protected]


PPI Real-Time Auto Lending Fraud Scoring Service Launched This Week

Auto Lending Fraud losses continue to creep up in the US as auto lenders struggle to maintain loan quality as volumes increase.  Much of that increase has come in the area of subprime and deep subprime which now account for about 20% of all new loans according to Experian Q2 2016

PointPredictive launched Auto Fraud Manager, a fraud scoring service that can detect fraudulent applications based on red flags within the application, the dealer as well as red flags and fraud indicators from a fraud consortium run by PointPredictive.

This week, I am proud to announce that we launched a realtime enabled hosted scoring service at PPI.  With this scoring service we greatly simplify the integration of the scoring service for lenders that want to test or integrate the scores into their loan origination platform.   The scoring service automates the pulling of application data into the model, scores the transaction sub-second and returns the result to the clients.  Within less than a second, PPI can evaluate hundreds of risk elements on an application and provide a fraud score which indicates the level of risk of an auto lender or any application that they submit.

If you would like more information, please contact Frank McKenna at [email protected]


Frank on Fraud

If you want to check out my personal fighting fraud blog and read stories from the various industries I consult with, you can check out my fraud blog here – FrankonFraud

I believe that fraud globally is at its worst point in history so I like to use the blog to highlight people, technologies and business practices based on my day to day consulting to risk management folks across the world.

Check it out and thanks for reading.



How We Help Auto Lenders Comply with Dealer Monitoring Regulations

I read an interesting article from Christine Pratt at AITE which was very interesting. You can read the article here – Are Indirect Auto Lenders the Real Target of the FDIC’s New Third-Party Guidance?

Something which we have known for years and now happening, is more regulation for Indirect Auto Lenders and the dealers that they work with.  The government has required mortgage lenders monitor their third party lenders and brokers for years.  We should know.  Many of the staff in PointPredictive were responsible for launching the broker and third party lenders monitoring tools in the mortgage industry.

 The FDIC is Honing in 3rd Party Monitoring

The Consumer Financial Protection Bureau’s has been pushing for more 3rd Party monitoring in the auto lending industry.  As Christine Pratt indicates, “The new FDIC requirements are heavily weighted toward the complete investigation of any third parties’ financial performance as well as ongoing monitoring requirements and will place serious constraints on lenders’ resources. It is incumbent on indirect auto FI lenders to very carefully examine this new guidance and be mindful of a very tight timeline for comments. The original comment date of September 12, 2016 has been extended to October 27, 2016—and right now there are no comments on the FDIC site. This should be an immediate call to action for indirect auto lenders, auto dealers, and the groups that work closely with them (American Bankers Association, National Auto Dealers Association, and the like).”

PointPredictive Offers the Industry’s Only Auto Dealer Risk Monitoring Network

PointPredictive provides the industries first comprehensive tool to help Auto Lenders comply with their 3rd Party Monitoring and risk management.

We began collecting information on the vast network of 40,000 auto dealers in the US to build monitoring tools which can dynamically help lenders understand the fraud, credit and performance risks of dealers across the industry.

From our experience, less than 10% of auto dealers represent the majority of the fraud and early payment default risks for auto lenders. Our Dealer Monitoring Solution provides auto lenders with an early warning system to identify those very few bad apples in the bunch so they can improve their overall loan quality.”

Dynamic Monitoring Updated Each and Every Transaction

As part of the Dealer Monitoring Solution PointPredictive leverages proprietary technology which update s for each and every transaction that a dealer submits to the system.     The dynamic monitoring ensures that good dealers are not penalized, and dealers that are risky are identified at low false positives.

Auto Fraud Network Alerts

As part of Dealer Monitoring, the solution provides alerts from the network about prior fraud experience.  We know from our analysis of 3rd Party Monitoring, the ability for lenders to share their information on fraud risk is critical.  The mortgage industry has been doing it for years, it’s time the auto lending industry had the same types of tools and alerting programs.

Portfolio and Application Level Analysis

As part of the Dealer Monitoring Solution, PointPredictive offers the capability of retro-scoring entire portfolios to mine for the level of outstanding risk.

We often find that the first retrospective analysis is capable of identifying the most serious and risky dealers on the books with a lender.

The retrospective scoring can also help identify underlying loans that represent the greatest risk of default if the lender is servicing the portfolio.

Hosted Service for Rapid Setup

Lenders that are short on time or that require an immediate setup of the service can be setup relatively quickly at PointPredictive through batch hosted dealer monitoring platform.   This helps lenders that are short on time for compliance become compliant very quickly.

Thanks for Reading.

Thank you for reading and if you would like to reach us, contact us at [email protected]


Anatomy of an Auto Lending Fraud


Auto Lending Fraud cost US finance companies, banks and lenders billions annually.  But I often get the question, “How do lenders lose money when they can actually repossess the car if they find fraud later.  The answer is, it’s often not that simple and even when the car is repossessed, they can still lose money.

Early Payment Default

Banks can lose money due to bad loans.  Most lenders lose money when loans default within the first year.  This is called Early Payment Default and it can run as high as 2% of loans in certain segments of lending like subprime, and even higher in deep subprime.   PointPredictive analysis suggest that as much as 30% of these loans contain some fraudulent misrepresentation in them.  The lenders can repossess these cars but they rarely recover 100% of the value and end up taking large losses.

Fraud Rings

Banks can lose money to outright fraud on the loan file by individuals or fraud rings.   These are often very dangerous fraud schemes where luxury cars are often sent to foreign countries in shipping containers after the purchase.  The lenders cannot repossess these cars because they essentially disappear.

Dealer Fraud

The overwhelming majority of auto dealers are good businesses.  However there are a few bad apples that can give dealers a bad name.  PointPredictive analysis suggest a small minority – approximately 3% represent real risk of fraud to a lender.  Common dealer frauds are often linked to recruiting straw borrowers, inflating the collateral value of the car or faking entire loan applications to steal from lenders and banks

This is typically the type of fraud that you will read about in the newspapers because dealer fraud is pretty common and when it happens can cost lenders millions.

Anatomy of an Auto Lending Fraud

To understand Auto Lending Fraud we don’t have to look any further than a recent case at Navy Federal Credit Union.   Navy Federal Credit Union was recently scammed out of $1.1 million in loans by a shady car dealer – Andysheh  Ayatollahi, who eventually  fled to Iran after perpetrating a massive scam against lender he worked with.

Lets look at the steps that Andysheh took to perpetrate his fraud scheme

Step 1 – Buy a Dealership

The first step was to buy a dealership and Andysheh did just that by purchasing a 50% stake in Car Store, a used car dealership in Virginia Beach. He purchased the stake in the Car Store from Reza Azizkhani who would become his partner in crime.  He paid $350,000 for that 50% ownership.  That was back in 2007 and at the time the dealership was operating above board.  But all that was about to change.

Step 2 – Hire Collusive Finance Managers 

It takes a village to pull off the perfect auto lending fraud scam and  Reza and Andysheh were determined to pull it off. To make it really work they needed finance managers  and sales people that would do whatever they told them even if that meant lying to banks, , creating fake documents to back up those lies and shredding those documents if the police ever caught on.

Step 3 – Get Luxury Cars

If you’re going to lie, lie big.  And that is what the Car Store did.  To fill their lots,they didn’t want the cheap economy cars, they wanted the luxury cars that could net them big money.  So they filled their lot with luxury cars like Jaguars, BMW’s, Lincoln Navigators and Mercedes Benz.

The bigger the car, meant the bigger the loan that they could get and that meant more money that they could put in their own pockets.

Step 4 – Get Borrowers – Straw Borrowers

The 4th step in their scheme was to get borrowers that were easily fooled.   They recruited desperate or needy people and turned them into “Straw Borrowers”. These were people with very poor credit and limited financial savvy that could be easily manipulated.  These were people that would let the sales person and finance manager put whatever they wanted on the application without questioning why.

Step 5 – Find a Target

To perpetrate a fraud, they needed to find a target bank that they understood very well.  A bank where they knew the processes, procedures and policies for getting a loan approved.  They set their sights on Navy Federal Credit Union as the target.    This way they could tailor their loan applications in a way that they knew that they could get them approved.

Step 6 – Move Product, Write Fraudulent Loans

Now that everything was in place, Reza and Andysheh were ready to begin the scam.  Starting in July of 2007, they began to dramatically increase their sales. They recruited straw borrowers and started selling cars with one small catch – the loans were far higher than the purchase price and all the information in the loans was fake.

The scam was to inflate the car price

The typical scam that they pulled was to run multiple fictitious applications through with fraudulent information to find the maximum that Navy Federal Credit Union with authorize as a loan.  Then they would inflate the value of the car they were selling to match that maximum amount the bank would lend.  In exchange for the straw borrowers complicity, Car Store would split the proceeds of the excess loan amount with them.  Everyone would win.  Except the bank of course.

Inflate Borrowers Income and Create Fictitious Documents

In mid 2007, for example they sold a 2002 BMW to an employee of a barber shop.  The borrower had poor credit and made a modest salary.  That didn’t stop Car Store from helping they buyer secure a loan on that car for $60,000 dollars.    Under the direction of Andysheh, the Finance Manager falsified paystubs, income and other documentation to show that the borrower actually had two jobs with income of over 8,000 a month.

And that was just the tip of iceberg.  Andysheh and the Car Store would go to any length to get a loan approved. The would even go so far as creating fake paystubs, earning statements or calling the bank to impersonate employers to verify income of the straw borrowers.

The losses racked up quickly

By mid 2008, the Car Store had racked up over 60 loans to Navy Federal Credit Union totally over 1.1 million dollars.  Many of the early loans were starting to go default and the lender was catching on.

As the Feds caught on, Andysheh realized he was in trouble and that is when he started to try to cover his tracks.  He instructed employees to start to destroy all the evidence and had the Finance Manager stay after hours and on the weekends to shed any and all paperwork.

When it was all said and done, Andysheh fled the country to Iran to avoid facing criminal charges.  He later returned late last year to face those charges and faces 27 months in Federal prison.

The Cost of the Fraud

In the end Navy Federal Credit Union and Insurance Companies lost $867, 388.  Close to 90% of the loan value that they had loaned went into loss.  The cost of auto lending fraud doesn’t stop there.  Consumers ultimately bear the burden of these losses in the form of paying higher interest on future loans.

Auto Lending fraud is not a small problem but a growing one here in the US.  The auto lending industry is booming and lending more to more risky customers than anytime in our history.  This is often a precursor of future risk that will be exposed.

New Behavioral Analytics Track Suspicious Behavior

To curtail fraud, lenders should have systems in place to monitor the behaviors of all dealers and the applications that those lenders submit.  PointPredictive offers the industries first Pattern Recognition algorithms to help lenders stop dealer frauds.

Contact [email protected] for more information.


Estimating Auto Fraud Lending Losses in the United States


Auto Lending fraud and risk losses are on the rise in the US.   The numbers are out and they are not good.  Last quarter uncollectible auto loans soared to over $1.1 Billion dollars and delinquencies on subprime loans hit their highest level since the worst recession in US history.  More concerning is that the rapid growth in auto lending is being fueled in part by deep subprime lending to borrowers with credit scores less than 500.

With auto lending origination levels soaring to some of their highest levels in history the downstream impacts are beginning to reveal itself with concerning levels of fraud.

As former experts in Mortgage Fraud Analytics,  PointPredictive is beginning to see history repeat itself – but this time in the Auto Industry.  In 2004, Mortgage lending volumes were hitting new heights thanks to an increase in subprime and non-direct lending through brokers.  Fast forward 12 years later and the Auto industry is experiencing the same types of rapid growth which is fueling the same sparks of fraud risk that erupted in the mortgage industry.

The Hidden Cost of Auto Lending Fraud are in 3 Areas

There is no central reporting agency for US Auto Lending Fraud so assessing the absolute levels of fraud losses in the US is difficult. The cost of Auto Lending Fraud does hit the bottom line of banks and lenders but it may not always be categorized or recognized as a fraud loss.

Auto lending fraud losses are typically seen in the following areas:

1) Early Payment Default –  Loans that default within the first 6 months have a much higher probability of containing material misrepresentations in the original loan application than loans that default much later.  PointPredictive believes that between 30% to 40% of early payment defaults in the auto lending industry can be directly linked back to some type of fraud misrepresentation.

2) Dealer Losses –  Auto Lenders, particularly in subprime lending experience high levels of risk based losses that can be tied back to individual dealers.  Some dealers have extremely high levels of early payment default, known fraud and bad loan quality that leads to losses.

PointPredictive analysis has determined that based on the lender, that almost 100% of their fraud losses may be coming from less than 3% of their dealers and close to 100% of their early payment default losses may be coming from just 10% of their dealers.

Dealer losses may not always be categorized as fraud, however through careful analysis lenders often determine that many of the losses they take are due to intentional misrepresentation at an industrial scale.

3) Fraud Losses – Most auto lenders do have some tracking in place for their fraud losses however it may not always reflect all of their losses since so much fraud is often hidden.  Identity Theft, Straw Borrower, Collateral and Dealer Fraud often top the categories of losses that lenders take on for fraud.

US Auto Lending Fraud Losses are in the Billions

Based on data analysis and industry studies, PointPredictive believes that auto lending fraud loan originations are between $2 billion to $3 billion annually based on the current origination volumes of $600 billion dollars.

auto fraud cost lenders billions

This conservative estimate assumes that approximately 30% of early payment default losses in the US contain material misrepresentation and that identified fraud is running at approximately 20 basis points of origination volume.

By applying conservative estimates to the origination volumes, it is clear that auto lending fraud is not a small problem but one that cost the auto lending industry potentially billions  a year.     Additionally, with loan quality (particularly in subprime) eroding quickly PointPredictive believes that auto lending fraud losses and rates could rise dramatically in the next 18 months.

Applying Analytic Models to Assess Risk

PointPredictive applies analytic pattern recognition models which helps auto lenders determine the latent levels of fraud and risk sitting in their servicing portfolios.  By analyzing application and loan level information, PointPredictive estimates which applications and loans are most likely to contain fraud and misrepresentation which helps lenders and banks understand how much risks is sitting on their portfolios

Additionally PointPredictive deploys these same models with auto lenders to identify risky loans prior to approval so underwriters can take the necessary actions to stop the fraud and risk before it is approved.

For questions, contact [email protected]


PointPredictive is growing and looking for data scientists to join the team

PointPredictive Inc. is looking for data scientists to join our rapidly growing fraud and risk analytics team in sunny San Diego California.

At PointPredictive you will work with us solving complex fraud and risk problems for the financial services industry on emerging and growing areas of fraud risk.

What our Data Scientist Do At PointPredictive

Create predictive models based on big data sets using a variety of tools and algorithms in the cloud

Innovate in fraud and risk modeling by testing new statistical modeling techniques on data to boost predictive performance

Collaborate with our customers to analyze, mine, and aggregate their data into our fraud and risk data consortiums

Creatively solve fraud and risk problems in auto lending, mortgage, check, mobile, and electronic payments using internal and external data sources

Evaluate new data sets to determine their ability to boost detection performance

Collaborate with Fraud Consultants and others in the company to develop and launch new, innovative predictive analytic products to the market

Deliver recommendations to clients and the industry on how to best apply advanced analytics

Join our Team in San Diego

Our offices are open plan and modern and San Diego offers the best quality of life of any city in America.

Successful candidates for the position will have a Ph.D. (or a master’s degree with statistical modeling experience) in a technical field, preferably at least 2-4 years of experience working with data in industry, facility with modern programming languages (e.g. Python) in a Linux environment, familiarity with machine learning techniques (such as neural networks, random forests, deep learning), fluency with statistical and modeling packages such as R and ADAPA, and the proven ability to quickly build and deliver production ready statistical models to clients.

Successful candidates for the position will have a Ph.D. (or a master’s degree with statistical modeling experience) in a technical field, preferably at least 2-4 years of experience working with data in industry, facility with modern programming languages (e.g. Python) in a Linux environment, familiarity with machine learning techniques (such as neural networks, random forests, deep learning), fluency with statistical and modeling packages such as R and ADAPA, and the proven ability to quickly build and deliver production ready statistical models to clients.

We move fast at PointPredictive, so if this position sounds like an attractive or potential match for you, please email your CV or profile link today to [email protected]


The Hidden Risk of Auto Lending Fraud Exposed


Auto lending fraud doesn’t get a lot of attention.  You don’t read about it daily.  It doesn’t make front page news.  In fact most people probably don’t even know what it means.  Well, the same was true of the mortgage industry 10 years ago and we learned some valuable lessons.  Auto Lending fraud will become news someday and it’s important we get ahead of the curve and do things to prevent that.

Fraud occurs when there is too much trust and not enough vigilance

There is a tendency for lenders across industries to rely too much on FICO scores and to trust information that is supplied by borrowers and third parties too much.  When this happens, the lenders can be exposed to more fraud and default risk than they were expecting.

What the Mortgage Industry Learned about fraud

In 2004, the mortgage industry was in full swing. The housing market was booming and mortgage originations were closing in on close to $3 trillion dollars annually.  Everything was seemingly going right, or was it.

Fundamentally things were about get very bad for lenders but they didn’t realize what was brewing.  In attempt to gain market share and keep up with the seemingly endless supply of people that wanted houses, lenders were expanding loan programs and layering risk in new ways that had never been tested.

In 2006, the cracks began to show – primarily in subprime lending and by 2008 the mortgage industry had completely melted down.  Loans were not performing and borrowers were defaulting at record levels.   So what happened to cause such a massive collapse.

1)  FICO scores were trusted too much – Lenders relied on FICO scores too much, however many borrowers with great credit scores were defaulting because FICO scores were primarily built on credit not mortgage data.

2) Lenders trusted the information from borrowers and brokers too much – The second problem that emerged was fraud.  Lenders trusted the information supplied by borrowers and mortgage brokers too much when in fact fraud was commonplace.   Studies indicated that 3% of mortgage brokers accounted for most of the fraud accounts and were systematically conning lenders into making bad loans.  The credit rating agency Fitch even did a study which indicated that 25% of the subprime mortgage loans that defaulted had fraud in the application.  Fraud hurt the mortgage industry dramatically.

3) Warnings of Fraud were Ignored – Lenders were in a race to get loan volume and ignored industry experts that warned of fraud.  It was commonly known that loan programs called Stated Income Programs were also called “Liar Loans” but lenders loaned on them anyway since they assumed they would perform.

4)  Over-reliance on the Collateral –  As housing prices soared property values were skyrocketing creating a market where homes became like ATM Cash machines.  When borrowers needed money they could simply refinance their houses and take the equity out.  Borrowers that could not afford their mortgage payments could simply “refinance” their way out of the problem.  Lenders were lulled into a false sense of security since the default rates were artificially low because of this natural re-aging process.

5) Hard and Fast Rules – Lenders attempted to stop the fraud problem by putting data checks and validations into place.  For example, lenders implemented processes to check pay stubs and borrowers social security numbers against public records databases.  There was a feeling that fraudsters would keep doing the same things that they had always done and that they could stop the fraud.  But this wasn’t the case, lenders relied too much on non-dynamic fraud tools and processes which were easily overcome by the clever fraudsters.

6) The Belief that Fraud Doesn’t Exist – Mortgage lenders rarely detected their fraud so they never reported it.  Because they never reported it, the believed that it never actually existed.  But they were wrong.  Fraud did exist and it was hidden in their early payment default losses.  Studies indicated while reported fraud losses were low, hidden fraud losses were in fact high.  Studies conducted in 2004 indicated that between 30% and 70% of early payment default losses had fraud in the original loan file.  Fraud was simply hidden in the lenders early payment default populations and they did not know about it.

The mortgage industry took these learnings and applied some really great technologies and processes to reduce fraud.  They began adopting fraud scores, pattern recognition technologies and fraud reporting.  They became experts in detecting fraud and they were successful.  As a result mortgage fraud losses dropped by 50% between 2008 and 2010 based on industry studies.

So What is Auto Lending Fraud?

Auto Lending fraud is unique but in many ways similar to industries such as mortgage.  There are about 8-10 primary types of auto lending fraud that impact lenders.

Income Fraud – Income Fraud is one of the most common types of auto lending fraud and it occurs when borrowers or borrowers coached by dealers inflate or outright lie about their income.

Employment Fraud – Employment fraud is commonplace in auto lending.  When borrowers lie about about their employer or employment status this is considered employment fraud and it impacts the performance of the loan.

Identity Theft – When borrowers use a social security number or identity that is not their own this results in identity theft and it has historically been a pretty sizable problem for auto lenders.  Since 2009, lenders have achieve far few losses by using external tools such as Lexis Nexis to verify identity and social security numbers.

Straw Borrower Fraud– The third most common type of fraud is straw borrower fraud.  This occurs when a borrower is either recruited by an unscrupulous buyer or broker to act as the purchaser of the car so that the real party can be hidden.  The straw borrower can be a family member or in some cases someone unknown to the actual borrower.  Straw borrowing is often confused with identity theft since the results are often the same.

Dealer Fraud  –  Dealer fraud may be less common, however the impacts are far greater.  Lenders report to PointPredictive that used car auto dealers often represent their highest risk factor when determining a loan.   Dealer fraud occurs when a dealer or finance manager systematically manipulates or coaches borrowers to misrepresent information on their applications.  Lenders report that less than 10% of their dealers represent an overwhelmingly majority of all of their fraud and early payment default risk.

Collateral “Stuffing” Fraud – Manipulation of the car value through a variety of methods including add-ons that never existed is fairly common with instances of auto lending fraud.  Since the collateral value dictates the amount of money that is leant on an automobile, collateral fraud is often one of the most damaging fraud types to lenders.

Excessive Dealer Markups – When dealers systematically markup loans against certain borrowers such as subprime or elderly borrowers this can be a big risk to lenders.  While it is not necessarily considered fraud it is oftentimes closely associated with fraudulent dealers.  Dealers with excessive markups may be more likely to be engaged in dealer related fraud.

Odometer Fraud – Odometer fraud which is has been declining is another example of fraud against lenders.  If odometers are rolled back the lenders are likely to value the collateral too highly and loan too much money on a vehicle.

Yo Yo Fraud – Yo Yo fraud occurs when a consumer is given temporary registration for a car and drives the car off the lot but the dealer later changes the terms of the sales contract forcing the buyer to accept the new terms.

What are the Cost of Auto Lending Fraud?

There are currently no industry reported fraud losses due to auto lending fraud.  But PointPredictive is analyzing the cost of fraud with lenders that are contributing to the auto fraud consortium.

There are several things that we are learning in our analysis.  We believe that auto lending fraud is running anywhere between 10 to 50 basis points of origination volume in fraud based on the portfolio and business practices.

Early Pay Default Losses are Linked to Fraud – There is some evidence to suggest that approximately 30% of early payment default losses may be related to fraud in the original loan file.  Loans that default within the first 90 days after origination and never cure have a very high correlation with fraud.

Less than 10% of Dealers Account for Most Risk –  Based on lenders surveyed and extensive data analysis, PointPredictive believe that between 3% and 10% of dealers represent an overwhelming majority of that lenders fraud and early pay default risk.  This finding is important because it follows closely with the experience of the mortgage lending industry with brokers.

Fraud Losses are Increasing with Subprime Boom – As the subprime lending industry booms, fraud losses are on the rise.  Experian reports that early pay default losses are rising to their highest levels since 2008.  PointPredictive believes that the eroding loan quality is partly due to increasing fraud on the application.

Exposing the Hidden Risk of Auto Lending Fraud

PointPredictive performs retroactive analysis to help lenders determine their fraud losses.  By using pattern recognition models built by trusted fraud scientist, PointPredictive is able to provide a lender with an independent view into their fraud risk exposure.  There are 3 primary ways PointPredictive provides the service

1) Application Fraud Scoring Model – Using millions of historic applications across lenders, we have created a sophisticated scoring algorithm that determines the probability that a loan contains material misrepresentation.  Each application in a portfolio can be scored and ranked according to risk.  PointPredictive than benchmarks the portfolio against other lenders in the consortium

 2) Default Probability Scoring Model – PointPredictive has also created a Default Probability Scoring Model which can determine the likelihood that an auto loan will default within the first 6 months.  By analyzing the early payment default population, a lender can determine if their fraud risk is also elevated.

3) Dealer Scorecard and Benchmark – Based on historical data from thousands of dealers nationwide, PointPredictive has created a predictive dealer scorecard which ranks each dealer according to their risk level.  The risk model scores each dealer every time a new loan is submitted so lenders can determine how to handle the dealer and the loans that they are submitting.

Best Practice Implementation

PointPredictive Fraud Consultants have worked with over 150 financial institutions and lenders worldwide.  By working hand in hand with lenders consultants have been able to reduce fraud losses and exposure significantly and in some cases have been able to save lenders in excess of $80 million dollars in fraud loss annually.

To reach us, please feel free to contact Frank McKenna at [email protected]

Thanks for reading!