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Most types of financial fraud are relatively straightforward: the fraudster uses creative accounting, inflated numbers, or out-and-out lies to trick their victim into handing over money or valuables they wouldnt otherwise part with, usually while twirling a villainous mustache. You can probably think of a dozen examples off the top of your head, from Bernie Madoffs Ponzi scheme to the phone scams that try to convince your Nana her Social Security benefits are in danger. But until allegations were recently brought against Federal Reserve governor Lisa Cook, most people had never heard of mortgage fraudand for good reason. This type of fraud is exceedingly rare. In 2021, only 58 mortgage fraud offenders were sentenced in the federal system, and the number of offenders has decreased by nearly 70% since 2017. Understanding what makes mortgage fraud such an uncommon financial crime can help clarify whats behind the recent allegationsand can make your own brushes with mortgage underwriting feel less opaque. Heres what you need to know. Defining mortgage fraud The specific type of mortgage fraud that Federal Reserve governor Cook (as well as New York Attorney General Letitia James and California Senator Adam Schiff) have been accused of is intentionally deceiving a mortgage lender or underwriter in order to secure a mortgage loan. There are several types of mortgage fraud that a borrower may engage in, including: Income fraud: When a buyer misrepresents their income to the mortgage lender. Straw buyer: When a fake buyer acts on behalf of a true borrower to misrepresent the transaction because the real borrower couldnt qualify for the loan. Typically, the straw buyer transfers the title to the property after the sale is done. Illegal property flip: When a buyer purchases a property at below market value and quickly resells it at an artificially inflated price. Although flipping a house for a nice profit is not illegal, if it involves a fraudulent appraisal or misleading the new buyer, it is considered mortgage fraud. Occupancy fraud: When the borrower lies about the occupancy status of the property to obtain a better rate, since owner-occupied primary residences receive more favorable terms than second homes or rental properties. Occupancy fraud is the type of fraud that Cook, James, and Schiff have been accused of, since the allegations against all three center on which of their properties are designated as primary residences. (Its important to note that Schiff claims he got permission from his lender to consider two homes as primary residences because of his need for a home base in both California and the D.C. area.) The high bar for mortgage underwriting Mortgage fraud is not so easy to pull off, as anyone who has been through mortgage underwriting can attest. This process puts the borrowers credit, income, and financial background under the microscope to determine if they are a good candidate for the loan. Specifically, mortgage underwriting looks at things like: employment records for the previous two years W-2 tax records for the past two years pay stubs for the past 30 to 60 days account information for every type of account you have, includingchecking and savings accountsCDsinvestment accountsretirement accounts that money market account you opened three years ago and forgot about additional income, like alimony, child support, bonuses a gift letter if friends or family have given you money to help with your down payment Underwriters have the tenacity of a bloodhound and will halt the process to ask for additional information about unexplained gaps in employment or funds that they consider unverified. (The card that Nana sent the $500 birthday check in may not be enough to satisfy your underwriter about the source of that unverified extra five Benjamins. Ask me how I know.) Depending on the lender, underwriting may also require the borrower to provide an intent to occupy letter as part of the process. This legal document offers proof that the borrower is purchasing the property as a primary residence, and works as a legal protection against occupancy fraud. Why is mortgage fraud different from all other fraud? With the exception of illegal property flipping, the most common types of mortgage fraud involve a borrower deceiving a mortgage lenderin order to borrow hundreds of thousands of dollars. This is not like tricking someone into giving you money and disappearing with it. The mortgage lender literally knows where the borrower lives. Additionally, if the borrower succeeds in deceiving the lender into a more favorable loan through mortgage fraud, the bank risks losing money if the borrower defaults, but the borrower faces higher risks. If they default, they will ruin their own credit. The lender can easily write off the loss of the money, especially since it still has the property as collateral, while the borrower will be in much worse financial straits after defaulting. This is not to say that mortgage fraud is some kind of victimless crime that can only hurt the borrower. But a type of fraud where you plan to make monthly payments to your victim is a noticeably different beast from simply illegally transferring money from victim to fraudster. Fraud or error? Fed Governor Cook has responded to the allegations that she fraudulently took out mortgages on two primary residencesone in Michigan and another in Georgiaby claiming she made no attempt to deceive anyone and that an unintentional error may be behind the problem. It is impossible to know for certain what happened in this situation. Mistakes certainly happen in mortgage paperwork. There is no specific definition of primary residence, which can vary from one state or county to another, and from one lender to another. And while mortgage records are public, they do not necessarily include all the information shared between borrowers and lenders. There is no way to prove or disprove fraudulent intent on Cooks part. Along with the fact that mortgage underwriting is very effective, the difficulty of proving mortgage fraud is why there are so few convictions. Yes, its possible that many borrowers are fibbing about their income or where they plan to live. (Donald Trump was famously found liable for fraud by a New York state judge for inflating his net worth in order to secure more favorabe loan and insurance terms.) Or they may have an agreement in place with their lenders. Or there may be a clerical error on their mortgage records. If they are making their mortgage payments on time, no one is paying much attention. Borrowing with an intent to defraud Mortgage fraud is a real financial crime where a home buyer deceives a lender in order to get more favorable loan terms. But its a difficult fraud to pull off because the mortgage underwriting process requires such attention to the borrowers financial situationalthough falsely claiming that a property will be a primary residence is probably one of the easiest types of mortgage fraud. While mortgage fraud is not a victimless crime we can simply accept from governors of the Federal Reservewho need to be above reproachits also unlike most other types of fraud. The fraudster is committing to years of monthly payments and faces financial ruin if they default. Lenders are in an excellent position to protect themselves from mortgage fraud through underwriting. The system appears to be working as intended, considering how difficult it is to identify or prove definitive cases of fraud. Which raises the questiondo we really need to examine the intent behind the borrowing irregularities that are currently in the news cycle?
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E-Commerce
Apple holds several events throughout the year, but none is as vital to the companys bottom line as its annual one in September. Thats when Apple unveils its new iPhone lineup, drawing our attention again to the device that accounted for just over half of the $391 billion the company brought in in fiscal 2024. Apples next iPhone event is scheduled to take place on Tuesday, September 9. The iPhone 17 event, taglined “Awe Dropping, represents a significant shift: Apple is expected to discontinue one of its current iPhone models and launch a new iPhone device in its place. The company is also likely to introduce several other hardware products. Heres what you can expect to see on Tuesday. iPhone 17 Air The star of the iPhone 17 event is expected to be a brand-new model of the iPhone, currently dubbed by tech pundits and analysts as the iPhone 17 Air (also sometimes referred to as the iPhone 17 Slim). As its name suggests, the iPhone 17 Air is expected to be a thin devicethe thinnest iPhone ever. Most rumors indicate that the iPhone 17 Air will have a thickness of between just 5.5mm and 6mm. (Its likely the 5.5mm thickness will be the device’s thinnest point, and the camera bump will add heft to the new iPhone near the top of the device.) Previously, the thinnest iPhone ever was the iPhone 6, released in 2014, measuring 6.9mm in thickness. (The thinnest iPhone 16 models are the iPhone 16, iPhone 16e, and iPhone 16 Pro, all of which have a thickness of 7.8mm.) In addition to a skinny form factor, the iPhone 17 Air is expected to feature a 6.6-inch display, an A19 chip, and a single-lens 48MP rear camera. The lack of a more advanced camera system suggests that the iPhone 17 Air will be for consumers who prioritize form and design over functionality. The new iPhone Air model will replace the current iPhone Plus model, which Apple is retiring this year. iPhone 17, iPhone 17 Pro, and iPhone 17 Pro Max Apple is also expected to upgrade the rest of its current iPhone lineup at the event on Tuesday. This includes the release of the iPhone 17, iPhone 17 Pro, and iPhone 17 Pro Maxsome of which may have a higher starting price than last year. If youre a fan of the entry-level iPhone, you may be disappointed to hear that the base iPhone 17 will likely get the fewest upgrades this year. Expect a display size bump from the current 6.1 inches to 6.3 inches. The iPhone 17 is also expected to get the A19 chip and a new 24MP front camera (up from the 12MP in the iPhone 16). But beyond thatand new colorsdont expect too many other significant feature improvements. The story is different when it comes to the iPhone 17 Pro and iPhone 17 Pro Max. Expect significant design changes, including a rectangular rear camera bump, a new aluminum frame, a hybrid aluminum and glass backplate, and a new orange color option. The iPhone 17 Pro is expected to feature a display size increase to 6.3 inches (from the current 6.1 inches), and the iPhone 17 Pro Max is expected to have a display size of 6.9 inches. Both models are also likely to feature the A19 Pro chipset, a 24MP front camera, and a new 48MP lens in their rear telephoto camera (up from 24MP). All iPhone 17 models will ship with the new iOS 26 and its Liquid Glass software design. Apple Watch Ultra 3 Last year, Apple Watch fans were disappointed to learn that Apple did not release an updated Apple Watch Ultra. Instead, the company kept the same Apple Watch Ultra 2 modelfrom 2023on sale, only adding a new color option. But on Tuesday, the company is finally expected to introduce a new Apple Watch Ultra 3. However, dont expect a radical overhaul of the high-end smartwatch. Instead, it is likely to get iterative hardware improvements. As noted by 9to5Mac, those improvements include a slightly larger display (the Ultra 2 has a 49mm display), a new S11 chip, high blood pressure detection, 5G cellular support, and satellite connectivity for messaging. Apple Watch Series 11 Apple is also expected to debut the new Apple Watch Series 11. However, those hoping for a larger display, like the one coming to the Apple Watch Ultra 3, will be disappointed. The Series 11 is expected to retain its current 42mm and 46mm options. Yet, the Apple Watch Series 11 is expected to gain many of the other features coming to the Apple Watch Ultra 3, including a new S11 chip, high blood pressure detection, and 5G cellular support. Apple Watch SE 3 The Apple Watch SE is Apples budget Apple Watch product. The current Apple Watch SE 2 came out nearly three years ago, so its long overdue for an upgrade, which is expected on Tuesday. However, as a budget model, dont expect the Apple Watch SE 3 to feature significant improvements. Rumors suggest that it could feature a slightly larger display and that Apple may have opted to use a plastic, color body to keep costs down. AirPods Pro 3 Finally, Apple is also expected to introduce an updated AirPods Pro 3. However, rumors about what new changes we can expect are more opaque than with the other Apple products listed above. Signs point to the new AirPods Pro 3 having a tweaked, possibly smaller, design, improved Active Noise Cancellation and audio quality, as well as a new heart rate monitoring health feature. Apples iPhone 17 event kicks off on Tuesday, September 9, at 10 a.m. PT/1 p.m. ET.
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E-Commerce
For farmers, every planting decision carries risks, and many of those risks are increasing with climate change. One of the most consequential is weather, which can damage crop yields and livelihoods. A delayed monsoon, for example, can force a rice farmer in South Asia to replant or switch crops altogether, losing both time and income. Access to reliable, timely weather forecasts can help farmers prepare for the weeks ahead, find the best time to plant or determine how much fertilizer will be needed, resulting in better crop yields and lower costs. Yet, in many low- and middle-income countries, accurate weather forecasts remain out of reach, limited by the high technology costs and infrastructure demands of traditional forecasting models. A new wave of AI-powered weather forecasting models has the potential to change that. By using artificial intelligence, these models can deliver accurate, localized predictions at a fraction of the computational cost of conventional physics-based models. This makes it possible for national meteorological agencies in developing countries to provide farmers with the timely, localized information about changing rainfall patterns that the farmers need. The challenge is getting this technology where its needed. Why AI forecasting matters now The physics-based weather prediction models used by major meteorological centers around the world are powerful but costly. They simulate atmospheric physics to forecast weather conditions ahead, but they require expensive computing infrastructure. The cost puts them out of reach for most developing countries. Moreover, these models have mainly been developed by and optimized for northern countries. They tend to focus on temperate, high-income regions and pay less attention to the tropics, where many low- and middle-income countries are located. A major shift in weather models began in 2022 as industry and university researchers developed deep learning models that could generate accurate short- and medium-range forecasts for locations around the globe up to two weeks ahead. These models worked at speeds several orders of magnitude faster than physics-based models, and they could run on laptops instead of supercomputers. Newer models, such as Pangu-Weather and GraphCast, have matched or even outperformed leading physics-based systems for some predictions, such as temperature. AI-driven models require dramatically less computing power than the traditional systems. While physics-based systems may need thousands of CPU hours to run a single forecast cycle, modern AI models can do so using a single GPU in minutes once the model has been trained. This is because the intensive part of the AI model training, which learns relationships in the climate from data, can use those learned relationships to produce a forecast without further extensive computationthats a major shortcut. In contrast, the physics-based models need to calculate the physics for each variable in each place and time for every forecast produced. While training these models from physics-based model data does require significant upfront investment, once the AI is trained, the model can generate large ensemble forecastssets of multiple forecast runsat a fraction of the computational cost of physics-based models. Even the expensive step of training an AI weather model shows considerable computational savings. One study found the early model FourCastNet could be trained in about an hour on a supercomputer. That made its time to presenting a forecast thousands of times faster than state-of-the-art, physics-based models. The result of all these advances: high-resolution forecasts globally within seconds on a single laptop or desktop computer. Research is also rapidly advancing to expand the use of AI for forecasts weeks to months ahead, which helps farmers in making planting choices. AI models are already being tested for improving extreme weather prediction, such as for extratropical cyclones and abnormal rainfall. Tailoring forecasts for real-world decisions While AI weather models offer impressive technical capabilities, they are not plug-and-play solutions. Their impact depends on how well they are calibrated to local weather, benchmarked against real-world agricultural conditions, and aligned with the actual decisions farmers need to make, such as what and when to plant, or when drought is likely. To unlock its full potential, AI forecasting must be connected to the people whose decisions its meant to guide. Thats why groups such as AIM for Scale, a collaboration we work with as researchers in public policy and sustainability, are helping governments to develop AI tools that meet real-world needs, including training users and tailoring forecasts to farmers needs. International development institutions and the World Meteorological Organization are also working to expand access to AI forecasting models in low- and middle-income countries. AI forecasts can be tailored to context-specific agricultural needs, such as identifying optimal planting windows, predicting dry spells, or planning pest management. Disseminating those forecasts through text messages, radio, extension agents or mobile apps can then help reach farmers who can benefit. This is especially true when the messages themselves are constantly tested and improved to ensure they meet the farmers needs. A recent study in India found that when farmers there received more accurate monsoon forecasts, they made more informed decisions about what and how much to plantor whether to plant at allresulting in better investment outcomes and reduced risk. A new era in climate adaptation AI weather forecasting has reached a pivotal moment. Tools that were experimental just five years ago are now being integrated into government weather forecasting systems. But technology alone wont change lives. With support, low- and middle-income countries can build the capacity o generate, evaluate, and act on their own forecasts, providing valuable information to farmers that has long been missing in weather services. Paul Winters is a professor of sustainable development at the University of Notre Dame. Amir Jina is an assistant professor of public policy at the University of Chicago. This article is republished from The Conversation under a Creative Commons license. Read the original article.
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E-Commerce
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