Preventing the Rising Threat of Financial Fraud

Corporations and banks have boosted their security infrastructure and employee training to avoid getting hit by financial fraud, including cybercrime, but individuals and families often are less prepared and, as a result, are a softer target for criminals.

“They’re not giving due consideration to the scores of ways they’re vulnerable and fraud can happen,” says Mona Manahi, head of personal CFO services for Geller, an independent multi-family office firm.

Several stats back this up, including a 30% rise in consumer scams reported by the Federal Trade Commission and a rise in mail fraud by criminals getting access to credit cards and checks reported by the U.S. Postal Service this past spring. A UBS survey found 63% of U.S. family offices reported being targeted by cyber threat actors.

The “great wealth transfer” to a younger generation is also putting large amounts of cash into the hands of millennials and Generation Z, “and they tend to be a little bit more lax in their trustworthiness,” Manahi says, citing how much younger people share information on social media and use electronic payment services.

“What’s happening now feels like a big paradigm shift, and people really need to pay attention,” she says. “It’s like a perfect storm.”

But families can protect themselves often with simple steps, such as wiring big amounts of money through credible financial institutions instead of putting a check in the mail. Manahi and Scott Bush, Geller’s chief client officer, detailed a range of fraud-prevention measures recently with Penta.

Avoiding Cyber Threats

The rise of technology in people’s lives has given criminals more sophisticated ways of commiting crimes, enabling them to target wealthier individuals and families.

A decade ago, criminals might have put a skimmer on a gas station credit card machine to glean data from just about anyone. Today, criminals break into household wireless networks to access email and phone communications that tell them where a family spends money, why they spend it, and where they can find pools of capital to tap, Bush says.

“What’s changed is that the more organized, very high-quality criminal networks have started to realize that they get better bang for the buck if they focus on ultra-high-net-worth families,” Bush says.

Most families allow all their personal financial information to be accessed through the same wireless network they use for watching Netflix or checking email, believing it’s safe because the network is password protected.

“What they often don’t think about is when their children give that same password out to that server to their friends so they can use the Wi-Fi or they plug in the gaming console or they allow all of the people that are helping them maintain the house access to the Wi-Fi so that they can plug their phone in when they’re working at the house,” Bush says.

A simple way to avoid a password getting into the wrong hands is to have two networks in the house, one for personal financial information and another for access to wireless services that anyone can use.

Also, despite lots of education on the topic, most people continue to create weak passwords that criminals can easily decipher, especially once they’ve learned the names of family pets and children, or other personal details.

“If you can focus on securing your household and securing how you manage your personal information, there’s a high likelihood that bad guys just will decide to go somewhere else,” Bush says.

Breaking the ‘Fraud Triangle’

Wealthy families often believe they can keep tight control over their finances if fewer people are involved. But that strategy can lead easily to theft that can go undetected for years. This past December, for instance, a 74-year-old Texas woman pleaded guilty to a scheme of embezzling at least US$29 million from a Dallas charitable foundation and other companies owned by a family, according to the U.S. Department of Justice.

Manahi brings up this incident in an article for Geller on how families can lower their risks by breaking “the fraud triangle,” a term coined by a Brigham Young professor Steve Albrecht decades ago to refer to the three elements needed to execute a fraud: motive, opportunity, and rationalization. Families can’t address a criminal’s motive or rationalization, but they can remove the opportunity. That greatly reduces the potential for fraud, Manahi says.

Often, that means not giving a single personal assistant, or bookkeeper in the Dallas case, too much access or authority to your finances. One of the simplest controls families can put in place is “segregation of duties,” she says. For example, don’t allow one person to have authority to set up a vendor for payments, execute on those payments, and then reconcile the movement of cash in a checking account.

“Regardless of what their structure is, [every family] should have a very clear set of protocols related to how capital moves,” Bush adds. There should be double or even triple authentication for cash transfers, and everybody who works with the family should be aware of “who has the right to move capital and where it might move to.”

Families should also create an employee manual that clearly outlines security and safety protocols. “Just letting [employees] know that there is awareness, that security is an issue, and they are accountable for it is a great way of creating an environment that is secure,” he says.

Also, families should put systems and processes in place to consistently track where money is spent, how it’s spent, and how it relates to a predetermined budget. Then you or an employee can flag when things are out of line. The idea is to show that “the family cares and that at any time, activity can be inspected,” Bush says.

Even the most diligent families can let down their guard during the summer months or the winter holidays, particularly when they are traveling to far-away or remote locations, which is not unusual for a wealthy family. “They’ll be on safari and all of a sudden there’s a flurry of activity in their account when they’re not available,” Bush says.

Having formal protocols in place to ensure no single person can move money will help. Any protocols should also include instructions for what to do in case of an unusual transaction.

“Not only segregating the responsibilities during that time, but also educating [employees] on what they should be looking for and reviewing and increasing their responsibility during that time,” Manahi says.

A sharp technology sell-off has exposed a new anxiety on global markets: whether the enormous borrowing behind the artificial-intelligence infrastructure boom can generate sufficient returns as interest rates rise and Chinese competition intensifies.

The artificial-intelligence investment boom has powered technology shares, semiconductor manufacturers and data-centre operators to extraordinary heights. Last week, investors were given a reminder that much of this expansion is being built with borrowed money.

The technology-heavy Nasdaq Composite fell 4.5 per cent over the week ended October 16, its weakest performance since late June. Semiconductor shares suffered an even deeper correction, with the Philadelphia Semiconductor Index losing about 11.4 per cent.

The selling spread quickly through global markets. Micron Technology fell 14.6 per cent over the week, while South Korea’s Samsung Electronics and SK Hynix dropped approximately 18 per cent and 24 per cent respectively.

This was more than a routine bout of profit-taking. Investors were reassessing three interconnected risks: higher interest rates, the rapidly expanding debt burden attached to AI infrastructure and the prospect of stronger competition from Chinese memory-chip manufacturers.

The debt behind the AI build-out

Artificial intelligence requires a physical infrastructure network of enormous scale. Data centres must be built, advanced processors purchased, power supplies secured and cooling systems installed before customers begin generating meaningful revenue from them.

Technology companies are increasingly supplementing their own cash with corporate bonds, syndicated loans, equipment leases, private credit and project-level financing.

JPMorgan describes the emerging funding model as a combination of conventional corporate borrowing and project finance tied to individual data-centre assets. In Australia, the Reserve Bank estimates that syndicated loans accounted for approximately three-quarters of data-centre financing during 2025 and 2026 to date.

The numbers are becoming large enough to affect broader credit markets. The Federal Reserve Bank of Dallas has cited Wall Street estimates suggesting AI-related investment-grade bond issuance could approach US$300 billion in 2026 alone.

That borrowing is not automatically problematic. Debt can be an efficient way to finance long-lived infrastructure when future revenue is sufficiently predictable. The concern is that AI technology, customer demand and computing economics are changing much faster than the repayment schedules attached to the assets.

A data centre financed today may carry debt for years. Investors must therefore make assumptions about future utilisation rates, electricity prices, chip performance, customer concentration and the amount businesses will ultimately pay for AI computing.

When interest rates rise, those assumptions become less forgiving.

Inflation changes the calculation

September’s US inflation data intensified the pressure. Consumer prices increased 3.7 per cent over the year, ahead of market expectations and up from 3.4 per cent in August.

The hotter reading reduced confidence that interest rates would fall quickly and pushed bond yields higher. That matters particularly for companies whose valuations depend on profits expected well into the future, as well as highly leveraged infrastructure operators that may need to refinance.

CoreWeave has emerged as a closely watched test case. The specialist cloud-computing company has accumulated substantial debt and lease obligations to build AI capacity, while relying on a concentrated group of large technology customers.

Its shares fell sharply during the sell-off, while the cost of insuring its debt against default climbed. CoreWeave has argued that its contracted backlog more than covers its obligations and that financial markets do not always understand its business model. Nevertheless, its securities have become a proxy for investor confidence in debt-funded AI infrastructure.

Oracle, another major investor in AI computing capacity, fell 8.8 per cent in Friday trading, while CoreWeave dropped 15.5 per cent.

The question is shifting from how much AI infrastructure can be built to how profitably it can be operated.

China adds another source of pressure

Interest rates were not the only catalyst. Reports of advances by Chinese memory-chip producer ChangXin Memory Technologies raised concerns that lower-cost Chinese products could increase competition across the global semiconductor industry.

Memory chips are essential to AI systems, but the industry is historically cyclical. Periods of tight supply and high pricing can encourage rapid investment, eventually producing oversupply and falling margins.

The prospect of a well-funded Chinese competitor expanding its technological capabilities unsettled investors in Samsung, SK Hynix and Micron. It also demonstrated that AI-related demand does not insulate chipmakers from conventional competitive pressures.

That combination—slower rate cuts, more expensive financing and potentially cheaper supply—challenged the assumption that every company exposed to AI would enjoy continually expanding margins.

A reset rather than the end of the boom

The sell-off does not establish that the AI boom is ending. Demand for computing power remains strong, the largest technology companies continue to generate formidable cash flows and many infrastructure projects are backed by long-term customer contracts.

What has changed is the market’s willingness to overlook the price and financing structure of that growth.

The most resilient companies are likely to be those able to fund investment from operating cash flow, secure long-duration customers and demonstrate that new capacity will earn returns comfortably above its financing costs.

More speculative operators may face greater scrutiny, particularly where debt is expensive, customers are concentrated or expected revenue depends on demand that has not yet materialised.

For Australian investors, the lesson extends beyond US technology shares. Local data-centre operators, infrastructure funds, electricity suppliers and property groups are increasingly exposed to the same investment cycle.

Artificial intelligence may remain one of the defining growth themes of the decade. But last week’s correction showed that enthusiasm for the technology does not eliminate financial discipline.

The next stage of the AI boom will be judged not only by computing power, model performance or demand for chips, but by something more traditional: whether the assets being financed can generate enough cash to repay the money borrowed to build them.

Fact box

Nasdaq Composite: Down 4.5 per cent for the week ended October 16, 2026

Philadelphia Semiconductor Index: Down approximately 11.4 per cent

Micron Technology: Down approximately 14.6 per cent

Samsung Electronics: Down approximately 18 per cent

SK Hynix: Down approximately 24 per cent

US September inflation: 3.7 per cent annually

Estimated 2026 AI-related investment-grade issuance: Approximately US$300 billion