How to Avoid Pitfalls When Loaning Money to a Family Member

It’s either the stuff of nightmares—or a normal, everyday part of life. Financial advisors who cater to wealthy clients say a loan to another family member can be the answer to a lot of problems, but caution that they can raise just as many issues as they aim to solve.

“When money’s involved, family isn’t always family. Sometimes money trumps family,” says Jon Ekoniak, a partner at Bordeaux Wealth Advisors, based in Silicon Valley.

Loans between family members—usually, but not always, from an older person to their children or grandchildren—may be best ventured with the help of financial advisors who have experience setting them up. Professional guidance can make the situation more comfortable for family members unaccustomed to transacting business with each other. It’s also safer, as intra-family loans may have implications ranging from taxes to legal issues.

Ekoniak describes one example of parents with a grown daughter who’d married someone who “bounced” from job to job. The young couple wanted to have children, but could only afford a small rental apartment. After nearly a year of agonising over different ways to help the couple, the parents finally decided to simply offer them a loan to buy a house.

“They set up a trust fund and 50% of the income from the trust would be used to pay back the loan,” Ekoniak says. The loan repayments were interest only, at the government’s prevailing “applicable federal rate,” which is the lowest rate the IRS allows for private loans.

It seems straightforward—the “Bank of Mom and Dad” has long been a normal part of young adulthood in America, after all. But there are plenty of possible pitfalls to keep in mind.

If nothing else, loans “should be money that the parent is willing to turn into a gift,” says Mark Weiskind, founding partner at Independence, Ohio-based Fairway Wealth Management. “You’re very unlikely to push hard to collect from your child.”

In some cases, that impulse may be communicated up front, with the lenders explaining that they’d like to receive repayment according to a particular schedule but are flexible if the borrowers can’t make a payment for some reason. But even lenders who do expect their borrowers to stick to a particular repayment schedule need to be flexible, experts say.

What’s more, Weiskind says, “You have to be willing to accept a lower interest rate” on a loan than you might otherwise. In other words, don’t expect to make money from entering into a financing arrangement with a family member.

Parents helping their children should also carefully consider whether they want to tell their other children, if there are any, about the loan. That becomes an even thornier question when money is being loaned between grandparents and grandchildren or aunts and uncles and their niece or nephew, Ekoniak points out.

The bigger question becomes: Does helping one child mean you have to do the same for all the children? “Everyone is thinking, ‘what about me?’” Ekoniak says.

Experts also advise thinking through all the various scenarios that could arise after the loan is agreed to. If the parents are never repaid, do they simply write off the loan as a gift, say by applying the full gift tax exclusion exemption of US$36,000 per couple every year until the balance disappears? If one sibling benefits from the loan but others do not, should it be considered an advance on the inheritance—and do estate documents need to be rewritten to account for the discrepancy?

While the most common uses for intra-family loans do tend to be real estate, Weiskind has also structured several that allow a parent to pass on a share of the family business to children. Either way, it’s important to consider whether the loan has some sort of collateral, he says, and how complicated the documentation needs to be.

“More often than not we see a simple promissory note,” Weiskind says. “I have seen a few instances where they have filed a mortgage on the property just to have protection in case of divorce”—that is, to protect the natural-born child against someone who’s married into the family.

“I’ve never seen a parent foreclose on a child,” Weiskind says with a laugh. But not paying back a loan “could lead to bad blood.”

As if family dynamics weren’t already challenging enough.

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