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Archives for October 2026

How to build CPA career paths that keep up with new licensure, mobility, and AI changes

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New Jersey opened another route to CPA licensure, adding new opportunities for firm hiring..

Don Meyer, Chief Growth Officer at the New Jersey Society of CPAs, explains what the new pathway means for students and firms and how some firms are approaching hiring practices in the new environment.  

The new pathway  framed Earmark’s webinar, Navigating the New CPA Pipeline: Licensure, Mobility, STEM, and AI. Julia Woislaw of LCvista joined Don and Susan Dyer, Director, Member and Market Growth at the New Jersey Society of CPAs, to discuss how opening access matters only if firms build a career path behind it.

Match hiring policies to the new pathways

Susan compared the traditional route, which generally combines 150 credits, one year of qualifying experience, and the CPA exam, to the alternative that generally pairs a bachelor’s degree with two years of qualifying experience and the same exam.

New Jersey’s additional pathway took effect in February 2026. While students now have an option with fewer total credit hours, candidates still need 24 accounting credits and 24 business credits. 

For some students, the new route means earning a salary instead of paying for 30 more credits. For others, graduate study still supports their goals.

Your firm’s policies should reflect both choices. Two strong entry-level hires may have different licensing timelines. If a promotion or pay increase requires a license, explain how that milestone works under each route.

Check effective dates, too. A state may pass legislation without making the new route available yet. Recruiting teams need to know the rules, not just a map of enacted laws.

Once you hire someone, the next question is whether the job supports the license.

Plan qualifying experience, not just time on payroll

“Two years on the payroll doesn’t automatically mean two years of qualifying experience,” Susan warned.

In New Jersey, qualifying experience requires direction from an appropriately licensed CPA with an active license. The assignments and documentation must meet board requirements. Repetitive work deserves a closer look. Does it provide qualifying experience and meaningful development?

Build a plan that identifies:

  • The intended licensing state and required coursework
  • Assignments that meet the board’s experience requirements
  • Who directs the work and documents the experience
  • Exam preparation, fees, reimbursement, and realistic study time

Compare assignments with the state board’s experience form. Revisit the plan when departments, supervisors, or employers change. And remember, being eligible to sit for the exam doesn’t necessarily mean someone is eligible to receive a license.

Those original qualifications also matter after licensure, when a CPA serves clients across state lines.

Mobility requires knowing how each CPA qualified

Julia separated three concepts:

  • Substantial equivalency: a board’s determination that another state’s licensing qualifications are equivalent to its own
  • Mobility: temporary practice across state lines without obtaining another license, when permitted
  • Reciprocity: obtaining an additional license based on an existing one

Many states moved away from state-based substantial equivalency and toward individual-based mobility. Instead of relying mainly on the licensing state’s requirements, they look at how the person qualified. Some add conditions tied to education, experience, or the exam. Good standing alone may not answer every question.

Julia recommended tracking six items for each CPA: initial licensing jurisdiction and year, other licenses, residence, principal place of business, original education pathway, and prelicensure experience pathway.

The original licensing date can make a difference for grandfathering provisions. Remote work adds additional complexity because residence and principal place of business may trigger different obligations. HR should share remote-work decisions with whoever manages licensing compliance. 

Show students the technology they can create

Don recalled discussing a New Jersey student-loan assistance program that seemed ideal for accounting graduates. The catch was it required a STEM discipline, and accounting didn’t qualify.

The profession’s effort focuses on the “T” in STEM (technology) rather than mathematics. Don reported that Assemblyman Sterley Stanley’s legislation passed the Assembly on September 24, but Senate action was still pending at the time of recording. He described potential benefits, including eligibility for loan assistance and education funding opportunities.

For recruiting, he recommends firms show students how accountants create technology, not just use it. Firms developing proprietary AI solutions for clients is an example.

That promise needs a workplace where new hires can learn, and technology doesn’t crowd out human contact.

Use AI without losing the people

Don described AI recruiting as a balancing act. It can screen large applicant pools, but candidates can game keywords. He also questioned AI-agent interviews, especially when companies don’t disclose them.

Training presents another tension. A managing partner at a regional firm told Don he doesn’t have work for interns and first-year associates. AI and outsourcing handle tasks that once taught beginners.

Simulations and personalized, AI-supported learning could help staff practice, identify learning opportunities, and give managers more room to coach. 

Susan emphasized structured onboarding, accessible mentors, competitive pay, clear workload expectations, and coordinated hybrid schedules. Learning depends on access to experienced colleagues, not simply days in the office.

To connect these pieces, start with one employee’s experience.

Review the whole path

Trace one new hire’s journey from application through the first several months:

  • Do recruiting promises match assignments and advancement policies?
  • Are experience documentation and licensing records clear?
  • Does the employee receive consistent training and coaching?

Use state boards, NASBA, and state CPA societies to stay informed. As Don advised, emphasize accounting’s relevance and purpose rather than trying to make it “cool.”

Watch the on-demand webinar, Navigating the New CPA Pipeline: Licensure, Mobility, STEM, and AI to review what these changes mean for your firm.

Intuit is going high and low—and accountants are in the middle

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Intuit’s fiscal 2026 fourth quarter results include two numbers that tell an interesting story. Revenue rose 14% to about $4.4 billion, beating the company’s guidance of 10% to 12%. Yet the number of paying QuickBooks Online customers grew only about 3% globally, reaching 8.9 million. Meanwhile, average revenue per customer climbed 15%.

That difference explains Intuit’s next move: attract more businesses at the low end, keep expanding into the mid-market, and earn more when money moves through payments, payroll, and lending.

In Episode 161 of The Unofficial QuickBooks Accountants Podcast, Alicia Katz Pollock translates that investor story into a language accountants can understand. She explains why Intuit is moving downmarket and upmarket at the same time, and what firms should do before automation, new products, and advisory opportunities arrive at their doors.

Strong revenue can’t hide slower customer growth

The quarter itself was strong. Global Business Solutions, which includes QuickBooks, generated about $3.1 billion in revenue, up 15%. QuickBooks Online revenue reached about $2.28 billion, up 20%.

The concern is what comes next. Intuit expects total company growth of 9% to 10% in fiscal 2027 and Global Business Solutions growth of 13% to 14%. Management also lowered its three-year growth range for that segment from 15%–20% to 10%–15%.

This is a reset of expectations. U.S. QuickBooks Online customer growth was healthier at 6%, but global customer growth still trails revenue growth. Intuit is good at earning more from existing customers. Now it needs to bring more businesses into the ecosystem.

That need reshapes both ends of the QuickBooks product line.

Intuit is widening the front door

At the low end are QuickBooks Free and QuickBooks Lite. Intuit describes them as a wider front door for new and very small businesses. More than 20,000 customers already actively use QuickBooks Free or move into it from a paid product.

The business model is to bring in a business at little or no subscription cost, then earn revenue through payments, payroll, lending, upgrades, and other services. These products could help microbusinesses move away from spreadsheets, personal bank accounts, and payment apps.

But Alicia warns that simple and safe aren’t the same thing. Test these products with a sample company. Review the automated workflows, run reports, and make mistakes on purpose. Then decide which clients are a good fit.

This wider front door also creates a larger gap between entry-level tools and higher-priced plans. Accountants may have to explain why a client needs to upgrade, even when they didn’t choose the original subscription.

The mid-market offers a different kind of opportunity

While Intuit moves downmarket, it’s also pushing higher. Revenue from QuickBooks Online Advanced and Intuit Enterprise Suite grew 38% during the quarter. Full-year mid-market revenue rose 39%, and the number of mid-market customers increased 28%.

These customers bring more entities, employees, approvals, integrations, and reporting needs. They also adopt more services. Advanced customers have a payroll attachment rate 13 percentage points higher than core QBO customers and a payments attachment rate nine points higher.

Enterprise Suite passed a $145 million annualized revenue run rate, about four times the prior year. Its construction edition also shows early traction.

Most important for accounting firms, accountants influenced 25% of new Enterprise Suite contracts over the last year. That creates opportunity, but product knowledge alone isn’t enough. Firms need methods for discovery, scoping, conversion planning, access controls, integrations, documentation, and post-launch support.

The financial link between Intuit’s smallest and largest customers is clear when we follow the money.

Money movement supports both ends of the ladder

QuickBooks Money revenue grew 31% in fiscal 2026. QBO payment volume, including Bill Pay, grew 30% and passed $225 billion. Businesses also create more than $2.7 trillion in invoices through QuickBooks Online each year.

At the low end, Intuit can offer inexpensive software because it may earn money when a business gets paid. At the high end, more invoices, employees, bills, and entities create more service revenue.

For accountants, subscription price isn’t the only cost to consider. Transaction fees, payroll, lending, and related services also belong in the calculation. This strategy also explains why QuickBooks frequently advertises service upgrades inside the accounting workflow.

Automation creates time, not an advisory practice

Intuit says more than 150,000 accountants use Intuit Accountant Suite. Those users report increaing advisory work about 30% as routine tasks become automated. Intuit also says its AI experienceshelp customers get paid four days faster.

Those are useful outcomes, but automation only creates available time. It doesn’t decide how a firm uses it. To turn that time into advisory revenue, ask:

  • Do we have a defined advisory offer?
  • Can our team recognize decisions clients need to make?
  • Have we set boundaries around what’s included?
  • Can we turn a forecast into a useful conversation instead of a long report?

Software can flag a cash shortage. An accountant helps the owner decide whether to change payment terms, use a line of credit, postpone a hire, or raise prices. Helping someone make a better decision is advisory work.

Turn the strategy into action

Alicia recommends three practical moves:

  1. Build a product-fit matrix. Compare transaction volume, inventory, entities, users, approvals, reporting, payroll, payments, and integrations across QuickBooks plans and non-QuickBooks options.
  2. Define one repeatable advisory conversation. Start with cash flow, project profitability, or receivables, and connect the discussion to a decision.
  3. Test the automation. Use a sample company to document what Intuit’s AI recommends, what data it uses, and where human review changes the answer.

Also keep watching desktop migrations and Mailchimp. Desktop still represents about a quarter of Global Business Solutions, while Mailchimp is now reported separately after roughly flat to slightly lower revenue.

Intuit’s quarter was strong, but its strategy is changing. For accounting professionals, that offers an opportunity. Match products to complexity, test automation before trusting it, and make human judgment stronger as the software gets smarter.

For Alicia’s complete analysis, listen to the full episode.

The Verification Failures Behind Two of Accounting’s Biggest Stories This Week

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A cluster of Polymarket accounts won 41 of 42 bets on whether KPMG-audited companies would beat earnings estimates, a 98% hit rate. Meanwhile, investors put more than $13 million into tax startup ComplYant, whose founder allegedly claimed to be a CPA. Both stories point to the same failure: trust without verification.

On Episode 505 of The Accounting Podcast, Blake Oliver and David Leary discuss the federal investigation involving the Polymarket bets, fraud charges against ComplYant founder Shiloh Luckey, AI-powered scams, accounting automation, and pressure on tax firms to improve pricing and client service.

Trust is the common thread. As AI takes over more routine work, accountants must prove their value through judgment, skepticism, verification, and a better client experience.

When no one verifies

The Wall Street Journal reported that a mystery trader or group made about $22,000 from bets involving 18 KPMG-audited companies, including Wells Fargo, Home Depot, and DoorDash. Blockchain forensics firm Bubblemaps linked 19 accounts through flows of digital funds, suggesting that one person or team may have controlled them. The accounts won 41 of 42 KPMG-related bets and 82% of bets involving companies audited by other firms.

The pattern isn’t proof of insider trading, and the Journal couldn’t confirm that the accounts belonged to a KPMG employee. Still, the Journal has reported that federal prosecutors are weighing charges against a KPMG employee suspected of betting on client earnings. “How could you know that these companies are going to beat their earnings or not unless you had some kind of information ahead of time?” Blake asked. KPMG said it has zero tolerance for trading on nonpublic client information.

The ComplYant case involves information that was publicly available but apparently went unchecked. Prosecutors charged Luckey with nine counts of securities fraud, three counts of wire fraud, one count of bank fraud, and two counts of money laundering. They allege that she falsely claimed to be a licensed CPA and inflated ComplYant’s revenue, subscriptions, and cash reserves while raising more than $13 million. The indictment also alleges personal spending and a $1.5 million check-kiting scheme. These are allegations, not proven facts. Since we recorded, Luckey has pleaded not guilty.

CPA licenses are publicly searchable. As David noted, a large funding announcement doesn’t prove investors performed careful due diligence. In both stories, serious problems emerged after people assumed someone else verified the facts.

AI raises the stakes for fraud prevention

That need for verification is more urgent as AI makes scams cheaper to run. The hosts cited reporting that AI makes fraud 4.5 times more profitable. A scheme that once required a room full of callers can now operate with a few automated agents on a server.

David pointed to Revolut, a neobank that reportedly released customer information after receiving a social-engineering request from a legitimate government email domain. Revolut’s own guidance tells customers to confirm such requests through official contact information. The same standard should apply inside companies: a familiar domain or convincing voice isn’t enough to establish identity.

Technology can strengthen the defense. KPMG bought a stake in Reality Defender, which detects fake voice and video in real time. Meanwhile, Polymarket’s public blockchain records helped outside analysts identify the suspicious betting pattern. But for technology to support trust, firms need to build it into effective controls.

Automation changes how accountants develop judgment

That same shift creates a training problem. KPMG’s TaxSim, developed with Centaurian AI, uses simulations to help entry-level accountants gain experience. Blake saw value in replacing repetitive administrative work with focused practice. David was more skeptical. If real work disappears, how much simulation replaces it? Blake asked who pays for that training. Firms can’t simply automate junior tasks and expect professional judgment to develop on its own.

KPMG is also working with OpenAI on conversational access to systems like ERP and CRM platforms. That could spare employees from difficult software interfaces, but it doesn’t remove responsibility for understanding the underlying data and controls.

Xero faced the same tension after sponsoring an influencer post that claimed connecting Xero to Claude replaced the management accounts she used to pay an accountant £800 a month to prepare. Accountants reacted angrily, and Xero removed the post and apologized. Its leaders said AI should handle routine work while accountants focus on expert judgment and advice. That distinction only matters if clients can clearly see the value of that judgment.

The bigger threat is a poor client experience

Blake and David tested AI on their own business returns. Blake used Claude Cowork with TaxAct for S corporation and partnership returns. David combined TurboTax, ChatGPT, and QuickBooks reports, asking the AI what to do at each step.

David valued the immediate back-and-forth. When he supplied an accrual-basis report instead of a cash-basis report, the AI flagged it. He didn’t have to wait days for another email or manage a long portal exchange.

Neither host claimed AI was error-free. They discussed mistakes made by professionals and tax software, including a return prepared on the wrong accounting basis and Blake’s mother being incorrectly marked as blind. David cited Christine Gervais’s advice to treat every AI answer like work from a first-year hire and verify it against the tax code. Human review still matters, but the possibility of an AI error doesn’t excuse a slow, burdensome process.

David summarized the problem with a mechanic analogy. When clients pay a professional, they shouldn’t have to manage the work themselves. “It’s not AI,” he said. “The threat is the experience for the client.”

The same pressure applies to billing. After IRS guidance warned that billing clients for time AI saved could be an unconscionable fee, Blake argued that hourly billing was the real issue. Firms should charge for expertise and responsibility, not hours that technology eliminates.

Closing the trust gap

These stories ask the same question in different ways: Can accounting professionals still deliver credible verification and judgment? AI makes fraud easier, but it also improves detection and service. Firms can succeed by combining strong controls with careful review, clear pricing, and processes that respect the client’s time.

Listen to Episode 505 of The Accounting Podcast for Blake and David’s full discussion.

How Finance Leaders Can Tell Whether AI Is Really Paying Off

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Your AI bill tells you what you spent, but not whether anything got better.

That divide worries finance leaders. Mike DePrisco, President and CEO of the Institute of Management Accountants (IMA), speaks with hundreds of CFOs and finance leaders around the world. Their questions have evolved from whether to use AI to how to get value from it.

On the Earmark Podcast, Mike and host Blake Oliver, CPA, explored how accountants can measure that value. The challenge is finding business improvements and developing people who can check, interpret, and explain AI’s output.

Adoption isn’t the same as a return

AI is the top concern among the finance leaders Mike meets. They’re deploying and investing in it, but skepticism is growing about the payoff.

“We’re not seeing the promised return on investment fast enough,” he says, describing what he hears from leaders.

Blake points to a related problem: rising costs. He describes a shift from predictable subscriptions of roughly $20 to $100 per seat toward additional usage-based charges from providers like Anthropic and OpenAI. Those charges are measured in tokens, the units used to track model usage, and CFOs and controllers struggle to keep spending under control.

Mike says accounting and finance professionals adopted AI slowly, partly because mistakes can affect financial statements, reporting, and compliance. That caution was reasonable. Now that more organizations use the technology, they need tangible evidence that it works.

But tracking spending is only half the problem. What should leaders measure on the other side?

Measure what actually improves

Blake notes that token costs are measurable. Connecting those costs to outcomes is harder. Mike offers several practical indicators, not as a complete ROI formula, but as ways to judge whether an investment produces value.

  • Finance operations. “Are you closing the books more quickly?” Mike asks. Are you reducing errors month over month? Together, these measures help show whether AI improves speed and quality.
  • Decision support. Is forecasting more accurate? Mike connects better forecasts with better decisions and better use of resources. More output alone doesn’t prove value; the information needs to help the business make decisions.
  • Customer experience. Does the technology address actual customer problems? Internal customers may need to get work done faster or at lower cost. External customers may want smoother transactions or easier searches.

For financial institutions, Mike gives a specific example of reducing false fraud flags that put customers’ accounts on hold. That’s a business outcome, not just an improvement inside the finance department.

Organizations IMA works with are starting to use these measures to decide whether AI is working and whether to continue, scale, or invest more.

Mike suggests the strongest results come from having a clear problem to solve. But even a useful application still needs people who can question its output.

Faster work still needs sound judgment

Blake raises concerns about AI reducing entry-level work in public accounting. Mike isn’t fully convinced that the technology will mean fewer jobs overall. He expects jobs to change, but sees a continuing need for accounting knowledge.

“We see the mistakes that AI makes every single day,” he says. People with business knowledge and customer context spend time correcting those mistakes.

He describes this as human-machine collaboration. People must stay involved to check for bias, verify accuracy, and interpret information before using it to make decisions.

That’s why foundational training is still important. When Mike asks faculty how they respond to machines taking over basic work, they tell him students still need to understand the fundamentals. If students are going to work alongside these machines, “they need to know what to look for.”

The role also extends beyond checking numbers. Mike describes management accountants as business partners who interpret data from AI, ERP systems, and forecasting tools. They help other functions assess opportunities, solve problems, and make decisions.

That creates a training challenge. How do accountants build judgment if technology takes over the routine work where earlier generations learned?

Build the skills AI can’t supply on its own

IMA expanded its competency framework to include advanced data analytics, transformation, innovation, sustainability, and leadership. Mike says employers need strategic thinking, clear communication, and people who ask challenging questions.

Accountants also benefit from storytelling skills, since they need to explain information in the context of the organization’s strategy.

Blake pushes back, noting these sound like mid-level or executive skills. Mike points to internships, stretch assignments, mentorship, and scenario-based training as ways to help younger professionals practice them.

He also describes replacing an essay-based section of the Certified Management Accountant (CMA) exam with case-based questions. The goal is application. “It’s more than just knowing; it’s about doing,” he says. Still, he acknowledges some skills take time and experience to develop.

Blake suggests using AI itself for practice. A learner could describe their role and company to Claude, identify a concept they want to learn, and ask for a guided, simulated challenge.

For mid-career accountants, Mike recommends staying curious and prioritizing learning. He describes IMA’s micro-credentials, including short, focused programs in areas like AI and finance or advanced data analytics. He says professionals can learn something over the weekend and apply it at work on Monday.

Measure the results. Build the judgment.

AI’s value should show up as faster closes, fewer errors, more accurate forecasts, better decisions, and less customer friction. But those results depend on professionals who understand the business and can evaluate what the technology produces.

Listen to Episode 117 of the Earmark Podcast for Blake’s full conversation with Mike.

How the Polka King turned loyal fans into victims of a $4.9 million Ponzi scheme

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An elderly couple sits across from polka bandleader Jan Lewan. Their bank pays 3% interest. He guarantees 12% — it’s printed right in the middle of his flier.

“Twelve percent sort of sounds too good to be true,” one says.

“It’s true. That’s why it’s so good,” Jan answers.

They invest anyway.

That scene from the 2017 film The Polka King perfectly demonstrates the problem for Episode 121 of Oh My Fraud. Host Caleb Newquist and co-producer Zach Frank discuss Jan Lewan’s real-life fraud and where Hollywood takes liberties. Zach questions the film’s sympathetic portrayal, while Caleb finds more to enjoy in its comedy. Both warn that liking someone isn’t evidence that their investment works.

For those of us who handle other people’s money, the story starts with familiar pressures and ends with numbers that charm could never fix.

Building an empire on promises

The film opens in 1990. Jan, played by Jack Black, runs a polka band and gift shop while delivering pizzas. His mother-in-law, Barb, asks how he’ll support her daughter and grandson.

“I build empire,” he says.

Meanwhile, clarinetist Mickey Stutz threatens to quit. Jan persuades Mickey to stay by promising him and the band more money. Working hard isn’t producing enough cash to support those promises.

“If you want to go with fraud triangle right away, there’s pressure,” Zach says.

The fraud triangle describes three factors: pressure, opportunity, and rationalization. The film supplies all three. Jan needs money, his fans trust him, and he convinces himself that one successful venture will let him repay everyone.

Caleb notes that rationalization is often delusional. Believing you’ll eventually make good doesn’t mean you can.

But Jan’s fans don’t need much convincing. Their trust supplies the opportunity his businesses can’t.

Fans bought into Jan, not his business results

Many investors were elderly Polish Americans who loved Jan’s music and felt they knew him. In the film, the couple points to other investors who are supposedly smart with money. If those friends trust Jan, why shouldn’t they?

When a trusted figure exploits relationships within a community or fan base, this is affinity fraud. It’s a pattern.

Zach explains that Jan sold promissory notes, essentially IOUs promising repayment, and usually with interest over a set period. Lewan’s notes were securities, and he wasn’t registered to sell them.

Jan pitches an investment in himself and his businesses: a shop, band, mail-order catalog, jewelry, and travel company. Most were real businesses, but that didn’t make them good investments. Zach notes that people later described the shop as always empty.

Investors were buying into Jan rather than testing his business results. Personal recommendations replaced independent checking. That turned dangerous when fresh investor money started paying old promises.

New money made the returns look real

In the movie, Pennsylvania securities investigator Ron Edwards, played by J.B. Smoove, gives Jan three days to make things right. But Jan already spent the money.

Then the elderly couple arrives wanting to invest more. The turning point in the movie is when Jan realizes he can use new investments to repay earlier investors.

That’s the core of a Ponzi scheme. Payments appear to prove the investment works, but the supposed returns come from other investors rather than earnings.

Zach’s example shows how the obligations grow. At a promised 12% annual return, $100,000 becomes $112,000 after one year and about $176,000 after five if the investor leaves the interest in. Reinvestment delays the need for cash while increasing the amount Jan owes.

The film compresses the regulatory timeline. According to Zach, regulators first confronted Lewan in 1992. Pennsylvania formally barred him from selling the notes in December 2000. When he continued, regulators sought a preliminary injunction in April 2001.

New deposits and delayed withdrawals kept the illusion alive, but a rush for the exit exposed it.

A scandal brought investors to the door

In the film, Jan’s wife Marla unexpectedly wins Mrs. Pennsylvania in 1998. Bribery allegations follow, and investors start demanding their money back. The movie uses Marla to replace Lewan’s real-life wife, Rhonda, and changes parts of the pageant story.

Caleb calls mounting withdrawals “the death knell” for a Ponzi scheme.

The elderly couple asks for roughly $273,000. As Jan prepares their check, he mentions a new opportunity paying 20%. They decide to roll their money over instead.

The bigger promise buys time.

Zach stresses that Lewan’s real businesses were also failing. The fraud involved about $4.9 million from more than 400 investors across 21 states. Liquidating his assets produced about $66,000.

“Just a little short,” Caleb jokes.

Lewan ultimately pleaded guilty and served time in prison. He was released in 2009, then returned to performing polka and organizing trips.

Accountants watching this story should consider what would have counted as real verification.

Access to the books isn’t proof

In the movie, Mickey asks to see Jan’s books. Jan readily agrees. Zach notes that the real case involved fake charts and records. Showing someone numbers isn’t the same as proving they’re true.

The hosts’ lessons leave us with three practical checks:

  • Treat referrals as introductions, not evidence. Do your own research, even within a trusted community.
  • Compare promises with performance. Can the businesses actually earn enough to cover the payments?
  • Verify records, not just access them. Reports supplied by the person under scrutiny need independent checking and someone who understands them.

Even investors who withdraw apparent profits may face clawbacks when courts try to recover money that came from other victims, although that didn’t seem to happen in Lewan’s case.

Charm isn’t collateral. Asking hard questions protects both the money and the relationships behind it.

Listen to the full episode of Oh My Fraud for Caleb and Zach’s discussion of the film, the real losses, and the warning signs behind the music.