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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.

Is your answer about what QuickBooks can’t do already out of date?

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A client asks whether QuickBooks can handle multiple units of measure, consolidated reporting, or AIA-style billing. We answer from memory, then discover the product changed. With Intuit releasing features this quickly, what was true six months ago may not be true today.

That’s the backdrop for Episode 160 of The Unofficial QuickBooks Accountants Podcast. Host Alicia Katz Pollock and guest Hector Garcia review Intuit Enterprise Suite (IES) developments through August 2026. They note that while IES expands what QuickBooks can handle, some features are moving into QuickBooks Online Advanced and Plus.

As Hector explains, “If I know how to use QuickBooks, I know how to use Intuit Enterprise Suite. I just have to learn the additional functions.”

IES extends the QuickBooks Online foundation

IES uses the familiar QuickBooks Online interface, API, app ecosystem, and Desktop migration process. It adds more fields and functions, but users don’t have to learn an entirely new accounting system. Intuit is also updating the underlying platform across QuickBooks Online, IES, and Intuit Accountant Suite, which Hector says should improve performance.

Intuit doesn’t publish pricing because it builds a custom package around each company’s needs. But Hector offered these estimates:

  • A single entity may cost about $750 to $800 per month
  • Three entities with payroll may average about $1,800 per month
  • Pricing can change based on users, entities, dimensions, payroll, and add-ons
  • IES includes five users, with the option to expand to up to 500. QuickBooks Online Advanced stops at 25.

Dimensions show the wider operating range. They work like multiple classes that you can name and use at either the transaction or line-item level. IES can support up to 20 dimensions. As of August 2026, you can apply dimensions to every transaction that normally supports classes, including timesheets and payroll.

Users can also build reports with one dimension on the rows and another on the columns. Balance sheets by dimension were in beta, while an AI backfill tool could suggest dimensions for older transactions based on patterns involving customers, vendors, and items. That historical backfill may make dimensions far more useful during implementation.

Multi-entity accounting creates a major advisory opportunity

IES connects related companies for consolidated reporting. Users can run consolidated profit and loss statements, balance sheets, sales reports, and roughly 20 other reports. They can also drill from a consolidated report into the original transaction in the underlying company file.

The system supports non-posting eliminations, which remove intercompany activity from consolidated statements without changing each entity’s books. Intuit added automatic eliminations in August for linked intercompany invoices and bills. An invoice created in one company can generate a related bill in another company and hold it for approval.

That automation depends on careful setup. Customers, items, and accounts must be mapped correctly between companies. Hector warned that this can take “hours and hours and hours,” but he also called it one of the biggest consulting opportunities in the Intuit ecosystem.

Companies can keep separate charts of accounts and map them to a standard reporting structure. Alternatively, they can adopt and lock a shared chart across all entities. Once that foundation is in place, IES can support much more specialized work.

Construction leads, with manufacturing and nonprofits following

Hector believes IES reaches, and in some areas exceeds, QuickBooks Desktop Enterprise’s construction capabilities. The expanding toolset includes:

  • Estimate-versus-actual and work-in-progress reporting
  • Percentage-of-completion revenue recognition
  • Revised contract values and separate positive or negative change orders
  • Project-specific user permissions
  • Target profit margins and project KPI dashboards
  • AI-assisted imports of project budgets from spreadsheets or PDFs
  • AIA-style billing based on an estimate and schedule of values

Manufacturing and inventory are next. Multiple units of measure is live in IES. So, for example, a yarn company could buy yarn by the case, track it by the roll, and sell it by the yard or inch. A few Advanced customers also had access, although Intuit hadn’t announced a broader release yet. Bills of material are in beta, while multi-location inventory, lot and serial tracking, and barcode scanning are under development.

Nonprofit tools may follow in 2027. Intuit is exploring grant management and restricted-asset tracking, although pricing will be an important concern for smaller organizations.

IES features are already moving downstream

Even if your clients never adopt IES, its development matters. Intuit has already moved several ideas into lower QuickBooks Online tiers, although availability may vary:

  • Cost groups organize products and services into reporting buckets such as labor, materials, and equipment
  • Project phases divide job costs into stages such as foundation, walls, and punch-out
  • Proposals turn estimates into polished documents with text, branding, and electronic acceptance
  • Calculated report fields add spreadsheet-style math inside QuickBooks reports
  • Sales order improvements connect one sales order to purchase orders from several vendors
  • Item receipts separate the date inventory arrives from the date the vendor bill posts

Item receipts require special care. Once enabled, the client must consistently follow the purchase order-to-item receipt-to-bill workflow. Skipping a step can leave a balance in the inventory clearing account.

That’s why Alicia recommends revisiting rejected features every three to six months. A workflow that failed last year, or even last quarter, may work differently now.

Build a habit of checking again

Smart accounting professionals will track IES releases to see what may reach QuickBooks Online next, reevaluate product limitations every three to six months, treat mapping, permissions, and workflow design as advisory services, and test optional workflows before introducing them to clients.

Listen to the full conversation between Alicia Katz Pollock and Hector Garcia in Episode 160 of The Unofficial QuickBooks Accountants Podcast.

Alicia Katz Pollock’s Royalwise OWLS (On-Demand Web-based Learning Solutions) is the industry’s premier portal for top-notch QuickBooks Online training with CPE for accounting firms, bookkeepers, and small business owners. Visit Royalwise OWLS, where learning QBO is a HOOT!

Building a people-first CPA firm while technology changes the work

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Matt Kidd, CPA, of Kidd Advisors hosted a tax-day party at an Irish pub near his Livonia, MI office. About 50 clients and prospects came. The tab was roughly $3,000.

Many guests are $500 annualtax return clients. Matt joked that they “probably ate and drank the profit margin” out of those returns.

So why would a mostly remote firm owner spend money bringing them together? Because a relationship can outlast the transaction that started it.

In Season 5, Episode 21 of Who’s Really the BOSS?, Matt joins hosts Rachel and Marcus Dillon to discuss investing in people while technology changes the work. They focus on choosing where time matters and making those relationships count.

Give employees a reason to improve the work

Matt’s four-person firm, including him, is about 2.5 full-time equivalents. They have flexible schedules with assigned phone coverage. Weekly check-ins cover each person’s best moment and current challenges, while connecting the accounting and financial-planning sides of the business.

He also introduced weekly “innovation meetings” focused on AI. After three or four weeks of brainstorming, the participating employees began building tools to solve their own frustrations.

Matt couldn’t identify every problem himself. He was too removed from much of their daily work. Instead, employees chose what to fix.

They were particularly motivated because Matt made the benefit clear: save time, and you can work less. Bring in more work, and the firm’s profitability-based bonus structure lets you earn more.

“They’re building way more than I am at this point,” he said.

Marcus described a challenge in his own firm: testing can consume time while client work still needs to get done. Matt acknowledged that tools used by two or three people are easier to adjust than tools rolled out across a larger organization.

He returned to the idea of letting employees choose meaningful problems and explaining how they’ll share in the gains. But better tools aren’t the only way to show people they matter.

Make appreciation a habit

Matt offers maternity and paternity leave and has paid substantial profit sharing. Still, he believes benefits don’t replace regular, genuine thanks.

“The biggest thing is just actually telling them you appreciate them,” he said. During tax season, he tries to recognize something specific at least once a day.

Rachel builds reminders into the Dillons’ routines, including weekly written check-ins, one-on-one conversations at least every other week, direct messages, and handwritten notes. As teams grow and spread out, appreciation needs a rhythm.

Marcus suggests thanking employees in front of someone they love, such as a spouse, parent, or another important person. Rachel adds that a card addressed to your team member’s special person can work when you’re remote.

Trust also requires listening. When an employee challenged Matt, he thought it over, realized the employee was right, and apologized. He credits that openness with building a collaborative team where people can question one another.

You don’t need a large budget to practice this. Give specific thanks, address problems promptly, and accept feedback yourself. Then apply that same care to how you spend time with clients.

Choose client relationships before filling your calendar

Matt bought his practice on September 1, 2017. At the time of recording, he expects revenue of about $750,000 for the accounting firm side, up from roughly $350,000 when he acquired it. He projects financial planning to add another $200,000.

Yet growth doesn’t mean keeping every client. With one accountant planning to retire within six months, Matt is considering downsizing and releasing some nonideal legacy clients. He already reserves most in-person meetings for financial-planning clients and the top 10% of accounting firm clients. In 2025, he spent just 27 days in the office, 17 of them in March.

His advice is to “figure out which clients you really enjoy working with,” then identify needs beyond your current services. That process led him into financial planning because many existing clients needed personal planning, rather than business advisory work.

Selective attention doesn’t mean abandoning everyone else. It means finding other ways to stay connected.

Build connections beyond the annual tax return

For clients Matt sees only once a year, appreciation events help put a face to his name. When retirement or an inheritance creates a planning need, they know whom to call.

The pub party idea came from an earlier food-truck event, where a conversation with a recently retired client led to assets moving into Matt’s advisory practice.

These gatherings also give the team something to enjoy. And, as Rachel noted, a lower-fee client may have friends or relatives who become excellent prospects.

Marcus described the broader shift as “moving from compliance to community.” Personal touches like handwritten thank-you cards, selected client gifts, and team photos that include family members support that shift.

Financial planning also changes whom you serve. Matt explained that, unlike many business engagements, financial planning often involves both spouses. His mother’s favorite thing about her advisor was the Christmas basket. That’s a reminder that different people notice different forms of care.

Timing helps, too. Rachel found that gifts sent in May stood out more than gifts sent during crowded December weeks.

Those connections bring the conversation back to the firm you want to build.

Build a community you want to belong to

Start with three choices:

  • Share the gains from better work. Give employees room to solve problems and benefit from the results.
  • Make recognition personal. Notice contributions, listen to criticism, and include the people important to your team.
  • Invest in relationships deliberately. Choose clients you enjoy serving, understand their needs, and stay connected beyond deadlines.

Listen to the full episode for Matt’s full conversation with Rachel and Marcus about building a firm around people, not just revenue.

Rachel and Marcus Dillon, CPA, own a national, remote client accounting and advisory services firm, Dillon Business Advisors, with a team of 28 professionals. Their latest organization, Collective by DBA, supports and guides accounting firm owners and leaders with Streamlined OS, an operating system for accounting firms, mastermind groups, and one-on-one advisory.