Finance software has become increasingly effective at showing companies where their money went. Dash.fi CEO and founder Zach Johnson thinks the next useful application of AI is determining whether some of that money should have been spent at all.
Dash.fi recently expanded its financial platform with AI-powered audit agents designed to examine advertising, shipping, and AI expenses for billing discrepancies and potential savings. Its latest focus is digital advertising, where the company is developing a click fraud agent for ecommerce businesses spending heavily on Google and Meta.
“Finance teams should be asking, ‘If we saved 3-6% of our advertising spend, likely one of our biggest line items, how much would that impact our profit,” said Zach Johnson, Dash.fi CEO. “There are a few ways to do this: a reliable cash back card for advertising, an audit agent that cleans up your ad targeting and an ad credit recovery tool that catches ad platforms when they slip up on their own terms. Even as ad platforms push advertisers from credit cards to invoice payments, it's still possible to get 1% back on those payments. Regardless of which tools you use, there's no reason to leave that money on the table when it could fund your expansion or flow directly through to profit.”
Click fraud is an established problem in digital advertising, but addressing it can require more operational work than many ecommerce teams are prepared to handle. Businesses may need to compare billing records with traffic data, identify suspicious patterns, maintain suppression lists, and pursue potential credits with advertising platforms.
At smaller and midsize ecommerce companies, the same operators watching customer acquisition costs and return on ad spend may also be managing inventory, cash flow, and growth. A discrepancy can remain untouched because investigating it costs more attention than the team can spare.
Dash.fi's click fraud agent is intended to automate more of that analysis. The system examines advertising billing and click data to identify potentially wasteful traffic and areas where a company may be overpaying. According to Dash.fi, its agent may identify potential savings equivalent to 3% to 6% of advertising costs, while advertising refunds may recover an additional 0.5% to 1%.
The company bundles the audit capability with its corporate card rather than charging a separate software subscription. According to Dash.fi, eligible customers may receive 3% cash back on Meta and Google advertising spend, while the company may retain a percentage of eligible advertising credits it helps recover.
Johnson believes that structure points toward a useful model for specialized AI agents because the outcome is relatively easy to measure.
“Reducing ad spend comes down to catching bad clicks, targeting errors and billing mistakes,” Johnson said. “But ad platforms are extremely dynamic and spend leakage is constantly shifting. Even if an ad manager catches everything today, tomorrow will bring new forms of waste. That's why an AI audit outperforms more conventional methods; AI can keep up with the constant flux in these platforms.”
That is a meaningful distinction as enterprise software companies look for practical uses of agentic AI. General-purpose assistants can help employees summarize documents or generate content, but measuring the financial return from those tasks can be difficult. An audit agent begins with an existing expense and searches for a defined economic problem.
Advertising presents an additional complication because poor traffic can affect more than the initial charge. Meta and Google's advertising systems use performance signals to optimize campaigns and audiences. Dash.fi argues that repeated low-quality or fraudulent clicks can contaminate those signals, contributing to weaker targeting and higher customer acquisition costs over time.
“Founders tend to see click fraud as just a billing problem, but bad traffic can also become a data problem,” Johnson said. “If an ad account starts learning from signals you never wanted, the cost can compound out of control.”
Dash.fi is focusing on ecommerce companies spending at least $10,000 a month on Meta or Google advertising, including businesses ranging from roughly $500,000 to $100 million in revenue. These companies can have significant acquisition budgets without the dedicated financial or advertising audit teams available to larger enterprises.
The click fraud agent sits within a broader Dash.fi platform that combines corporate cards, spend controls, expense management, bill pay, vendor management, and business checking. The company launched its expanded finance platform in June with audit agents focused on advertising, shipping, and AI services.
Each category presents a similar data problem. A shipping charge has more meaning when examined alongside parcel and carrier information, while AI expenses increasingly depend on model usage and changing pricing structures. Dash.fi is betting that specialized agents can connect financial records with the operating data behind an expense and continuously look for anomalies.
“Visibility was the first generation of spend management,” Johnson said. “AI gives the system a chance to become more proactive. It puts you back in control of your ad spend.”
The approach still depends on the quality of the underlying data, and identifying suspicious advertising activity does not guarantee that Google or Meta will approve a refund. Dash.fi's stated savings will also vary according to each company's traffic and spending profile.
The larger test is whether finance teams are ready to give AI systems a more active role in controlling costs. Dash.fi is starting with categories where waste can hide inside large volumes of transactions and operational data, and where the output of an agent can be evaluated against a relatively simple measure.
For Johnson, that is where AI in finance becomes more useful.
“The impactful agents will be responsible for an outcome,” he said. “Our job is to find the waste, show the customer where it is, and help recover the money.”
Dash.fi bets that finance software should no longer stop after correctly categorizing a transaction. With enough operational context, an AI agent can begin examining whether the expense made sense in the first place.
The information provided in this article is for general informational and educational purposes only. It is not intended as legal, financial, or professional advice. Readers should not rely solely on the content of this article and are encouraged to seek professional advice tailored to their specific circumstances. We disclaim any liability for any loss or damage arising directly or indirectly from the use of, or reliance on, the information presented.
VentureBeat newsroom and editorial staff were not involved in the creation of this content.