Financial fraud can threaten any business. It’s an even larger issue for startups — both when it targets early-stage companies and when organization insiders scam investors. A crowded startup space can make these concerns seem increasingly prominent, but new technologies have made them easier to spot.

Businesses and investors have more data at their fingertips than ever. By leveraging artificial intelligence (AI), they can use this data to detect startup fraud before it causes much damage.

Why is fraud such an issue for startups?

Companies with fewer than 100 employees experience just 22 percent of fraud cases but suffer the highest losses from these situations. Even large corporations with over 10,000 workers lose less. It’s also worth noting that while startups may experience the lowest percentage of cases, it is not by a wide margin.

Given their smaller status, any loss hits startups harder than a larger organization. While losing $150,000 is not beneficial in any context, the relative impact is much higher in a pre-revenue company. In extreme cases, the business may not recover.

Startups are more susceptible to financial fraud for a few reasons. Scammers may see them as easier targets, as a newer, less cash-rich business is less likely to have the experience or controls to stop them.

They also provide an ideal vehicle for investment fraud. While venture capital investment has fallen since its 2021 peak, it has still totaled over $170 billion every year since 2020. Scammers may see this as an opportunity to attract investors but keep much of the cash flow for themselves instead of putting it into the business.

Benefits of AI fraud detection

Startup founders and investors must address this issue. However, the proliferation of these scams suggests conventional fraud prevention techniques often fall short. AI provides a more reliable alternative.

Machine learning models are better at noticing subtle patterns in data than humans. They can identify suspicious activity that human experts may miss. Real-world use cases back such claims — the U.S. Treasury recovered $375 million in 2023 by switching to an AI-based detection system.

AI fraud detection also works faster than manual alternatives. Automated systems can alert stakeholders to potential fraud the second dubious trends emerge. These faster responses, in turn, let startups and investors stop the fraud earlier to minimize related losses. Businesses can also do so around the clock without a dedicated antifraud workforce.

Advanced AI technologies do require significant investment in many cases. However, organizations lose 5 percent of their annual revenue to fraud. Preventing these losses will save more than startups spend on the preventive system, justifying the costs over time.

How to implement AI fraud detection

Like any technology, AI fraud detection models require proper implementation to achieve optimal results. Here are some steps startups can follow to use this innovation to its full potential.

1. Select the right model

Startups and investors can find ready-made AI-powered fraud prevention tools or build their own AI solutions. In either case, effective AI fraud detection starts with choosing the right machine learning algorithm.

Studies find random forests — which combine multiple decision-making flow charts — are the most effective at predicting fraudulent transactions. However, other types of fraud may require more nuanced models. Clustering is ideal for identifying unusual behavior, and deep neural networks can analyze a greater variety of factors to catch subtler cases.

The ideal solution depends on the startup’s budget, AI experience, and the kind of fraud they’re most concerned about. Businesses without much AI programming expertise should consult an expert third party to determine what model best suits their needs.

2. Train and deploy the AI model

After selecting an AI model, organizations must train it. This is the process of introducing data and correcting its decisions so it can learn to distinguish between acceptable and suspicious behavior.

The specifics of model training vary depending on the type of algorithm. In all cases, data quantity and quality are crucial. Data management is the most common barrier to AI adoption, so it’s important to address these concerns early. That means collecting enough information to enable reliable results and ensuring it is relevant to the end use, complete, and error-free.

Startups must tweak their models during training until they can consistently achieve desired accuracy levels. Once they do, businesses can deploy the AI solution, monitoring internal accounts or investment figures for fraud.

3. Optimize over time

It can be tempting to assume the work is over at this point, but AI requires ongoing optimization. Positive returns on AI investments take roughly 14 months on average, and that does not include the 12 months it takes to deploy these solutions. AI implementation is a slow process, so businesses must recognize the need to nurture the technology.

As AI fraud detection models encounter more data and new situations, they can adapt to become increasingly versatile. However, they may require adjustments to account for these changes. Startups should monitor their AI’s performance and refine the model as necessary when errors occur to drive long-term improvements.

Similarly, startups must not over-rely on AI. Humans must always have the final say, as AI is imperfect and can suffer from bias or hallucinations. Instead of taking automated alerts at face value, organizations should review each case to verify it before acting.

Startups and investors alike need AI fraud detection

The startup space cannot afford to keep being a hotbed of financial fraud. Both businesses and their investors need more reliable fraud prevention techniques. In today’s world, that means they need AI.

Machine learning is an imperfect but powerful solution. Learning to leverage AI will help startups and their funders increase trust, security, and long-term financial performance.


Zac Amos is the Features Editor at ReHack, where he covers business tech, HR, and cybersecurity. He is also a regular contributor at AllBusiness, TalentCulture, and VentureBeat. For more of his work, follow him on Twitter or LinkedIn.

TNGlobal INSIDER publishes contributions relevant to entrepreneurship and innovation. You may submit your own original or published contributions subject to editorial discretion.

Burnout leads to employee AI use at work