How to reduce human errors in FinTech with automation and precision

May 19, 2025

Automation and digitalization of processes seem to be standard in modern business today. Especially in innovative and strictly regulated markets like FinTech. But it turns out that even here, manual processes continue to create vulnerabilities that can lead to substantial financial losses, compliance violations, and damaged customer relationships. And in a moment, we'll show you real-life examples of such disastrous errors.

But we also have good news: by implementing strategic automation and emphasizing precision across operations, FinTech companies can dramatically reduce these risks while improving efficiency and service quality.

Keep reading to learn what needs to be done to achieve these goals!

The impact of human error on FinTech operations

The Latin saying states “Errare humanum est” (to err is human). And despite the passage of millennia, it remains relevant. We can even say more: in times when automation, robotization, and digitalization of processes play the first fiddle, it is often human errors that prove to be the greatest risk factor for business and generate the greatest losses. How large are they? According to some studies, they reach globally even the equivalent of €17 billion annually. These losses are related to inaccuracies in reporting, compliance breaches, and operational inefficiencies. As you can see, these aren’t just abstract numbers; they represent real-world failures that have devastated even industry giants.

Let’s look at some examples. Consider Airbus, which lost sensitive vendor data in 2023 after an employee installed unauthorized software, enabling attackers to leak 3,200 partner details. More recently, CrowdStrike’s 2024 security breach, caused by human oversight, led to a $25 billion market loss and inflicted a $500 million operational hit to Delta Air Lines alone.

The banking sector faces particularly high stakes, with manual processes in risk management generating error rates of 1-5% that escalate dramatically as data complexity increases. Do these seem like small numbers? Not at all. In reality, these are cascading risks, including substantial regulatory penalties and eroded customer trust that can take years to rebuild.

What makes human error particularly dangerous in FinTech is its unpredictability. Unlike system failures that can be systematically identified and corrected, human mistakes vary widely in nature and timing, making them difficult to anticipate and prevent without comprehensive solutions. And one thing is certain: in business, especially in such a demanding market, unpredictability is the last thing you want.

How automation can minimize risk in FinTech?

But relax: you don’t have to accept that a certain percentage of actions taken in your company carries the risk of making a tragic mistake. The answer is obvious: to avoid human errors, you must make as many processes as possible independent of human interference. How do we do this? Of course, we’re talking about automation and advanced IT processes that you must design properly.

How effective is this action? According to Feathery Workflow Automation Statistics, fintech automation improves efficiency by 40-60% and reduces manual errors by up to 90%, particularly in processes like invoice management and payment processing. According to the Klippa Accounting Automation Guide, AI-powered tools improve accuracy by 90%, minimizing compliance risks and errors in accounting processes such as invoice validation and data extraction.

The most effective automation implementations focus on high-volume, rule-based processes where human error is most likely to occur. Automation can enter many areas of daily operation in FinTech and eliminate the most common sources of accounting errors. Most importantly: with the right technology partner, you will see the effect almost immediately.

The role of precision in enhancing FinTech accuracy

In serious matters like activities in the financial market, one thing is certain: precision is king. Its lack means not only lower efficiency and possible increase in operational costs, but above all potential problems with the regulator. And that’s certainly something you don’t want to allow yourself.

Okay, we already know that you won’t achieve precision by relying on manual human work. But that’s not all: you won’t achieve accuracy either. And distinguishing between them is very important. This issue is addressed by Manfred Gilli and Enrico Schumann in the article Accuracy and Precision in Finance: while precision refers to exactness in calculations, true accuracy means delivering results that reliably solve real-world problems.

Modern FinTech operations achieve this balance through sophisticated modeling approaches that prioritize practical utility over artificial exactness. Heuristic methods in portfolio optimization achieve sufficient precision while solving more accurate models than rigid numerical methods.

An example from real life? Here you go: PayPal has implemented machine learning models, such as Gradient Boosting Machines (GBM), to detect fraud in real time by analyzing vast datasets. This approach has significantly improved fraud detection and reduced transaction losses to 0.12% of total payment value, far below the industry average of 1.86%.

The financial industry’s most successful implementations recognize that data accuracy in fintech matters more than computational precision. The most effective FinTech solutions therefore emphasize robust, adaptable models that maintain accuracy across changing market conditions.

Automating repetitive tasks to free up resources in FinTech

What is one of the most important problems with manual tasks? They take a lot of time, consume a lot of money, and simply tire employees. Therefore, one of the most significant benefits of financial process automation is the reallocation of human capital. What powers can be unlocked?

According to Smartsheet, workers estimate they could save six or more hours per week—almost a full workday—if repetitive aspects of their jobs were automated. Nearly 72% of surveyed employees stated they would use the saved time for more valuable and strategic tasks.

By delegating repetitive tasks to automated systems, FinTech companies not only reduce errors but also address employee burnout—a significant contributor to human mistakes. Staff freed from monotonous data entry can instead focus on exception handling, relationship building, and innovation, areas where human judgment adds genuine value.

Everyone benefits, and above all, efficiency and effectiveness increase, which we wrote about in the previous paragraph. Great example: Valley Bank slashed anti-money laundering (AML) false positives by 22% using AI, allowing analysts to focus on genuinely high-risk cases rather than processing routine alerts. And this is just one area of financial institution operation. Imagine how much you can gain by focusing on centralized systems and full automation of processes in your company.

Why manual processes are a major source of error in FinTech

Okay, you probably don’t need to be convinced anymore that manual processes are one of the biggest threats to the security of your FinTech and that their automation can significantly affect the security and stability of the company, right?

But let’s repeat once more: manual processes create particular vulnerability in three key areas: data transcription (where information moves between systems), exception handling (where standard procedures don’t apply), and complex decision-making (where multiple factors must be weighed simultaneously).

The average error rate in manual data entry mentioned at the very beginning leads to misreported revenue, compliance fines, and reputational damage—costs that far exceed automation investments.

The most dangerous aspect of manual errors is their potential to go undetected until they’ve caused significant damage. Without automated validation and reconciliation, mistakes can compound silently, creating increasingly severe consequences over time. And if it turns out that they had an impact on breaking regulations? The losses can be multi-faceted and even weigh on the company’s market existence!

Leveraging machine learning to reduce human error in FinTech

There is no doubt: to reduce the risk of human error, you need the latest technologies. Among them, machine learning stands out. It represents the frontier of error reduction in FinTech, offering models that outperform humans in both accuracy and scalability. In this article, we’ve already shown you plenty of examples of how ML-based models have allowed market leaders and challengers to save huge sums.

What’s very important: These systems continuously improve their accuracy over time through exposure to new data, reducing false positives while enhancing security measures. Where can you use them? Let’s take a look at some examples. In credit scoring applications, ML models can evaluate creditworthiness more precisely than traditional methods by analyzing a broader range of data sources, including non-traditional indicators. The automation of repetitive tasks like data entry, customer service, and transaction processing not only increases operational efficiency but significantly reduces the likelihood of human error.

Financial institutions implementing algorithmic trading systems benefit from ML’s ability to make split-second decisions based on complex market analysis, eliminating emotional biases and execution errors common in manual trading. Tools that combine robotic process automation (RPA) with machine learning capabilities create comprehensive error prevention systems that you can apply in your FinTech!

To summarize: how to reduce human error in fintech requires a multi-faceted approach combining automation, precision tools, and strategic allocation of human resources. By implementing these solutions systematically, focusing first on high-risk processes with clear error patterns, your FinTech company can dramatically improve accuracy while reducing operational costs.

For FinTech leaders looking to reduce human error, the question isn’t whether to automate, but how to implement automation most effectively for their specific operational challenges. By focusing on the areas where human error is most costly and implementing precision-focused automation solutions, you can see positive results almost immediately. Of course, you’ll need proven advisors with experience in the FinTech sector. Combining technological and business competencies is the best recipe for success in the financial market!

RITS

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