Digital fraud is no longer an occasional event, but it has become part of the daily routine of e-commerce.Nethone data shows that suspected fraud attempts remained above 400 million in January and February, indicating that fraudsters continue to target users even when the volume of returns, refunds and complaints is at its peak, making detection even more challenging.

The main focus of these actions is high-value digital commerce, such as e-commerce stores that sell high-priced products and companies in the airline ticket industry. These businesses, because they are more attractive to criminals, are forced to innovate faster than others, becoming laboratories for the latest fraud detection technologies.

What makes a transaction high risk (so-called “high-risk business”) is the association with high value operations, immediate liquidity or large volume.We are talking about:

E-commerce platforms for expensive items such as electronics and premium brand marketplaces that handle products easily resold in the informal market;

Online games and betting, which allow quick movement and multiplication of resources;

Tourism and airline tickets, with high average transaction values and immediate resale potential;

Cryptocurrencies and digital assets, which enable transactions marked by anonymity, liquidity and absence of borders;

Fintech services, where account opening and customer interactions are vulnerable to social engineering and account takeover scams.

Companies with this profile face sophisticated threats on a daily basis, which forces them to raise their security standards and continuously innovate. Those outside this group should pay close attention, because the risks faced by these businesses today tend to spread throughout the market in a short time.

The problems of traditional prevention approaches

The classic response to fraud is blocking based on registration data and transaction history.This is a static model, with clear limitations, such as excessive blocking, which increases the number of false positives and leads to the loss of legitimate customers.In addition, the traditional model does not keep up with the dynamism of attacks, and sophisticated fraudsters already know how to manipulate static data, such as document numbers, addresses and cloned cards.

In the end, blocking too much costs sales; blocking too little generates financial losses. That is why it is so important to include other elements in the equation, such as behavioral analysis. This is already a learning of high-risk sectors, which no longer limit their evaluations to what the user reports, but also analyze how he behaves online.

Some behavioral metrics that have been successfully applied include:

Speed and typing pattern;

Geolocation and discrepancies with billing address;

Use of VPNs or device emulators;

Navigation flow (time on pages, repeated attempts, click paths).

Fraudsters can obtain cadastral data, but it is much harder to consistently replicate a legitimate behavioral pattern.

Artificial intelligence on the front line

The main lesson high-value digital commerce can teach about fraud prevention is that it is never static: it is an ongoing process that requires constant updates in response to evolving criminal techniques.

Every e-commerce, even the lowest risk, should be inspired by this dynamic ecosystem and adopt a proactive stance, since reputation, cash flow and customer relationships depend on the ability to detect and block threats.

The massive use of technologies such as Artificial Intelligence (AI) enables the detection of anomalies in real time, something essential in sectors where decisions need to be made instantly.In addition, these systems have continuous learning capabilities 'WHE models improve as they identify new attack vectors, providing faster and more effective responses to emerging behaviors.

AI has not yet completely replaced human analytics, but it powers anti-fraud teams by automating massive volumes of attempts, creating a combination that makes defenses much more robust.

What all industries need to understand (and fast)

Investments and security strategies of high-risk sectors should be treated as a reference for the entire market. After all, technological evolution happens on both sides & defrauders & IDF and what is being tested today in highly targeted sectors may spread to others very soon.

As the data shows, fraud is increasingly dynamic, no longer restricted to key dates in the business calendar, and it is a mistake to treat it as a “IT IT team issue”.

In practice, this means that bolstering defenses just in November, before Black Friday, is not enough. Even for moderate risk sectors, the way is to prioritize investment in behavioral technology and AI.

Those who closely follow how high-risk sectors deal with fraud are better prepared for the challenges that already knock on the door of the market as a whole 'WHEEL fraud prevention is a business strategy, not just a defense measure.

By Thiago Bertacchini, Head of Sales at Nethone

By Edsel Simas, Chief Technology Officer of Setrion Software and Milldesk

The pressure for leaner margins and increasingly tight deadlines has brought the logistics sector to a turning point. In a market where minutes of delay impact contracts and customer retention, ensuring system availability and standardizing processes across operational units has ceased to be merely a technical challenge—it has become a strategic imperative.

The help desk—often treated as a basic operational function—within this context has become central to the efficiency of the value chain. When structured with technology, automation, and visibility, technical support transforms into a tool capable of reducing costs, avoiding operational losses, and increasing end-to-end control, especially in companies with multiple locations.

Recent data helps to size the impact of downtime on logistics results. According to Gartner estimates, one hour of downtime can cost up to US$42,000 for a medium or large-sized company. And these are not isolated incidents: the same study indicates that companies face, on average, 87 hours of unplanned downtime per year—the equivalent of almost four full days of operation lost.

In the logistics sector, where the availability of warehouse management systems (WMS), transportation management systems (TMS), mobile devices, networks, and printers is directly linked to productivity, each interruption impacts deadlines, contractual penalties, routing, and ultimately, final customer satisfaction.

The complexity of support in decentralized environments

In distributed logistics operations, decentralizing IT amplifies support challenges. Warehouses, branches, urban hubs, and distribution centers operate with diverse structures, distinct user profiles, and varying levels of technical maturity.

In many organizations, support still relies on overloaded local teams or on-site support from headquarters – a model that creates disparities in response times, impromptu resolutions, and, most importantly, a loss of visibility for corporate managers.

According to a 2024 survey by a global ITSM consultancy, nearly 461,000 companies with geographically distributed operations report difficulty maintaining consistent service levels between their units. Without standardization, support becomes reactive, slow, and prone to rework. Tickets get lost, histories aren't consolidated, and recurring problems go unidentified, multiplying silent operational costs.

Centralization and Visibility: What Changes with a Structured Help Desk

Adopting a streamlined, centralized help desk solution is a game-changer for logistics companies. This model unifies technical support on a single platform, allowing all requests—regardless of origin—to follow defined workflows, with standardized categorization, priority levels, and agreed-upon response times.

This type of structure creates an environment where real-time visibility is guaranteed. The IT manager can monitor incidents by unit, affected system, or problem type, identifying trends, bottlenecks, and areas requiring technical reinforcement or training. This continuous monitoring, combined with performance indicators such as SLAs (Service Level Agreements) and SLIs (Service Level Indicators), transforms IT into a data-driven decision center, rather than one based on assumptions.

By centralizing support, the company also reduces dependence on on-site support in remote locations. Incidents that previously required technician travel can now be resolved remotely, quickly, with standardization, and lower costs. Studies show that remote support reduces the average resolution time by up to 30% and operational costs by approximately 50%, especially when combined with automated workflows.

Automation and standardization: the new foundation of efficiency

Automation is a key differentiator of a modern help desk solution. Repetitive, low-value tasks – such as password resets, system updates, or diagnosing known failures – can be performed automatically, freeing up the team to handle more complex and strategic incidents. Furthermore, intelligent workflows ensure calls are routed directly to the correct team or analyst, according to pre-configured business rules.

This model eliminates the need for manual screening and significantly reduces service time. It also avoids unnecessary scheduling and helps improve the internal user experience, providing more agile, accurate, and predictable support.

Another essential resource in this context is the integrated knowledge base, continuously fueled by resolved calls. Each incident transforms into documented learning, accessible not only to the technical team but also to users. This allows the organization to offer dynamic self-service portals and FAQs, enabling simple problems to be resolved by the employee themselves without submitting a ticket. Automation combined with the knowledge base's intelligence reduces call volume and improves average response time, while simultaneously strengthening the autonomy of operational teams.

Data-driven indicators, reports, and decisions

In logistics environments, where every minute counts, operational predictability is a valuable asset. Therefore, another strategic benefit of adopting a structured help desk is the ability to generate actionable reports – with segmented indicators by branch, call type, resolution time, reopening frequency, and impact level.

With this data in hand, IT can more clearly assess which units require infrastructure reinforcement, which systems exhibit greater instability, and where recurring points of failure lie. This visibility not only optimizes investments but also allows the technology area to act proactively, anticipating demands and reducing the likelihood of critical failures.

A 2024 study published by an international service management association showed that organizations operating with well-defined and tracked SLAs were able to reduce the occurrence of critical incidents by 76%. Furthermore, availability of the most operationally sensitive systems, such as WMS and routing platforms, increased by up to 20%.

Financial Impact and Tangible Return

The efficiency and control gains delivered by a well-implemented help desk structure translate into tangible financial impacts. Beyond direct reductions in travel, technical visits, and rework, there's substantial savings associated with decreased downtime.

For example, according to IBM data, companies that adopt automation in support can reduce average incident costs by up to 45%, as well as improve the first-contact resolution rate – a key indicator for containing recurring calls and preserving the productivity of local operations.

Process traceability itself also contributes to risk reduction. In regulated sectors, such as pharmaceutical product transportation or food transport, maintaining a complete history and auditing support is essential to meet legal and contractual requirements. In this context, the help desk becomes not just a support tool, but a strategic component of operational governance and compliance.

Operational efficiency in the logistics sector depends on multiple factors, but few have such a direct—and often overlooked—impact as technical support. In companies with dispersed units, remote branches, and high reliance on integrated systems, investing in an efficient help desk solution isn't a matter of convenience: it's a strategy to protect margins, ensure quality, and scale securely.

By centralizing support, automating workflows, precisely measuring performance, and documenting learnings, IT gains predictability and intelligence – delivering real value to the operation. And in an increasingly demanding market where errors are costly and delays jeopardize contracts, a strong help desk is more than just important: it's a way to ensure the business continues running smoothly, with control, visibility, and confidence.

The recent history of technology is marked by the promise of disintermediation.In the beginning of the internet, there was a belief that the network would allow direct connections, in which artists would talk to their fans without labels, companies would sell to consumers without retailers, ideas would circulate without filters. It was a libertarian, almost romantic ideal of a simpler and more transparent world.For a while, this view seemed to take shape, but reality reorganized around new intermediaries, as powerful as the old ones, although disguised in the form of digital platforms.

Services such as Uber, Mercado Livre, Instagram and Amazon created closed ecosystems that facilitated interactions and transactions, but also established new layers between desire and its fulfillment.They were practical, efficient and often inevitable.The rise of software as a service (SaaS) consolidated a model in which technology presents itself as packaging, where an elegant interface that involves user intention and translates it into action, but in the process, remains a barrier.

Three out of four companies (75%) plan to prioritize SaaS application backup operations as a critical requirement by 2028, a dramatic leap from the 15% recorded in 2024, Gartner said.However fluid an interface may seem, it requires opening an application, typing, selecting, and browsing. Each microdecision represents friction, and the accumulation of these small frictions has become evident.

Currently, we live surrounded by passwords, flows and screens, in a maze of tools that should facilitate, but often complicate. With this, the perception grows that we do not seek applications themselves, but the results they deliver. And if it is possible to reach these results without going through an app, even better. Artificial intelligence is promoting this silent and integrated change, especially by popularizing natural interfaces such as voice search.

In 2025, about 20.5% of people worldwide use this form of research, a slight increase from the 20.3% recorded in the first quarter of 2024, according to Data Reportal data.In addition, the number of voice assistants in use exceeds the global population, reaching 8.4 billion devices in 2025, according to Statista. By merging intention and execution in the same act, AI eliminates the need for explicit interactions with platforms.

Online search already offers signs of this transition, in which a question is typed and the answer appears, without clicks or manual filtering. Traditional search, which required multiple steps, begins to be replaced by direct answers. This is the new disintermediation, not a visible disruption, but a gradual disappearance of tools, and this transformation shifts the role of interface technology to infrastructure.

Soon, tasks such as writing, organizing, translating or planning can be performed the moment the desire arises, without the mediation of visible applications. Technology will become as ubiquitous and silent as electricity or piped water, essential but invisible. This implies that many software and platforms, once central to the digital experience, will no longer have a noticeable form, brand or presence.

The practical consequence is that a significant part of the SaaS ecosystem can become infrastructure and no longer service to the end user. When functionality becomes internal to an automated cognitive layer, the need to access specific tools disappears. For the user, this absence will not be a loss; on the contrary, it will be perceived as a gain in fluidity. Application nostalgia will cease to exist because, in practice, they will dissolve in the flow of tasks.

The impact of this disintermediation on the market is profound. Business models based on user retention on a platform will need to reinvent themselves, because value will reside in the result, not in the way. For companies, this means competing no longer for the most attractive interface, but for the ability to integrate invisibly and efficiently into the life of the user. For consumers, the possibility of a daily life less fragmented by screens and logins, but more dependent on infrastructures controlled by few global suppliers is opened.

The great disintermediation that emerges is neither utopian nor libertarian, as dreamed in the early years of the internet. It is technical, silent and definitive. By shortening the distance between thought and action, artificial intelligence erases the center of the digital experience and relegates the interfaces to the background. In the near future, we will not notice when an application ceases to exist, we will simply move on, as if it had never been part of our daily lives. And perhaps it is exactly there that one realizes that the future has already arrived.