LegalTech can be a transparent and scalable business. This article highlights how IT innovations, with a focus on data and automated scoring systems, can form the basis for creating transparent and scalable platforms for the creator economy. And creators will be able to reach a new level with their help, monetize their skills, and build a large business.
Creative economy growth
The creative economy is one of the youngest and fastest-growing sectors of the global economy. Culture and creativity account for 3.1% of global GDP and 6.2% of all jobs. It's a global force with a turnover of 50 billion that is changing how brands build loyalty, attract attention, and grow their businesses.
The creative economy is based on the exchange of goods produced through intellectual activity. In such an economy, the central goods are content, breakthrough technological solutions, music, and online video streams. Today, the creator economy is primarily associated with digital and creative professions, media, and influencer marketing. Its participants included creators of creative products, buyers—content consumers, and intermediaries who connect the former and the latter—platforms.
For example, one such content consumption organizer is Patreon, a crowdfunding platform that allows content creators and artists to fund their work. Musicians, journalists, writers, podcasters, photographers, and artists earn money through Patreon. Each author sets the price for their content.
The number of interested individuals and companies participating in the industry's development is also constantly increasing. Advertisers and investors who fund the development of applications for creating and processing photo, video, and audio content are appearing there. The interest of venture investors makes this market legitimate and prevents its contribution to the economy from being ignored.
Goldman Sachs forecasts that the creative economy could nearly double in size to reach $480 billion by 2027, according to the 2023 "Creator Economy Report." According to Statista, there are currently over 200 million content creators worldwide, ranging from professional digital entrepreneurs to part-time hobbyists. The global creator economy is expected to reach $528 billion by 2030, with an average annual growth rate of 22.5%.
A barrier to trust
Platforms are working on improving their algorithms, user-friendliness, and other engaging features to increase screen time. But one of the most important barriers and challenges to the large-scale development of the creator economy is the issue of trust in creators. The market is still forming, and therefore the main risk becomes the illusion of reliability. A person might look like a successful creator—with a high rating and dozens of reviews—but it could all be the result of fake engagement or bots. For now, it's a market with enormous potential, but without established filtering and evaluation mechanisms. The second risk is the lack of a standard: trust is calculated differently on different platforms, and there is no universal "language." This hinders both the creators themselves and the clients.
Let's discuss typical user problems that arise when interacting with creators on platforms. The main complaint is that expectations don't match reality. The creator beautifully presented the offer but then disappeared. Or they completed the order, but late and without communication. Or everything is formally done correctly, but you get the feeling the person is slacking off. This isn't a market bug; it's its nature: millions of people with varying levels of responsibility, without a single certification, work directly. Therefore, the platform's task is not merely to provide a marketplace but to safeguard transactions: to highlight risks in advance, guide creators to operate sustainably and transparently, and enable clients to make informed decisions.
This issue can be addressed by creating an infrastructure where trust doesn’t need to be reinvented for each transaction. When metrics are transparent, they are easier to maintain, and violations can be detected early. Such a product can be developed by leveraging solutions from other industries, such as fintech.
The experience of developing scoring software for litigation financing can be adapted to create software that ensures trust for creators. Scoring software is characterized by the absence of standardized data: each case is unique, and each participant has a different level of integrity. The behavior of the parties, timelines, and even the style of interaction during the process help build clear metrics and filter participants based on relevant parameters.
The scheme is similar in the creator economy; a classic credit history doesn't work there yet either: there are no centralized profiles or verifications. And here too, you can work with chaos and build algorithms in an environment where trust is a scarce resource. As soon as a clear evaluation system is introduced, the participants themselves start behaving more transparently. This is the observer effect, and it works in any economy—from the judicial to the creator economy.
To draw a parallel, in the litigation model, trust is a probability: will the plaintiff win, or will the defendant pay? In the Creator Economy, trust is about someone’s willingness to give you their money, time, and expectations. Here, not only reputational and behavioral signals matter, but also emotions, communication style, and even alignment of expectations. Litigation scoring was about risk management. Scoring in the creator economy is about micro-decisions made every minute: to buy or not to buy, to trust or not to trust. In this project, it’s crucial to design a model that can provide real-time signals.
What metrics should be used to evaluate a creator?
Objective data in the creator economy is digital behavior: how often a creator takes orders, how many they complete, how quickly they respond, and how well their descriptions match the outcome. It's important not just to count likes but to look at the dynamics—for example, is repeat ordering increasing, and is the cancelation rate decreasing? As with litigation funding, context is crucial: the same indicator can signal reliability in one category but indicate risk in another. Therefore, it's important to build models that consider both the "raw" indicator and its behavior over time.
To competently and effectively assess a creator's reliability, it is crucial to focus not on their popularity but on reliability in action. Reliability is not a five-star rating in a profile but rather practice manifested in the moment. And it is precisely this that we must learn to discern and translate into a clear signal. To achieve this, the system should account for the percentage of successfully completed orders, response speed, alignment of the outcome with the description, the proportion of returns and conflicts, repeat orders from the same clients, and behavioral stability over time. For instance, consider how the client support process unfolds: how the creator explains, clarifies, and proposes solutions. Additionally, reputational inertia is how the creator conducts themselves following a negative experience—whether they rectify it or ignore it.
It is crucial that scoring be contextual—evaluating a creator within the framework of a specific category, rather than against a universal scale. What matters for a tutor is not equivalent to the criteria for video editing. The model must be flexible, adaptive, and as closely aligned as possible with real user experiences.
Many people hesitate to engage with platforms and creators due to concerns over manipulations and artificial inflation of likes, followers, and reviews. In my view, to foster a trustworthy environment, it is essential to deliberately eschew metrics that exert pressure on creators and incentivize simulated activity. Likes, views, and followers are superficial signals, and we do not make them pivotal for user decision-making.
It is important to design the mechanics in such a way that the influence of anonymous users on reputation remains minimal. Reviews and ratings carry weight only when backed by genuine interactions: an order, payment, and completed session. This significantly diminishes the incentive to "inflate trust" without substantive work. Moreover, it is vital that all activity undergo behavioral verification. We monitor temporal patterns, action repeatability, and account interconnections. If activity appears artificial, its weight in the system is automatically diminished.
How scoring helps develop platform businesses
By 2026, the creator economy will become much more applied and professional. The market will grow due to the elimination of intermediaries, integration into the everyday economy (not just content, but also services), the emergence of specialized platforms, and new payment solutions. People will earn not only through their audience but also through their skills: from training to services.
In this case, transparency will not just be an option but a currency of trust. This means that trust will become not just a comfort factor but the main tool for monetization. This means that effective infrastructure and ensuring trust on the platform are crucial steps for the development and growth of the platforms themselves.
Platforms that simplify access to information and establish predictable rules are winning. Therefore, we see demand for solutions that make trust measurable. It's not just about technology; it's about the business model: when the user feels comfortable, they stay, and when they don't, they go to messengers. It is trust, packaged in technology, that will determine with whom deals are made and to whom money, attention, and time are entrusted. Without this, no marketplace will take off.
Objective metrics allow for the creation of an environment where it is advantageous for a creator to be consistent rather than a "flash in the pan." This changes the very motivation: instead of chasing likes, the focus is on sustainability, repeat orders, and long-term connections with the audience. When a user understands how the rating is formed, how a deal can be insured, and how the rules work, they act more boldly and frequently. This directly translates into repeat orders and an increase in lifetime value.
The implementation of scoring algorithms can accelerate the growth and scalability of marketplaces for creators, which will be a key factor for growth. As long as decisions are made based on "liking a profile," scaling is impossible. Scoring gives the platform the ability to work with the long tail—tens of thousands of new users who are unknown but can be reliable. This opens the door to growth without relying on large influencers or manual moderation. Furthermore, algorithms allow for personalization of search results and recommendations, which improves the user experience itself. And that means higher conversion rates, higher trust, and higher profitability.