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How Farosian data is disrupting the gig economy.

Revolutionizing Recruitment: How Farosian is Disrupting the Gig Economy

The gig economy has transformed the way we work and the way we hire. With an estimated global market value of $3.7 trillion and unstoppable growth – this sector provides a wide range of opportunities to both employers and employees.

What is the gig economy?

The gig economy (like the music industry) is a labour market which is heavily reliant on part-time positions being filled by freelancers or independent contractors as opposed to full-time employees.

Most examples of the gig economy are through online platforms such as Fiverr, Upwork, PeoplePerHour, Toptal, Uber and Glovo, but it can also include people who work through AirBnB, online marketplace sellers, influencers and even consultants.

In some instances, however, organisations hire part-time employees on a larger scale to fill positions for services such as food delivery, courier deliveries, and taxi drivers. The issue here is that these ‘freelancers’ still represent the brand that they’re working for, but they don’t have the same consequences to deal with as full-time employees for misconduct.

What steps can these organisations take to ensure that they’re hiring the best candidates possible, in order to better manage their brand equity and reputation, and improve customer service, performance and profitability, all while reducing the number of complaints, disciplinary action and the time spent training and recruiting new hires?

Although this sounds like a big ask – it’s not altogether impossible. Transforming a business in this way, essentially just comes down to ensuring that you’re hiring the best possible candidates for the positions while removing the potential red flags.

The rise of Farosian’s Ninox Platform

This is where Farosian’s Digital Media Data solution (codenamed operation Ninox) comes into play. The development team at Farosian has been hard at work on this for a few years, and perhaps the biggest challenge we encountered, was communicating the value proposition of this and trying to prove that it works.

For the tech nerds out there – the solution is a first-of-a-kind AI system, which helps automate the process of matching social media accounts, analysing data, and allocating risk scores based on data collected. This allows us to conduct risk assessments and digital media vetting at scale, thus reducing the time and resources needed to accurately conduct the assessment, as well as reducing the cost incurred by the client.

Finally, on the 8th of June 2022, we had a breakthrough and our first opportunity to prove the value of our product for an unnamed international organization working in the gig economy space. The initial pilot test conducted was based on 1000 random data subjects who were currently employed with our unnamed company. The risk scores from their digital media screening reports were cross-referenced with various key performance indicators from their place of employment.

Not even we could have prepared for what the data revealed.

So what happened..?

Firstly, it is important to note that the average risk score from the assessment was 27,54%. For those who are unfamiliar with Farosian’s scoring methodology, it’s important to note that we allocate a risk profile based on the risk score percentage received from the report. These range from Very Low-Risk, Low-Risk, Moderate-Risk, High-Risk and Extreme-Risk. For simplicity’s sake – and in order to conduct this assessment, we separated the data subjects into 2 categories, high-risk and low-risk.

The percentage of individuals who were moderate to high-risk was 17,89% of the total, which means that almost 1 in 5 candidates were underperformers and potentially costing the company from a revenue and reputation perspective.

Negative Content Breakdown

From a negative content perspective, the most common type of negative content was sexual content, closely followed by unprofessional content, illegal activities and sexism. Some of the other kinds of content discovered include homophobia, discrimination, violent content and disclosure of confidential information.

Farosian Cae Study - Risk Profile Statistics

Performance & Reviews

Fortunately, the unnamed organization allows for the review of freelance service providers by customers. This allowed us to compare the number of negative reviews with the risk scores to see the correlations between the number of negative reviews an individual received vs their digital media behaviour.

The results showed that the average number of negative reviews for low-risk service providers was 0.247 per person, while the average number of negative reviews for high-risk service providers was 0.655 per person. This means that the integration of Farosian’s social media screening solution could reduce the number of negative reviews by up to 40,81%!

Farosian Gig Economy Report - Negative Reviews

Hours Worked

The average number of hours worked by low-risk service providers came to a total of 154.92 per person, compared to the average of 126.1 for high-risk service providers. This means that on average, low-risk service providers spend 28.82 more hours online in a month than their high-risk counterparts.

Again, this may not seem like a substantial amount of time, however, when you consider that the company has in excess of 1 million freelance service providers, it becomes a rather large figure.

Farosian Gig Economy Report - Hours Worked

Tasks Completed

A similar trend can be found when making a comparison between the number of tasks or jobs completed by the freelance service provider and their digital media risk score.

Farosian data revealed that the average number of tasks completed by high-risk individuals was 240.43, whereas the number of tasks completed by low-risk individuals was an average of 278.26.  This means that on average, low-risk individuals complete 37.83 more tasks than high-risk individuals per month.

Farosian Gig Economy Report - Tasks Completed


Revenue is perhaps the most critical factor to take into account. High-risk individuals earned a monthly average of R12 775.33 per service provider, while low-risk individuals earned a monthly average of R15 268.50. This equated to a loss of earnings for our unnamed organization of R623.30 per freelancer hired.

Depending on the size of the organization, this can really add up. For the organization in question, this translated to a loss of earnings in excess of R3.7 million per year.

Farosian Gig Economy Report - Revenue

The Outcome

Farosian compiled a full report on this pilot test which was submitted to the unnamed organization. Flash forward a couple of months, and we are extremely proud and excited to say that the Farocity system has since processed an additional 40 361 data subjects, thus saving our valued client in excess of R300 million per annum.

If you are reading this, you’re still early. Like with many technological developments, very few people have fully understood the power and the potential of using digital media data to make more informed hiring and business decisions.

It is our expectation that the incorporation of this kind of data into the recruitment cycle will become the norm in the near future. We are just grateful to be at the forefront of the movement to reimagine the way that hiring and recruitment are done and to be working with such incredible organizations that share the same vision as us.