The Rise of Alternative Data Makes for a Fairer Gig Economy

Independent workers are a swiftly growing group of employees who contribute a substantial £20bn to the UK economy each year. Despite this, many of these workers struggle when it comes to accessing financial services and products such as mortgages and loans. 

The struggle that these workers experience is not due to a lack of funds in securing a loan or mortgage, but because the current credit scoring system is not set up to understand and recognise these new ways of working. Having a single source of income and a work history of being at the same company for many years is what financial institutions identify as ‘good’ applicants. Therefore, the workers who don't follow these traditional paths are by default considered a higher risk. 

So, how can we create a system that treats all workers fairly based on an accurate and holistic view of their income and employment data? 

The Current Credit Scoring Bias 

The Hidden Cost of Gig Worker Living report, funded by Rollee, found that 7 in 10 UK gig workers have been denied access to basic financial products such as a loan, despite having a good credit score. 

Unfortunately financial institutions still are evaluating workers' financial situations based on old criteria that do not consider new working trends. Financial worthiness which is based on credit scoring alone simply does not fit in today’s world of work and financial institutions need to find a solution to continue doing business with this growing market without impacting their acceptance rate negatively.

This biassed model does not consider alternative factors which encompass a person’s world of work accurately. A binary view of employment is outdated and is beginning to impact more and more of us. 

Bridging the Gap Using Alternative Data 

Looking to the future, leveraging solutions which lean on sourcing a complete view of income and employment data will help to improve the state of inclusion within the financial services sector. 

Let's imagine an example of a Senior Software Engineer switching from full-time employee to freelancer on a freelancer platform such as Malt. This employee works only during the first and the last quarter of a year with a daily rate of £800, generating a yearly revenue of £96,000. We all agree that this would be enough to have a more than comfortable life in Europe. However, If you look at the employee's banking transactions during the summer, you will see no income at all. A traditional credit scoring system as we know it would unfairly consider this a red flag. Unfortunately, this is exactly what is happening when financial institutions make a loan decision based on the regularity of a person's income without considering the dynamics behind their activity. 

One’s skill set, the duration of a project, the quality of customers, or a workers' demand are all alternative data points which are essential to building fair credit scoring rules for different categories of self-employed or freelance workers. We should not apply the same rules to an Uber Driver, an Etsy Sole trader or a Malt Developer simply because they share a similar working status.

To build fairer and transparent scoring rules, each worker category needs suitable scoring features which best represent their professional behaviours. 

Creating a fair picture of financial solvency 

Financial institutions must adopt a fully digitised and automated process that provides complete visibility and transparency of diverse and dispersed data sets in real-time. With automated access to alternative data points that reflect the solvency of various self-employed worker categories, financial institutions can develop fairer scoring models. By consolidating and standardising data, it also eliminates otherwise time-consuming manual processes, saving time and money while speeding up decision-making. Moreover, a central monitoring system to analyse employee data ensures greater transparency and reduces the risk of fraudulent activities or data tampering. 

Outsourcing Support 

Financial institutions must understand the importance of modifying their scoring criteria to cater to self-employed gig workers. By obtaining detailed information on their income and work history, these institutions can cater to a wide range of modern workers. As independent work becomes increasingly popular, it is essential for financial institutions to serve this growing market of workers.

These solutions not only enhance financial accessibility for workers but also enable businesses to make informed and equitable decisions based on a wider market of professional circumstances.