By Ali Hamriti, CEO and Co-founder of Rollee
Today’s market represents both a mix of “traditional” and independent gig workers who have a diversified set of income streams for financial institutions to assess. Current credit scoring systems are not always set up to reflect the full picture of an individual’s dispersed set of income and employment records. As a result, many independent workers find themselves experiencing unequal access to financial services such as mortgages or loans. In fact, the findings in The Hidden Cost of Gig Worker Living report, reveals 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.
The struggle they experience is not because they can’t afford 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. These records are a sign of stability. They can be easily assessed to understand workers’ income and their ability to pay back a loan. Workers who don’t follow these traditional paths are considered by default as someone with a higher risk.
To build fairer and transparent scoring rules, each worker category needs suitable scoring features which best represent their professional behaviours.
Let’s take the example of a Senior Software Engineer switching from full-time employee to freelancer on a freelancer platform such as Malt. Working only during the first and the last quarter of a year with a daily rate of 800 euros can generate a yearly revenue of 96K euros. We all agree that it’s enough to have a (pretty) decent life in Europe. However, if you look at her banking transactions during the summer, you will see… no income at all. Making a loan decision based on the regularity of a gig worker’s income without considering the dynamics behind their activity will inevitably lead to biased decisions. This is due to a number of reasons.
Manual risk assessments
Some financial institutions are still using manual methods to undertake credit risk assessments. With different salary records separated and dispersed from one platform to another, financial institutions do not have the time to manually track down and take into account all sets of data which results in gig workers being denied access to financial services and business being lost in the process.
Unscalable data integrations
Other financial institutions recognise the need to leverage alternative income and employment data through integration with freelance platforms and HR software through public APIs. However, this approach of building integrations in-house can be met by roadblocks and bottlenecks. It requires companies to negotiate directly with platforms to gain direct access to a private API which can sometimes lead to refusals. With dozens of new platforms to integrate with, the approach is difficult to scale. It also requires an investment of resources from the backend, data and DevOps teams all in an effort to drive data-driven decisions to support growth. However, this approach is limited up to a point due to tech complexity.
Striving for scalable integration
To build fairer scoring models that work for all kinds of workers, financial institutions need faster and frictionless access to alternative data points which reflect the solvency of all different categories of self-employed workers. Relying on an external API infrastructure which can integrate automated connections to dozens of income and employment platforms is the key to building data connectivity that can scale across markets and regions. When automation is taken advantage of to consolidate and standardise the data, time-consuming manual processes and the complexity of internal tech team efforts can be left in the past.
Adopting an automated digitised system also empowers independent workers to be the owners of their data – granting permission to share financial data, without completely parting with the data itself. In addition, using a central monitoring system to analyse data ensures greater transparency and reduces the risk of fraudulent activities or data tampering.
Financial institutions know it is a necessity to adapt scoring rules for today’s market of diversified workers. If they find a fast and scalable way to gain access to a worker’s granular professional data like income and activity, their services will become inclusive to all kinds of workers. They’ll also be able to do business in confidence with a growing market which represents the workers of today and the future.
