Preparing Financial Institutions in Facing the Era of Gig Economy

Brick’s Transactional Data API coupled with Transaction Categorization API allows Financial Institutions to assess gig workers' creditworthiness.

Indonesian workers’ participation in the gig economy has increased over the last couple of years. According to Indonesia’s Central Bureau of Statistics, as of August 2020, almost 7 million Indonesian adults are now freelancers.

Said numbers only include those who reported ‘freelancer’ as their main type of employment which may undercount the real number as it excludes side-giggers, those who engage in freelance work aside from their main full-time employment.

Digital-based platforms such as Gojek, Grab and Shopee gave rise to the gig economy trend. The trend is also further accentuated by the COVID-19 pandemic. The crisis brought upon by the pandemic resulted in a massive layoff, a circumstance that forces them to engage in gig work.

The gig economy absorbs the workforce into on-demand service providers. Ride-sharing platforms such as Gojek and Grab reportedly employ 4 million drivers across the country. The freelance model extends beyond blue-collar workers. Highly skilled Indonesian are using platforms such as Sribulancer, Upwork, and Fastwork to list their service offerings and connect with companies in need of such services.

Gig workers in Indonesia earned around Rp 1,6 million per month on average. However, the numbers differ by education level as well as the type of work being done.

For instance, those with secondary level education (sekolah menengah atas) earn well above Rp 2 million per month on average while those with primary level education (sekolah dasar) earn an average of Rp 1,4 million.

Based on the type of work, white-collar freelancers earned an average of Rp 1,65 million per while grey-collar and blue-collar earned Rp 1,2 million and Rp 1,3 million respectively.

Understanding Gig Worker’s Financial Needs

Unlike traditional employment, gig workers aren’t provided with the tools needed to perform the task at hand. Gig workers operate almost like a business, in a sense that it requires working capital to run.

For a Ride-Hailing app driver, fuel and vehicle maintenance is an out-of-pocket expense. Whereas laptop and software subscription cost is an out of pocket expense for a freelance graphic designer. These new circumstances faced by gig workers blurred the line between consumptive and productive loans.

Despite the fact that gig workers now make up almost 15% of the Indonesian working population, traditional financial institutions have yet to adapt.

Getting a loan to purchase a laptop or a smartphone is still categorized as a productive loan and is financed as such, while in this new reality of the gig economy, it should be treated as working capital loans.

Even if gig workers would agree to categorize such expenses as consumptive, they would still struggle to get access to credit. For instance, credit card applications still require applicants to provide employment information which is an obvious blocker for gig workers to apply. Hence it is unsurprising that a significant portion of gig workers are underbanked.

Designing Financial Services for Gig Workers

Credit scoring is an essential predictive tool to assess a borrower’s ability to pay back loans and consequently manage risks.

However, when financial institutions rely on the old age credit assessment criteria (such as proof of employment) they risk losing 15% of the market. Hence, a new model needs to be developed to cater to the rising gig workers population.

Just like any other predictive modeling, it can always be improved with enhanced data points. The model should measure one’s creditworthiness by including alternative data points such as transaction history.

By looking into borrower’s transaction history, a credit scoring model could identify borrower’s:

• Income
• Liabilities
• Spending patterns

How can Brick help?

Brick can help financial institutions retrieve borrower’s transactional data straight from their financial account.

On top of that, Brick also provides automated transaction categorization capabilities with machine learning allowing lenders to understand borrowers’ income, liabilities, and spending patterns.

Interested in learning more about Brick’s product? Visit our website to find out more.