The rise of alternative data

Work landscape and scoring rules: two-speed acceleration

The uberization of work started with booking a cab on our smartphone and progressed to scheduling a medical appointment, getting lawyer advice or calling a plumber in a few clicks. Gen Y and Millenials adopted those habits in no time, while Gen Z has been raised in a world with frictionless access to those services.

On the supply side, we've seen millions of people leveraging their skills through platforms like Uber, JustEats or Etsy.

Creating your customer network could have taken a long time before, so accessing a high demand close to your space became a formality through those disruptors.

Even if independent workers' rights are still far from expectations, we've witnessed the unemployed starting to work again, people living in small cities accessing new job opportunities, and entrepreneurs growing their business faster with gig platforms.

However, financial institutions still need help understanding those new work habits, which leads to a growing issue of inclusion.

Indeed, they are evaluating workers' financial situations on old criteria that don't consider those new working trends. Having a single source of income and working for many years at the same company was likely what lenders privilege in terms of good applicants. It's still seen as a sign of stability and makes it easier for financial institutions to project workers' income and their ability to pay back a loan.

If workers don't follow those traditional paths, they will be considered by default as someone with a higher risk.

Self-employed people usually increase their revenues and diversify them but they have to wait, on average, three years to gain lenders' trust. At the same time, lenders will welcome a full-time employee after a few months at the same company.

Redefining what stability means

The main reason behind those differences is the historical notion of job stability. Job stability is “a state of certainty about continued employment”.

It, by definition, excludes self-employed people. This notion matches a world where the typical journey is prioritising companies in reluctant industries as a sign of long-term security.

But can we still apply the same stability principles when the work environment dramatically changes?

Indeed, the self-employed boom is not due to a decrease in job opportunities but more to a desire of millions of people to embrace this new work mode. Independent workers don't look for an unstable life; they figure out they can gain flexibility and earn more money by freelancing their skills. The pandemic has accelerated this trend, with people worldwide applying for job opportunities as contractors thanks to remote HR solutions like Oyster, Deel or Remote.

We now have 28 Million self-employed workers in Europe, and there will be 72 Million by 2025 in Africa. Financial institutions are aware of the necessity to adapt their scoring rules to them, and relying on granular professional data like income and activity would be a catalyst for their financial inclusion goals.

The power of Alternative data to include self-employed

Before launching Rollee, I had a comfortable position as a Data Scientist in a corporate-backed Fintech. High salary, good benefits… the perfect profile for traditional banks to lend. After four months of probation, I secured my long-term contract, and my banker encouraged me to buy an apartment and start building my wealth. At the same time, my job was to analyze self-employed profiles and make initial credit rules to score them. My feeling at the moment was pretty weird: we knew our company was struggling, but I was still considered a good profile while I was putting demanding conditions on self-employed who were more likely to succeed than the company I was working for.

The problem lies in something other than the lender's selection process when it starts its activity. It could be understandable (even if I firmly believe it's by losing money that you outperform everyone in the future) as everyone wants to reduce their default risk and privilege profiles that offer the best guarantees.

We were trying to infer stability through banking transactions while powerful and meaningful data points were available elsewhere to assess it.

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. And making a loan decision based on the regularity of their income without considering the dynamics behind their activity will lead to biased decisions.

The skill set, the project's duration, the quality of customers, the workers' demand… those alternative data points are essential to building fair credit scoring rules for all different categories of self-employed. 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 scoring rules, each worker category needs suited features (seasonal income, daily rate…) best describing their professional behaviour.

We consider this mission vital for our society at Rollee. We decided to start removing all friction to empower workers with their professional data through our API. We are committed to improving our customers' decision process step by step for more inclusion.

Improve your customers' knowledge with alternative data.

We are glad to work with disrupting companies that use Rollee to improve their KYC/KYB process, build suited credit scoring rules or better underwrite insurance policies.

Most of our customers integrated several APIs, so our core product perfectly fits their expectations. But as we've gotten more interest from traditional bankers and insurers, it should not be a blocker for anyone to leverage alternative data and refine their decision processes.

That's why we are launching our Dashboard to accelerate customers' knowledge of financial institutions. Risk managers, compliance teams, Data Scientists.. everyone can collect users' consent and access their income, activity and profile data points through more than 40+ working platforms.

This will lead to a better understanding of each applicant's financial situation, and we believe that it will bring more trust between non-traditional workers and financial institutions.

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