Jerome Eger is the founder and CEO of Smile API. In this article, he shares his thoughts on the data gap and ways to address it.
Seven out of ten people in Southeast Asia are underbanked or completely unbanked. Underbanked means you do not have any access to any financial services beyond your bank account, and unbanked means you don’t have any bank account.
If we look at Southeast Asia, we’re talking about an environment with no data or non-traditional data. Non-traditional data means we don’t have any credit card transactions or credit scores. We do not have anything that covers us as service providers that would enable us to provide them with any financial services they typically need to originate loans.
Almost everyone has a job, and because of the pandemic, many jobs have moved online and have been digitized. That creates a huge wave of data that is quite valuable. This is in the form of e-commerce, government services, data on the cloud, etc. For example, in the Philippines, in the BPO sector, payroll systems moved to the cloud. Delivery platforms carry data about transactions. Data is being collected and processed every minute. Previously, you could not leverage this information to issue a loan to become banked. The pandemic was one of the catalysts that accelerated this kind of data.
Distribution is a major challenge if you want to leverage employment data in the financial services space or beyond. To create some impactful use cases, we have to create some sort of categorization. So, if we consider open employment and structure it by employers, government systems, and gig platforms, we can get identity, employment, and income data. This data, even at an aggregated level, is quite valuable for underwriting purposes and making macroeconomic decisions. There is important information available for background checks, as well. In all these cases, a structure is required.
Considering all this, API design becomes valuable. Different use cases will have different schemas and will have different API structures.
Many use cases apply to employment information, but four cornerstones need to be considered –
Truth – How true is the data? What is the veracity of the data? The data must be true as this is governed by taxation and the government.
Timeliness – Employment data does not change as fast as financial data, but it is still time-bound, and the timeliness of the data is important.
Relevancy – The data needs to be relevant so that loans can be processed based on the data. You want to ensure that the data is not tampered with.
Availability – The data should be available when required.
Common use cases
Buy now, pay later –
- Get access to alternative data for credit scoring and credit decisions
- Proof of income and employment
Personal Lending –
- Streamline work and income verification
- Minimize risk of credit defaults and non-performing loans
Earned Wage Access –
- Easy access to third-party employment and income data
- Apply consistent standards for assessing creditworthiness
Smile API does not directly access the data. It provides tools to access data. So, you would need an SDK that we will integrate into the client’s app. This SDK provides a front-end to the end user that lets them give them consent. The consent has to be very clear and concise. It needs to detail what kind of data is being accessed and for what purpose so there is a legitimate cause for that. After encrypted login authorization, data can be accessed. We can see sophistication on the client’s end. Now, we need to see sophistication on the user’s end. Earlier end users were reluctant to share this data, but now, when they see the usefulness, they are more willing to share. This is an exciting time, specifically for APIs in the employment space.
