Employment Data API: Taking Credit Scoring To The Next Level
Multiple financial platforms have started to leverage open API solutions to provide better financial services. Stronger consumer demand has encouraged this trend, as fintech companies compete to innovate and meet consumers’ emerging wants. According to a study by Accenture, 80% of consumers are willing to share their personal data in return for more personalized and convenient financial services. A more personalized service simply means knowing customers better to provide what is more fitting for the individual.
Personalization in financial services is seen in many verticals, with a common use case being credit scoring. Credit scoring is a numeric method to measure how likely a borrower is to make payments to the money they borrowed (source: Bankrate). Open APIs allow more connections to data sources, meaning that more inputs are retrieved to generate a more accurate assessment of the borrower. The accuracy of the system no doubt lies within its data sources.
The State of Digital Lending
The digital lending scene may be separated into two major players — banks and fintechs. Banks have a more traditional lending process, while fintechs tend to provide more flexibility. In Southeast Asia, the digital lending market has increased rapidly at a rate of 50% CAGR from 2015–2019 (source: Momentum Works). In Indonesia, for example, the number of lenders and borrowers for digital loans were increasing by 18.3% and 134.59% respectively by the end of 2020, according to Otoritas Jasa Keuangan (OJK), the country’s financial services authority.
Despite the popularity of digital lending platforms, the typical digital lending process is typically more cumbersome than one would imagine. The following is a visualization on how credit scoring process generally works, taking an example from peer-to-peer (P2P) platforms.
One of the main factors that hampers the current digital lending flow lies in the data input. A borrower’s credit score depends heavily on the documentation and information they provide, such as financial statements, repayment activities, payroll, and employment history. Not only is the process manual on the borrower’s part, the process of checking is laborious too on the lender’s part. In addition, information provided is static and may not be up-to-date. No doubt, more connections to additional live data sources becomes a necessity to reduce risk.
The Future With Employment Data API
One of the most important sources of data digital lending providers could have is historical employment data. The ability to validate a borrower’s employment status and cash inflow would allow a lender to determine with more certainty a borrower’s ability to repay their loan.
With Employment Data API, data that was previously only visible within certain institutions can now be accessible to digital lending platforms, with users’ consent. Think about the API as a connector between the said institutions and digital lending platforms, whereas personal data is exchanged between those parties. In the US, Plaid has introduced Plaid Income which provides automated income verification by connecting with users’ payroll provider accounts or by uploading payroll documents. Brick in Indonesia has also recently offered their Employment Data API to help lending platforms to retrieve general employee information, employment status, and salary information, enabling automation and better credit scoring process.
A lot of new opportunities can be harnessed with the Employment Data API. The credit scoring system will produce better output, knowing one’s clients better can help improve repayment rates and thereby lower risks. Personalized financial services will also be more realistically adopted due to the help of open API solutions. Partnership between institutions also acts as the key for open source solutions to be well implemented.
