12/03/20195min
The real scale of informal employment in developing countries is incomparably higher than the volume of goods and services produced in the informal sector. The longer traditional methods of assessing the real earnings of the population fall behind, the more customers alternative lending can expect, found Robocash Group.
The share of the informal economy in Indonesia stated by the IMF in 2015 amounted to 14.8% of GDP, which was close to the level of many Western countries (e.g., Latvia had 16.6%). However, surveys of BPS-Statistics Indonesia showed that in the non-agricultural sector in 2017, there was 38.5% of the population informally employed in urban areas, and 54.8% in rural areas.
There is a similar situation in the Philippines. According to the Philippine Statistics Authority, in April 2018, 56.2% of all self-employed worked in the service industry and 39.1% in agriculture. The number of family enterprises was 60.9% in agriculture and 30.3% in services.
The utter lack of unemployment benefits across the region facilitates a high level of employment among the population. However, official earnings are often low that makes people look for an additional source of income. In turn, this situation brings forth a common problem for the entire lending industry in developing countries — a qualitative assessment of the real income of borrowers.
The Philippines is a bright example where the underperformance of traditional banking methods has led to a decrease in bank lending and an increase in customers for alternative lending companies using new methods for assessing solvency. According to Bangko Sentral ng Pilipinas, only 0.6% of the adult population had a bank loan in 2017 (2015 — 2.1%). At the same time, there was an increase in the number of clients of microfinance organizations, which include fintech firms providing alternative lending, to 7.6% in 2017 (2015 — 4.7%).
Commenting on the findings, Sergey Sedov, CEO of Robocash Group said, “The experience of Robocash Group in Asia has shown that the use of algorithms based on artificial intelligence and machine learning serves as a bridge between the interests of business and society. We collect and study scattered information about customers to provide them with an opportunity to access to finance and accumulate credit history. In turn, this helps to overcome the paradox when people cannot borrow because of any credit history, which they cannot have due to the inaccessibility of loans. Speaking long-term, the expansion of fintech in Asia will significantly boost financial inclusion and thus accelerate subsequent economic growth by increasing the penetration of financial products into people’s lives.”