ABC expanding big data for farmer loans
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A booth of Agricultural Bank of China Ltd at a finance expo in Shenzhen, South China's Guangdong province, Nov 7, 2015. [Photo/VCG] |
Agricultural Bank of China Ltd, the third largest commercial lender by assets in China, deepened its application of big data analytics to the online offering of loans without collateral to farmers, to improve risk control and reduce costs.
"We innovate online financing products based on data analysis by digging into our clients' financial assets data, transaction data and credible external data," Zhang Xiuping, general manager of ABC's online finance department, said Thursday. "During the process, we take into consideration characteristics of economic development, the villagers' level of affluence, their credit status and the features of planting and breeding of different regions."
By digging into big data, ABC is able to draw credit profiles for the clients and help them overcome financing difficulties via a model that relies heavily on the client's credibility rather than guarantees for a loan. It has also improved business efficiency and reduced costs by designing automated online workflows, she said.
In Fujian province, the bank did a thorough cost-benefit analysis of the tea industry, collected and analyzed tea planters' operation and transaction data and designed a credit extension model for them. Based on the historical data of 138,000 local tea planters, it generated a list of 58,000 people who are given access to its loans.
Each tea planter household on the list may receive a loan of up to 100,000 yuan ($15,035). The exact quota is decided according to a combination of factors including the planting area, costs of production and operation, a tea planter's credit history and the status of his financial assets, according to the ABC.
Wang Wei, executive vice-president of the bank, said: "We plan to increase our online financing related to agriculture, farmers and rural areas to 500 billion yuan in 2020, from 5.4 billion yuan as of the end of June 2017. It is expected to cover 10 million rural households."
