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NPL growth stabilizing at major lenders

Jiang Xueqing
Updated: Sep 1,2017 7:36 AM     China Daily

Signs of stabilization in the growth of nonperforming loans emerged for China’s large commercial banks in the first half of 2017, but banking industry experts remain concerned about the risks associated with existing loans and new types of risk.

The NPL ratios of the four largest commercial lenders by assets all dropped from the end of last year, according to their interim results announcements.

Agricultural Bank of China Ltd recorded the biggest fall among the top four banks by 18 basis points to 2.19 percent, followed by Bank of China Ltd (by 8 basis points to 1.38 percent), Industrial and Commercial Bank of China Limited (by 5 basis points to 1.57 percent) and China Construction Bank Corp (by 1 basis point to 1.51 percent).

Data from China’s top banking regulator show that the NPL ratio of commercial banks stood still at 1.74 percent for three consecutive quarters. The proportion of special-mention loans, potentially weak loans presenting an unwarranted credit risk, to total loans fell from 4.1 percent in the third quarter of 2016 to 3.64 percent in the second quarter of this year.

Wen Bin, chief researcher at China Minsheng Banking Corp Ltd, said: “We could see that the pressure of growth in new nonperforming loans is lessening, and the increase in NPLs will stabilize gradually.

However, he added: “As a large number of companies are carrying out supply-side reform by reducing overcapacity and debt to asset ratios, potential risks of NPL exposure for commercial banks will still arise. Therefore, prevention and control of NPL exposure and disposal of NPLs will remain a major task for banks in the next period.”

As risks will continue to accumulate and new types of risk have started to emerge, Guo You, China Construction Bank’s chairman of the board of supervisors, said on Aug 31: “We look forward to using our new IT system, which was launched earlier this year and reintegrated our management and service models, to improve the risk management ability by digging into big data.”

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