 |
| Participants in workshops using new technology for risk management and using new technology for increased credit quality. VNS Photos |
ThuHà
HàNộI – The application of artificial intelligence (AI) and data standardization is key to improving Biot Nam's credit quality and bank risk management during our workshop on risk management and credit quality improvement at Hànội on Wednesday.
The event focused on using technology for risk management and improving credit quality with new credit ratings and scoring models.
Phạmtiếndũng, Lieutenant Governor of the State Bank of Vietnam (SBV), said Politburo has issued resolution 57 to promote the country's digital transformation, and the banking industry has applied technology to reduce costs and implement online lending.
“The application of digital technology is an inevitable trend. Banks that do not apply digital solutions to risk assessments, providing credit scores for customers, and paying online loans on technology platforms will be excluded from the game.”
Vice-President and General Secretary of the Vietnam Banks Association, NguyễnQuhαng, told the meeting that ensuring credit security and improving risk management efficiency is an increasingly important factor in the operation of credit institutions, particularly commercial banks, in the context of a volatile economy.
In parallel, the rapid development of technology, particularly AI, machine learning and big data analytics, opens up new opportunities to optimize credit scoring processes, improve risk assessment accuracy, and improve credit portfolio quality.
“For many years, we have witnessed the strong development of Viot Nam's credit risk management tools, from building internal credit rating systems to applying advanced international governance standards in accordance with Basel II and Basel III,” says Hùng.
According to HHUNG, to ensure the safety of the system and meet international standards and practices, SBV has previously issued several regulations related to the internal credit rating system.
Some of the most recent regulations include the Cycle 31/2024/TT-NHNN dated June 30, 2024, the classification of assets in the operations of commercial banks and non-bank credit institutions, and the Cycle 14/2025/TT-NHNN dated June 30, 2025, which regulates the capital ratios of commercial banks and non-bank credit institutions.
Under these rules, credit institutions must develop internal credit rating systems to rank customers as the basis for credit approval, credit quality control and risk provisioning policies that are appropriate for their scope of operation. To date, all large credit institutions have created their own internal credit rating systems.
However, Hùng pointed out that credit institutions' current credit ratings and scoring models still have some drawbacks.
“Many client financial data is not disclosed transparently and clearly except for public companies, but there are few connected information data systems for direct verification,” Hùng said.
“Major data such as taxes, social insurance, customs and communications have not been misused, resulting in incomplete and inaccurate scores and ranking results,” he added.
He goes on to say that some new credit institutions' internal credit rating systems will use only traditional data to assess customers instead of non-traditional data.
Furthermore, the criteria for scoring and valuing clients at some institutions are not strict and do not align with market standards and practices that pose significant risk to credit institutions.
“With the digital economy, changing consumer behavior and diversifying credit types, new methods and tools are urgently needed to assess risks that are fast and reliable. The application of modern credit rating models based on new technology platforms will not only help credit institutions make more accurate credit decisions, but also help them become more competitive and strengthen their management requirements.
During the workshop, Thanh Huyền, a senior expert at International Finance Corporation (IFC), said that ViệtNam's banks are using self-collected data to earn credits. Therefore, the IFC recommended that Credit Information Centres (CICs) be the focus for building a common credit scoring model across the banking industry.
Meanwhile, Nice Info Vietnam CEO Chun Henry Hyunwoo explained that in Korea, a limited number of non-bank defined institutions are currently applying third-party corporate ratings, such as credit risk indexes, to calculate regulatory taxes, but the majority rely on internal R&D models.
In ViệtNam, the R&D framework is not yet fully implemented. At this point, CIC's credit model could play a central role in credit risk assessments and serve as a reliable and practical foundation for both regulatory compliance and day-to-day operations, Chun said.
“Given this context, it is important that ViệtNam banks and financial institutions actively utilize CIC's core and corporate information for the development of future internal rating systems,” he said.
“As regulatory frameworks become clear, agencies may begin to adopt additional models in line with supervision guidelines. When that time comes, additional models such as strategic and regional models may help complement R&D-based approaches in areas that are not fully covered by regulated models.”
According to Chun, during this transition period, all new CICs have established scoring systems and datasets as the basis for daily preparedness decision-making and future model development. – bizhub/vns
