Research Of Credit Risk In Chinese And Vietnamese Listed Commercial Banks Based On Neural Network Model

This dissertation establishes a ratios system to evaluate bankingcredit risk by using the Neural Network method. We analyzed the output tofind the weakness reason and gave some controlled ways to manage the risksituation. The data in this research come from selected listed banks of Chinaand Vietnam. We will analyze the banking credit risk’s reasons and researchto give some policy recommendations due to national financial policies inChina and Vietnam.We have a new approach model–Neural Network to research onevaluating credit risk in Vietnamese and Chinese listed banks. CombiningNeural Network method and information technologies, we can easily takefinal result for each country. After collected their data from their annualreports and run software to find their own model, we can analyze this resultsand give some management methods which can be applied to thegovernment policies makingThis research introduces credit risk evaluation in Chinese andVietnamese listed commercial banking system. Some present situations oflisted banks in China and Vietnam are described. And some challengesfaced by the listed bank in China and Vietnam, some imperfect and lesseffective causes were also shown.In some previous research, the output of model was “0” or “1”, thatmeant “bad” or “good” for assessment situation results, when we wanted toevaluate credit risk ability. Now, we have more levels for situationassessment. Ranked Table with9levels is established and applied toanalyze Chinese and Vietnamese credit risk. It will help us take theanalyzed result, which is closer than realistic situation.In this research, we evaluated the level of listed banks in China andVietnam based on their own database, and compared their assessment ofcredit risk banking system. We also show some weak points in this analysisafter using Neural Network Method and the software named Clementine 12.0through database from Chinese and Vietnamese listed banks. Inaddition, we show the order of China and Vietnam listed banks up to secondquarter2011, such as “Bank of Nanjing Co., Ltd”, is the best of fifteenChinese banks, and the order of four main China banks is Bank of China,ICBC, Agriculture bank of China, and China Construction Bank. In Vietnam,VCB and EIB are the best listed commercial bank with level6,“Medium-grade obligation”, and HBB is the worst listed commercial bank with level-1-“Lowest rated class”. Now, HBB was combined to SHB in August,2012.We also give some suggestions let our assessment be more reliableand logical, such as co-ordinate with national auditor department for gettinga better and more exactly data, investing in high quality human resourceswho will control and apply that system in the effectiveness; supportedinstruments for statistic, investigation, analyzing and make decisions. Inaddition, we can control their tolerance, and push the evaluation position inhigh levels.Through Neural Network method, information technology andlearning machine via historical data, the result comes so fast, logical andreliable. It will give us a general view, and a forecasting ability about ourcredit risk banking system. We can push some effective reactions in duetime and manage a distress in the near future.