Lidan Fan
ABSTRACT
Customers of the bank will have demands for various services of the bank. Of course the bank will design different portfolio products to deliver services according to the demand. It deserves to be solved how to find the most appropriate combined service ratio to make the bank's business delivery more accurate and make the bank's service delivery ratio match the number of people using the service in each period. This paper uses the annual interest table data of each product of the bank and combines the portfolio optimization model to study the credit risk and financial planning of bank customers. The results show that for customers with different risk tendencies, the proportion of their investment in deposits and financial products is significantly different. The discriminant model is used to classify product customers and get high quality customers. Focus on the high quality customers with risk preference (10%, 59.15%, 30.85%) and risk aversion (42.72%, 21.34%, 35.94%). At the same time, consider the proportion of deposit, consumption and financial management products to make the forecast. Extracting the data related to the transformation between some account operations, using the Markov process to solve the ratio between products changing with time. The results are as follows: deposit accounts for 39.55%, consumption accounts for 38.57% and financial management accounts for 21.88%. This model establishes the relationship between risk preference and financial products, which has empirical value for the bank's product placement and the identification of customers' service tendency.
- Jinlong Li (2018). Establishment and implementation of customer credit risk management system based on AHP analytic hierarchy process. East China University of Science and Technology.Google Scholar
- Shilong Liu (2020), Same information account money laundering risk identification and control, China Business News.Google Scholar
- Yang Liu and Siyu Xia (2016) Research on GARP Quantitative Stock Selection and Markov Chain Timing Strategy. Finance and Economy.Google Scholar
- Si Shoukui, SUN Zhaoliang. Mathematical Modeling Algorithms and Applications [M]. Beijing, National Defense Industry Press, 2015.Google Scholar
- Zhifeng Liu. Research on pre-loan credit risk management of personal consumer credit of Agricultural Bank of China Binzhou Branch based on customer group characteristics [D]. Shandong University of Science and Technology, 2020.Google Scholar
Index Terms
- Research on the scientific financial planning of bank customers
Recommendations
Credit rating of bank customers and money laundering risk prediction based on pattern recognition: Take Chongqing City as an example
ICCIR '21: Proceedings of the 2021 1st International Conference on Control and Intelligent RoboticsJudging the credit risk level of bank customers can effectively determine whether there is illegal operation of customer account funds, so as to prevent money laundering. Scientific investment portfolio of financial products helps to retain high-quality ...
Asset Reclassifications and Bank Recapitalization During the Financial Crisis
Regulators frequently relax accounting rules during a financial crisis as a means of regulatory forbearance. The new accounting options provide banks with an opportunity for an accrual-based increase in their regulatory capital. The use of such an ...
An Application of Linear Programming to Bank Financial Planning
<P>The fundamental issues facing senior bank management revolve around the structuring of a bank's balance sheet. Since yields can be assigned to each asset category, and costs to each liability category, the profits of the bank can be represented in ...
Comments