User Portraits and Investment Planning Based on Accounting Data

This paper develops a procedure to "recover" the missing data of a personal accounting application. The missing data are estimated using a thesaurus matching method and a neural network model. The data sets are split into two parts, the expenditure data and the income data. To estimate the users' missing expenditure data, this paper uses a thesaurus matching method combined with text segmentation technology, successfully reclassifying the accounting data and mining the users' accounting habits. In order to infer the almost vacant income data inversely from the users' expenditure data, a neural network is trained to deduct the relationship between expenditure data and income data, using the income and expenditure sample data of 20,133 households mined from Chinese Household Financial Survey (CHFS) database. The recovered accounting data would be helpful for IT companies in analyzing users' consumption habits and income status, building users' portraits and designing personalized investment products for users. Finally, after dividing users into four categories based on clustering algorithm, the types and quantity of investment products are designed for each group of users to optimize users' asset allocation structures and to make advertisements targeted.