Research On Expense Reimbursement Behavior Management Of Group A Based On Machine Learning Under Financial Sharing Mode

In the Internet era,cloud computing,artificial intelligence and other information technologies are developing rapidly.The pain points of corporate management under the financial shared service model are gradually different from the needs of the traditional model.The business management problems facing enterprises urgently need to rely on new information technology for optimization and improvement.Expense reimbursement management is a key area of focus for enterprises.It is based on employee behavior problems and uses big data and machine learning algorithms to create intelligent and personalized behavior management optimization programs,which make corporate economic management activities more efficient and risk management more accurate and stable.In order to achieve healthy and sustainable development,enterprises must ensure the scientificity,flexibility and objectivity of management.Through the use of big data mining and machine learning technology,important information with research value can be mined from it.Based on theoretical support,empirical reasoning,and logical methods of algorithm rules,it implements portraits of expense reimbursement,identification of risk motivation,and intelligent early warning under the financial sharing center model.This article takes Group A's expense reimbursement behavior management as the research object.On the premise of literature and case studies,it uses machine learning model method to study how to optimize the management of reimbursement behavior.Firstly,the related theories of financial sharing,expense reimbursement,user behavior portraits,artificial intelligence,etc.were expounded,and the research status of related theories at home and abroad was sorted out.With the help of offline field survey problem collection and data collection,the status of Group A expense reimbursement management under the financial sharing model is analyzed and summarized,and the content of the group's reimbursement management project content,reimbursement behavior process,and reimbursement information system are summarized.Based on the above,the needs of Group A expense reimbursement management are summarized in the picture of problems,identification of fraud,and optimization of risk level early warning.According to the needs,further optimize the design of expense reimbursement behavior management: use K-Means clustering algorithm to analyze the image of expense reimbursement behavior problems,enhance the personalized management of behavior problems;introduce Logistic regression algorithm to characterize reimbursement behaviors with fraud motive Discrimination,make up for the bias of subjective analysis,and ensure the objectivity of the evaluation;use the C4.5 decision tree algorithm to establish an early warning model of the risk of reimbursement behavior,and improve the early warning and control of future violation risks.The following is the specific implementation stage of the optimization of the management of expense reimbursement behavior based on machine learning under the financial sharing model,and the corresponding safeguard measures are proposed.Finally,the full text of the research conclusions and prospects.This article analyzes the problems existing in the management of the group's current expense reimbursement behavior and proposes countermeasures.In view of the pain points of the group's management of the reimbursement behavior,it differentiates from the classification of behavioral characteristics,makes it difficult to accurately identify reimbursement fraud,and cannot predict the risks in advance Existing problems,etc.,summed up the optimization needs of A's expense reimbursement behavior management under the financial sharing center,using machine learning algorithms to service decision-making,and improve the objectivity and accuracy of behavioral portraits,fraud detection,and behavioral risk level early-warning evaluation.Sex.It is hoped to help enterprises have benign behavior management measures,and provide certain theoretical and practical reference significance for enterprises to improve the phenomenon of credit standards.