Manbang Financial Internet Credit Management Optimization Research

With recent advances in internet technique and artificial intelligence,Internet Finance(ITFIN)brings the financial industry to a new epoch.Therefore,it's extremely significant for financial companies to focus the development on two aspects: i)how to promote the credit management system,and ii)how to increase the market share.As one of ITFIN companies in China,Manbang Group Finance(MGF)also needs to optimize its interior system,in order to obtain a higher market share.According to this,this paper studies the credit management of MG.To this end,we apply several related theories about international credit management,and propose a novel solution according to the research method that combines theoretical analysis and data mining.Accordingly,we summarize the performance of MG's credit management methodology and analyze the factor for such performance.Moreover,we propose an optimization strategy for MG's solution and suggest a set of actions to guarantee successful and efficient executions.As a branch of MG in finance,MGF is one of the 276 certificates holders for internet-loans.MGF utilize the MG internet platform as their basement of providing financial services.Additionally,they also use artificial intelligence and data mining techniques to build the risk management model,where various kinds of data(e.g.,trade data,behavior data,internal and external credit data)have been involved.However,comparing with other well-developed financial companies,MGF is still quite weak and vulnerable in its management system,especially on credit management.This situation is mainly due to that the management system of MGF is mainly in line with other traditional financial management approaches.In contrast,this paper analyses its credit management from three different stages: i)before loan,ii)during loan,and iii)after loan.We find that the credit management of MGF still has three vital issues: 1)the anti-fraud method is out-of-date;2)credit data is insufficient;3)collection loan pattern is inadequate.After formulating this problem,we utilize data mining techniques to enhance anti-fraud performances by effectively detecting fraud behaviors.We also provide a framework that is based on both the traditional approach and the data mining methodology.Further,we utilize the pattern of the internet to boost the ability for collecting the loan.This will help us build a defense system for credit management.Last but not least,we provide strong actions to guarantee the successful execution of such policies.These actions include: building the staff training procedure,creating a professional credit management team,constructing quality supervision mechanism.These actions could fundamentally guarantee an effective and efficient credit management.This paper deeply analyzes the credit management of MGF.According to the advancement of credit management theory and methodologies,we study how to further optimize the credit management of MGF based on the current constraints of MGF.We also propose methods to continually optimize the MGF's credit management efficiency,aiming to make it become state-of-the-art.Simultaneously,this paper would provide a theoretical foundation for other internet financial companies.