Financial object abnormal factor screening analysis method based on failure response code

Abstract

The invention discloses a financial object abnormal factor screening analysis method based on a failure response code, and the method comprises the following steps: S1, selecting one of a plurality ofgiven factors affecting the quality of financial transaction data to acquire different object information under the factor, dividing all the failure response codes in a preset period to each object;S2, selecting one of the objects as a target financial object for screening analysis, and taking given remaining factors except the selected factors as factors of the target financial object; S3, dividing the failure response code under the target financial object into each factor of the objects, counting frequency information under the target financial objects, and calculating related frequency information according to the frequency information; and S4, defining the chi-square value of each failure response code in each factor, and obtaining the aggregation degree of the failure response codes according to the chi-square values. According to the invention, accurate abnormal factors can be obtained through screening and analysis, so that the reason of transaction failure can be quickly determined.

Description

technical field [0001] The invention relates to financial data analysis, in particular to a method for screening and analyzing abnormal factors of financial objects based on failure response codes. Background technique [0002] As far as today's society is concerned, mobile payment has become the mainstream. How to submit transaction success rate and enhance user experience is a problem that payment companies, related merchants and banking institutions need to pay attention to. Payment is a long-link transaction activity with many participants, including: cardholders or payment users, merchants, acquirers, card issuers, Nets Union or UnionPay and other related institutions. The factor causing transaction failure may be a single factor, but It is more a combination or superposition of multiple factors, and many failure factors cannot be judged simply by one point. In addition, the success rate of current transactions is very high. Generally, the overall transaction success r...

AI Technical Summary

Problems solved by technology

Payment is a long-link transaction activity with many participants, including: cardholders or payment users, merchants, acquirers, card issuers, Nets Union or UnionPay and other related institutions. The factor causing transaction failure may be a single factor, but It is more a combination or superposition of multiple factors, and many failure factors cannot be judged simply by one point

In addition, the current transaction success rate is very high, and the overall transaction success rate is generally around 99%. In the face of massive payment transaction data, how to find and discover the problems and reasons for transaction failure is very difficult.

[0003] For failed transaction data, the failure response code fed back by the financial system can generally be used to determine the cause of transaction failure. However, some failure response codes currently involve multiple factors. In this case, a clear analysis result cannot be obtained, which is not conducive to rapid identify outliers

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