High-cabinet personnel scheduling prediction method based on bank outlet business data

Abstract

The invention discloses a high-cabinet personnel scheduling prediction method based on bank outlet business data. The method comprises the steps of obtaining first historical data before an outlet ends a first time node; establishing a prediction model, and based on the first historical data, generating scheduling data required by prediction of the network points in the period from the first timenode to the second time node; acquiring second historical data before the website stops the second time node; establishing a calculation model, and generating actually required scheduling data of a network point between the first time node and the second time node based on the second historical data; and modifying parameters of the prediction model based on the difference between the actually required scheduling data and the actually required scheduling data. According to the method, the number of high-cabinet scheduling people in each website within several days is predicted, the labor cost is saved, and the efficiency is improved.

Description

technical field [0001] The invention belongs to the field of bank scheduling, and in particular relates to a method for predicting high cabinet staff scheduling based on bank outlet business data. Background technique [0002] A teller is a general term for those who work at the counter in a bank. The front desk tellers are responsible for direct-to-customer counter business operations, inquiries, consultations, etc.; the backstage tellers are responsible for business transactions that do not need to face customers, such as joint banking, bill exchange, and internal accounting, as well as review, confirmation, and authorization of the front desk business. deal with. [0003] The high cabinet can be simply understood as a cash business, including personal savings, company cashiers, etc. The high cabinet is mainly for cash business, mainly for individual customers, such as deposit and withdrawal, peer remittance, card application, online banking, loss report and other busine...

AI Technical Summary

Problems solved by technology

[0012] It is time-consuming and labor-intensive to count the business volume and other data of the day to estimate the number of future shifts every day, and there may be cases of forgetting or data errors

Manual statistical data is prone to errors, which will affect the number of people scheduled for the next day. If some emergencies occur, they cannot be dealt with well, which will lead to doubts about the accuracy of manual scheduling, and will cause resistance to it over time

[0013] Since we want to use past data to predict the future, if we only use the data of the previous day or the previous few days to be random, we may encounter holidays and other situations, and we cannot dig out business rules for a long time in the past

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