Bank outlet excess reserve prediction method based on long short term memory recurrent neural network

A cyclic neural network, long-term and short-term memory technology, applied in neural learning methods, biological neural network models, forecasting, etc., can solve problems such as difficult to prepare forecast demand

Inactive Publication Date: 2017-06-23
湖南科创信息技术股份有限公司
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Problems solved by technology

However, the existing research results cannot meet the new requirements for time series forecasting in increasingly complex application scenarios. Mo

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  • Bank outlet excess reserve prediction method based on long short term memory recurrent neural network
  • Bank outlet excess reserve prediction method based on long short term memory recurrent neural network
  • Bank outlet excess reserve prediction method based on long short term memory recurrent neural network

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Embodiment Construction

[0047] The system architecture of the present invention is as figure 1 As shown, it includes three parts: data preprocessing, model training, and bank branch reserve fund forecasting. In the data preprocessing stage, the cash transaction records of the bank outlets are collected, and the transaction records are counted to obtain the daily net amount sequence of the bank outlets. The present invention will construct a feature vector of [month, date, total deposit, total withdrawal, date attribute]. Calculate the mean and standard deviation according to the daily net amount distribution of outlets, use the 99% confidence interval as the prediction interval, and divide the prediction interval into N_CLASS classes, and mark each record as a one-hot vector.

[0048] In the model training phase, the default parameter configuration used is shown in Table 1. As well as weight initialization through small random numbers, activation of tanh nonlinear functions, comparison of predicti...

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Abstract

The invention discloses a method for predicting reserve fund of bank outlets based on long-short-term memory cyclic neural network, which includes three stages of data preprocessing, model training and prediction. The data preprocessing stage takes the day as the unit, counts the total daily deposits and daily withdrawals of cash transactions at bank outlets, and the date attribute of the day, and constructs a feature vector; calculates the daily net amount based on the daily cash transaction records. In the model training phase, the LSTM model is trained based on historical feature vectors and daily net data. In the forecasting stage, the eigenvectors of several days before the forecast date of the bank branch are counted, and the range of the daily net amount predicted by the LSTM model is input, and the random value in the range is taken as the reserve fund requirement of the day. The invention makes full use of the historical data and the advantages of the long-short-term memory cyclic neural network in the time-series data analysis, and effectively improves the accuracy rate of reserve fund prediction at bank outlets.

Description

technical field [0001] The invention relates to a method for predicting reserve funds at bank outlets based on long-short-term memory cyclic neural networks. Background technique [0002] In recent years, with the rise and development of third-party payment institutions, the cash business of commercial banks has been impacted to a certain extent, and the intermediary business of commercial banks bears the brunt. The intermediary business of commercial banks mainly includes payment and settlement, guarantee, commitment, transaction, inquiry, etc., and the payment and settlement business as a traditional medium is the most important part. The financial performance system of commercial banks mainly measures the three indicators of liquidity, safety and profitability, among which the reserve ratio is the key factor affecting the three indicators. [0003] For bank outlets, the reserve fund represents the cash business activity within its radiating range, which is affected by th...

Claims

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Application Information

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IPC IPC(8): G06Q10/04G06Q40/02G06N3/06G06N3/08
CPCG06Q10/04G06N3/061G06N3/084G06Q40/02
Inventor 王建新刘煜董姝婷单文波
Owner 湖南科创信息技术股份有限公司
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