Banking outlet inventory cash holding amount prediction method and device

Abstract

The invention discloses a method and device for predicting bank outlet inventory cash holding quantity, and belongs to the technical field of big data analysis, and the method comprises the steps: collecting and preprocessing the historical data of bank outlet cash flow, and forming a data set; based on the business requirements and the preprocessed historical data, predicting the future bank outlet withdrawal amount and the bank outlet daily net cash flow by using a time sequence ARIMA model; based on the withdrawal amount and daily net cash flow predicted by the ARIMA model, calculating the optimal inventory cash holding amount and the optimal inventory cash holding interval by using a Miller-Orr model; and performing bank outlet inventory cash management based on the optimal inventory cash holding amount and the optimal inventory cash holding interval. According to the invention, based on the Miller-Orr model and the ARIMA model, the optimal inventory cash holding interval of the bank outlet is calculated, and the prediction of the optimal inventory cash holding amount of the bank outlet is realized.

Description

Technical Field

[0001] The present invention relates to a method and device for predicting the amount of cash held in bank branches, belonging to the technical field of big data analysis. Background Art

[0002] With the rapid development of financial technology, banks are increasingly relying on advanced algorithms and models to improve efficiency in their operations and management. Cash management at bank branches is an important part of daily operations. A reasonable level of cash on hand can not only ensure the normal operation of the business, but also effectively reduce holding costs and improve capital utilization efficiency.

[0003] Currently, banks’ control over cash on hand at each branch is based solely on reports from each branch and manual experience, resulting in cash on hand balances at bank branches far exceeding actual usage levels and excessive non-interest-bearing assets, leading to a waste of funds.

[0004] Traditional inventory cash management methods often rely on empirical judgment or simple mathematical models, which lack scientificity and precision. Therefore, it is particularly necessary to introduce a more scientific mathematical model. Summary of the invention

[0005] In order to solve the above problems, the present invention proposes a method and device for predicting the cash holding amount in bank branches, which can calculate the optimal cash holding interval of bank branches and predict the optimal cash holding amount in bank branches.

[0006] The technical solution adopted by the present invention to solve the technical problem is: In a first aspect, an embodiment of the present invention provides a method for predicting the amount of cash held in bank branches, comprising the following steps: Step S1, collecting and preprocessing historical data of cash flow of bank outlets to form a data set, wherein the cash flow includes deposit, withdrawal and transfer transaction records; Step S2, based on business needs and pre-processed historical data, using the time series ARIMA model to predict the future bank branch withdrawal amount and bank branch daily net cash flow; Step S3, based on the bank branch withdrawal amount and the bank branch daily net cash flow predicted by the ARIMA model, the Miller-Orr model is used to calculate the optimal cash holding amount and the optimal holding range of cash on hand; Step S4, performing cash inventory management of bank branches based on the optimal cash inventory holding amount and the optimal cash inventory holding range.

[0007] As a possible implementation of this embodiment, the step S1 collects historical data of cash flow of bank outlets and performs preprocessing to form a data set, including: Step S11, extracting historical transaction data from the bank branch database, the historical transaction data including date, institution, transaction type, transaction amount and historical inventory cash balance, the transaction type including deposit and withdrawal; Step S12, deleting abnormal values ​​and standardizing the data format of historical transaction data; Step S13, based on the processed historical transaction data, summarize the total daily cash withdrawal amount and the total daily cash income amount of bank branches to construct a data set, wherein the total cash withdrawal amount is obtained by summarizing the withdrawal amount of current deposits, the withdrawal amount of time deposits and other cash expenditure transactions, and the total daily cash income amount is obtained by summarizing the deposit amount of current deposits, the deposit amount of time deposits and other cash income transactions.

[0008] As a possible implementation of this embodiment, step S2, based on business needs and pre-processed historical data, uses a time series ARIMA model to predict the future bank branch withdrawal amount and the bank branch daily net cash flow, including: Step S21, establishing a withdrawal amount model and a bank branch daily net cash flow model based on the time series ARIMA model, and using the training set in the data set to train the model, obtaining relevant parameters of the withdrawal amount model and the bank branch daily net cash flow model, and obtaining the trained withdrawal amount model and bank branch daily net cash flow model; Step S22, input the training set data in the data set into the trained withdrawal amount model and bank branch daily net cash flow model to predict the future bank branch withdrawal amount and bank branch daily net cash flow.

[0009] As a possible implementation of this embodiment, step S3, based on the bank branch withdrawal amount and the bank branch daily net cash flow predicted by the ARIMA model, uses the Miller-Orr model to calculate the optimal cash holding amount and the optimal cash holding range, including: Step S31, setting the lower limit of cash on hand at bank branches based on the average withdrawal amount at bank branches in the past year; Step S32, calculate the variance of the daily net cash flow of the bank branch : , in, The monthly net cash reserve of the bank is the total daily cash withdrawal minus the total daily cash receipt. is the average of the daily net cash flow of bank branches each month, is the number of data; Step S33, calculating the conversion cost of the cash on hand at each bank branch based on the escort fee between the bank branch and the vault, the storage fee of the county branch, and the additional fee of the agricultural support branch; Step S34, calculating the opportunity cost of the bank branch holding cash on hand based on the interbank lending rate; Step S35: calculate the lower limit of cash on hand at bank branches and the variance of daily net cash flow changes at bank branches. , the conversion cost of holding cash on hand and the opportunity cost of holding cash on hand are input into the Miller-Orr model, the optimal cash on hand holding amount Q and the upper limit of the cash on hand balance H are calculated, and the optimal holding range of cash on hand (H, L) is obtained: , , Among them, F is the conversion cost of cash on hand held by bank branches, K is the opportunity cost of cash on hand held by bank branches, and L is the lower limit of the cash balance on hand held by bank branches.

[0010] As a possible implementation of this embodiment, the step S4, based on the optimal cash holding amount and the optimal cash holding interval, performs cash holding amount management of bank branches, including: Step S41, when the cash balance of the bank branch reaches the upper limit of the optimal holding range of cash on hand, the excess cash is released through the interbank lending market or the bond market; Step S42, when the cash balance of the bank branch falls to the lower limit of the optimal holding range of cash on hand, the cash on hand is replenished by using the interbank lending market or the bond market; Step S43, tracking the changes in the cash balance of bank branches in real time, and regularly adjusting the upper and lower limits and target value of the optimal holding range of cash on hand.

[0011] In a second aspect, an embodiment of the present invention provides a device for predicting the amount of cash held in bank branches, including: A data collection module is used to collect historical data of cash flow of bank branches and pre-process it to form a data set, wherein the cash flow includes deposit, withdrawal and transfer transaction records; Cash on hand forecasting module, which is used to forecast the future withdrawal amount and daily net cash flow of bank branches based on business needs and pre-processed historical data using the time series ARIMA model; Cash on hand calculation module, which is used to calculate the optimal cash on hand holding amount and the optimal cash on hand holding interval based on the bank branch withdrawal amount and the bank branch daily net cash flow predicted by the ARIMA model, and the Miller-Orr model; The inventory cash management module is used to manage the inventory cash of bank branches based on the optimal inventory cash holding amount and the optimal inventory cash holding range.

[0012] As a possible implementation of this embodiment, the data acquisition module is specifically used to: Extracting historical transaction data from a bank branch database, the historical transaction data including date, institution, transaction type, transaction amount and historical cash balance on hand, the transaction type including deposit and withdrawal; Delete outliers and standardize data formats from historical transaction data; Based on the processed historical transaction data, the total daily cash withdrawal amount and the total daily cash income amount of bank branches are summarized to construct a data set. The total cash withdrawal amount is obtained by summarizing the withdrawal amount of current deposits, the withdrawal amount of time deposits, and the amount of other cash expenditure transactions, and the total daily cash income amount is obtained by summarizing the deposit amount of current deposits, the deposit amount of time deposits, and the amount of other cash income transactions.

[0013] As a possible implementation of this embodiment, the cash on hand forecasting module is specifically used to: Based on the time series ARIMA model, a withdrawal amount model and a bank branch daily net cash flow model are established, and the model is trained using the training set in the data set to obtain the relevant parameters of the withdrawal amount model and the bank branch daily net cash flow model, and obtain the trained withdrawal amount model and bank branch daily net cash flow model; The training set data in the dataset is input into the trained withdrawal amount model and bank branch daily net cash flow model to predict the future bank branch withdrawal amount and bank branch daily net cash flow.

[0014] As a possible implementation of this embodiment, the cash on hand calculation module is specifically used to: Set a lower limit on cash on hand at bank branches based on the average amount of cash withdrawals at bank branches in the past year; Calculate the variance of daily net cash flow changes of bank branches : , in, The monthly net cash reserve of the bank is the total daily cash withdrawal minus the total daily cash receipt. is the average of the daily net cash flow of bank branches each month, is the number of data; Calculate the conversion cost of cash held in stock at each bank branch based on the escort fees between the bank branch and the vault, the storage fees of the county branch and the additional fees of the agricultural support branch; Calculate the opportunity cost of bank branches holding cash on hand based on the interbank lending rate; The lower limit of cash on hand at bank branches and the variance of daily net cash flow changes at bank branches , the conversion cost of holding cash on hand and the opportunity cost of holding cash on hand are input into the Miller-Orr model, the optimal cash on hand holding amount Q and the upper limit of the cash on hand balance H are calculated, and the optimal holding range of cash on hand (H, L) is obtained: , , Among them, F is the conversion cost of cash on hand held by bank branches, K is the opportunity cost of cash on hand held by bank branches, and L is the lower limit of the cash balance on hand held by bank branches.

[0015] As a possible implementation of this embodiment, the inventory cash management module is specifically used to: When the cash balance of bank branches reaches the upper limit of the optimal holding range of cash on hand, the excess cash will be released through the interbank lending market or the bond market; When the cash balance of bank branches falls to the lower limit of the optimal holding range of cash on hand, the bank will use the interbank lending market or the bond market to replenish the cash on hand; Track changes in cash balances at bank branches in real time, and regularly adjust the upper and lower limits and target values ​​of the optimal cash holding range.

[0016] In a third aspect, an embodiment of the present invention provides an electronic device, comprising a processor, a memory and a bus, wherein the memory stores machine-readable instructions executable by the processor, and when the electronic device is running, the processor communicates with the memory via the bus, and the processor executes the machine-readable instructions to perform the steps of the method for predicting the amount of cash holdings in inventory of any bank branch as described above.

[0017] In a fourth aspect, an embodiment of the present invention provides a storage medium on which a computer program is stored. When the computer program is executed by a processor, the steps of the method for predicting the amount of cash holdings in inventory of any bank branch are executed.

[0018] The beneficial effects of the technical solution of the embodiment of the present invention are as follows: A method for predicting the amount of cash held in bank branches according to the technical solution of an embodiment of the present invention comprises the following steps: step S1, collecting historical data of cash flow of bank branches and preprocessing to form a data set, wherein the cash flow includes deposit, withdrawal and transfer transaction records; step S2, based on business needs and preprocessed historical data, using a time series ARIMA model to predict the amount of withdrawal from bank branches and the daily net cash flow of bank branches in the future; step S3, based on the amount of withdrawal from bank branches and the daily net cash flow of bank branches predicted by the ARIMA model, using the Miller-Orr model to calculate the optimal amount of cash held in stock Q and the optimal holding interval of cash held in stock; step S4, based on the optimal amount of cash held in stock Q and the optimal holding interval of cash held in stock, bank branch cash management is performed. The present invention is based on the Miller-Orr model and the ARIMA model, combined with the opportunity cost and conversion cost of bank branches holding cash in stock, and by studying the historical withdrawal and deposit data of bank branches, the optimal cash holding interval of bank branches is calculated, and the optimal amount of cash held in stock of bank branches is predicted.

[0019] Based on the prediction of the optimal cash holdings on hand for bank branches by the present invention, bank branches can make optimal settings for their cash holdings on hand within the recommended reasonable cash holding range with reference to the optimal cash holdings on hand results, thereby changing the traditional decision-making method based on experience. The present invention provides data support and feasibility suggestions for bank cash on hand management, reduces the bank's non-interest-bearing assets, and reduces the bank's operating costs.

[0020] Compared with the prior art, the present invention has the following characteristics: 1. Improve the efficiency of capital utilization: set the upper and lower limits and target values ​​of cash through scientific methods, reduce the idleness of excess cash, improve the efficiency of capital utilization, and increase investment returns; 2. Reduce cash holding costs: optimize cash holdings and avoid excessive capital occupation, thereby reducing the bank's holding costs and improving overall financial benefits; 3. Enhance risk management capabilities: By predicting and managing cash flow through accurate mathematical models, banks can better cope with uncertainty and risks and improve their risk management capabilities; 4. Support decision making: Provide detailed cash flow forecasts and analysis reports to assist management in making more scientific and reasonable fund management decisions.

[0021] A device for predicting the amount of cash held in bank branches according to the technical solution of an embodiment of the present invention has the same beneficial effects as a method for predicting the amount of cash held in bank branches according to the technical solution of an embodiment of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] Figure 1is a flow chart of a method for predicting the amount of cash held in bank branches according to an exemplary embodiment; Figure 2 It is a schematic diagram of the structure of a device for predicting the amount of cash held in bank branches according to an exemplary embodiment; Figure 3 It is a comparison diagram of an optimal holding range (H, L) of cash in stock at a bank branch and its actual cash in stock balance according to an exemplary embodiment; Figure 4 is a schematic diagram of an original sequence of an ARIMA algorithm process according to an exemplary embodiment; Figure 5 is an autocorrelation diagram of an ARIMA algorithm process according to an exemplary embodiment; Figure 6 is a partial autocorrelation diagram of an ARIMA algorithm process according to an exemplary embodiment; Figure 7 The figure is a schematic diagram showing the comparison between a predicted sequence and an original sequence according to an exemplary embodiment. DETAILED DESCRIPTION

[0023] In order to more clearly illustrate the technical features of the solution of the present invention, the present invention is described in detail below through specific implementation methods and in conjunction with the accompanying drawings.

[0024] like Figure 1 As shown, a method for predicting the amount of cash held in bank branches provided by an embodiment of the present invention includes the following steps: Step S1, collecting and preprocessing historical data of cash flow of bank outlets to form a data set, wherein the cash flow includes deposit, withdrawal and transfer transaction records; Step S2, based on business needs and pre-processed historical data, using the time series ARIMA model to predict the future bank branch withdrawal amount and bank branch daily net cash flow; Step S3, based on the bank branch withdrawal amount and the bank branch daily net cash flow predicted by the ARIMA model, the Miller-Orr model is used to calculate the optimal cash holding amount Q and the optimal holding interval of cash on hand; Step S4, based on the optimal cash inventory holding amount Q and the optimal cash inventory holding range, bank branch cash inventory management is performed.

[0025] As a possible implementation of this embodiment, the step S1 collects historical data of cash flow of bank outlets and performs preprocessing to form a data set, including: Step S11, extracting historical transaction data from the bank branch database, the historical transaction data including date, institution, transaction type, transaction amount and historical inventory cash balance, the transaction type including deposit and withdrawal; Step S12, deleting abnormal values ​​and standardizing the data format of historical transaction data; Step S13, based on the processed historical transaction data, summarize the total daily cash withdrawal amount and the total daily cash income amount of bank branches to construct a data set, wherein the total cash withdrawal amount is obtained by summarizing the withdrawal amount of current deposits, the withdrawal amount of time deposits and other cash expenditure transactions, and the total daily cash income amount is obtained by summarizing the deposit amount of current deposits, the deposit amount of time deposits and other cash income transactions.

[0026] As a possible implementation of this embodiment, step S2, based on business needs and pre-processed historical data, uses a time series ARIMA model to predict the future bank branch withdrawal amount and the bank branch daily net cash flow, including: Step S21, establishing a withdrawal amount model and a bank branch daily net cash flow model based on the time series ARIMA model, and using the training set in the data set to train the model, obtaining relevant parameters of the withdrawal amount model and the bank branch daily net cash flow model, and obtaining the trained withdrawal amount model and bank branch daily net cash flow model; Step S22, input the training set data in the data set into the trained withdrawal amount model and bank branch daily net cash flow model to predict the future bank branch withdrawal amount and bank branch daily net cash flow.

[0027] As a possible implementation of this embodiment, step S3, based on the bank branch withdrawal amount and the bank branch daily net cash flow predicted by the ARIMA model, uses the Miller-Orr model to calculate the optimal cash holding amount Q and the optimal cash holding interval, including: Step S31, setting the lower limit of cash on hand at bank branches based on the average withdrawal amount at bank branches in the past year; Step S32, calculate the variance of the daily net cash flow of the bank branch : , in, The monthly net cash reserve of the bank is the total daily cash withdrawal minus the total daily cash receipt. is the average of the daily net cash flow of bank branches each month, is the number of data; Step S33, calculating the conversion cost of the cash on hand at each bank branch based on the escort fee between the bank branch and the vault, the storage fee of the county branch, and the additional fee of the agricultural support branch; Step S34, calculating the opportunity cost of the bank branch holding cash on hand based on the interbank lending rate; Step S35: calculate the lower limit of cash on hand at bank branches and the variance of daily net cash flow changes at bank branches. , the conversion cost of holding cash on hand and the opportunity cost of holding cash on hand are input into the Miller-Orr model, the optimal cash on hand holding amount Q and the upper limit of the cash on hand balance H are calculated, and the optimal holding range of cash on hand (H, L) is obtained: , , Among them, F is the conversion cost of cash on hand held by bank branches, K is the opportunity cost of cash on hand held by bank branches, and L is the lower limit of the cash balance on hand held by bank branches.

[0028] As a possible implementation of this embodiment, the step S4, based on the optimal cash holding amount Q and the optimal cash holding interval, performs cash holding amount management of bank branches, including: Step S41, when the cash balance of the bank branch reaches the upper limit of the optimal holding range of cash on hand, the excess cash is released through the interbank lending market or the bond market; Step S42, when the cash balance of the bank branch falls to the lower limit of the optimal holding range of cash on hand, the cash on hand is replenished by using the interbank lending market or the bond market; Step S43, tracking the changes in the cash balance of bank branches in real time, and regularly adjusting the upper and lower limits and target value of the optimal holding range of cash on hand.

[0029] like Figure 2 As shown, an embodiment of the present invention provides a device for predicting the amount of cash held in bank branches, including: A data collection module is used to collect historical data of cash flow of bank branches and pre-process it to form a data set, wherein the cash flow includes deposit, withdrawal and transfer transaction records; Cash on hand forecasting module, which is used to forecast the future withdrawal amount and daily net cash flow of bank branches based on business needs and pre-processed historical data using the time series ARIMA model; Cash on hand calculation module, which is used to calculate the optimal cash on hand holding amount Q and the optimal cash on hand holding interval based on the bank branch withdrawal amount and daily net cash flow predicted by the ARIMA model and the Miller-Orr model; The inventory cash management module is used to manage the inventory cash of bank branches based on the optimal inventory cash holding amount Q and the optimal inventory cash holding range.

[0030] As a possible implementation of this embodiment, the data acquisition module is specifically used to: Extracting historical transaction data from a bank branch database, the historical transaction data including date, institution, transaction type, transaction amount and historical cash balance on hand, the transaction type including deposit and withdrawal; Delete outliers and standardize data formats from historical transaction data; Based on the processed historical transaction data, the total daily cash withdrawal amount and the total daily cash income amount of bank branches are summarized to construct a data set. The total cash withdrawal amount is obtained by summarizing the withdrawal amount of current deposits, the withdrawal amount of time deposits, and the amount of other cash expenditure transactions, and the total daily cash income amount is obtained by summarizing the deposit amount of current deposits, the deposit amount of time deposits, and the amount of other cash income transactions.

[0031] As a possible implementation of this embodiment, the cash on hand forecasting module is specifically used to: Based on the time series ARIMA model, a withdrawal amount model and a bank branch daily net cash flow model are established, and the model is trained using the training set in the data set to obtain the relevant parameters of the withdrawal amount model and the bank branch daily net cash flow model, and obtain the trained withdrawal amount model and bank branch daily net cash flow model; The training set data in the dataset is input into the trained withdrawal amount model and bank branch daily net cash flow model to predict the future bank branch withdrawal amount and bank branch daily net cash flow.

[0032] As a possible implementation of this embodiment, the cash on hand calculation module is specifically used to: Set a lower limit on cash on hand at bank branches based on the average amount of cash withdrawals at bank branches in the past year; Calculate the variance of daily net cash flow changes of bank branches : , in, The monthly net cash reserve of the bank is the total daily cash withdrawal minus the total daily cash receipt. is the average of the daily net cash flow of bank branches each month, is the number of data; Calculate the conversion cost of cash held in stock at each bank branch based on the escort fees between the bank branch and the vault, the storage fees of the county branch and the additional fees of the agricultural support branch; Calculate the opportunity cost of bank branches holding cash on hand based on the interbank lending rate; The lower limit of cash on hand at bank branches and the variance of daily net cash flow changes at bank branches , the conversion cost of holding cash on hand and the opportunity cost of holding cash on hand are input into the Miller-Orr model, the optimal cash on hand holding amount Q and the upper limit of the cash on hand balance H are calculated, and the optimal holding range of cash on hand (H, L) is obtained: , , Among them, F is the conversion cost of cash on hand held by bank branches, K is the opportunity cost of cash on hand held by bank branches, and L is the lower limit of the cash balance on hand held by bank branches.

[0033] As a possible implementation of this embodiment, the inventory cash management module is specifically used to: When the cash balance of bank branches reaches the upper limit of the optimal holding range of cash on hand, the excess cash will be released through the interbank lending market or the bond market; When the cash balance of bank branches falls to the lower limit of the optimal holding range of cash on hand, the bank will use the interbank lending market or the bond market to replenish the cash on hand; Track changes in cash balances at bank branches in real time, and regularly adjust the upper and lower limits and target values ​​of the optimal cash holding range.

[0034] The specific process of calculating the bank branch cash holding range and the optimal holding amount using the bank branch cash holding amount prediction technical solution of the present invention is as follows.

[0035] (A) Data collection and preprocessing.

[0036] Data processing required for model data, collecting historical cash flow data of bank branches, including transaction records such as deposits, withdrawals, and transfers.

[0037] 1. Data collection: Export historical transaction data from 2018 to the present from the database, including date, institution, transaction type (deposit, withdrawal, etc.), transaction amount, etc., as well as historical inventory cash balance.

[0038] 2. Data processing: (1) Deleting outliers: such as transaction records that are obviously inconsistent with the norm; (2) Standardized data format: ensuring that all data are in the same format; (3) The daily total cash withdrawal amount of each bank branch is obtained by summarizing the withdrawal amount of demand deposits, the withdrawal amount of time deposits, and the amount of other cash expenditure transactions; the daily total cash income amount of each bank branch is obtained by summarizing the deposit amount of demand deposits, the deposit amount of time deposits, and the amount of other cash income transactions.

[0039] (ii) Predict future withdrawal amounts and daily net cash flows of bank branches based on machine learning techniques (time series ARIMA model).

[0040] Taking into account the impact of holidays and other factors on the withdrawal amount and daily net cash flow, the ARIMA model is used for forecasting.

[0041] 1. Sample splitting: Split into training samples and validation samples. For the above processed data table, the data of the first 12 months are used as training samples, and the data of the last 12 months are used as validation samples. The training samples are used to input the model for training. The values ​​of the dependent variables "average amount of withdrawals in the month" and "average net cash flow per day in the month" in the training samples are known and input into the model; the validation samples are used to verify the accuracy of the results of model training. The values ​​of the dependent variables "average amount of withdrawals in the month" and "average net cash flow per day in the month" in the validation samples are known, but their values ​​are not input into the trained model. Instead, the "average amount of withdrawals in the month" and "average net cash flow per day in the month" predicted by the model output are compared with the known situation for verification.

[0042] 2. Model training: Modeling is based on machine learning technology (time series ARIMA model), and the model is trained using training samples to obtain model-related parameters , , .

[0043] Use Python to write analysis functions to implement the ARIMA algorithm process; input the cash on hand and daily net cash flow data table of each bank branch, call the analysis function in a loop, obtain the corresponding model parameter values, and complete the prediction model training for each bank branch. The input value of the trained model is the sample data table, and the output value is the average cash on hand and the predicted value of the average daily net cash flow of the month. The specific process output diagram is as follows Figure 4-Figure 7 As shown, Figure 4 This is the original sequence diagram of the ARIMA algorithm process. Figure 5 is the autocorrelation plot of the ARIMA algorithm process, Figure 5 The shaded area is the 95% confidence interval. Figure 6 is the partial autocorrelation plot of the ARIMA algorithm process, Figure 6 The shaded area is the 95% confidence interval. Figure 7It is a schematic diagram comparing the predicted sequence with the original sequence. The trend of the predicted sequence is basically consistent with that of the original sequence, indicating that the prediction results are credible.

[0044] 3. Model verification: Compare the predicted results with the actual results to verify the accuracy of the model.

[0045] 4. Model prediction: 4.1 Forecast sample data processing: According to the generation steps in the above data processing, the data of the sample to be predicted is processed; 4.2 One-step-ahead prediction: After the model is verified, the prediction sample is input and the model can output the prediction result: , in, is the autoregressive order, is the moving regression order, is the number of differences required for the time series to become stationary, is a constant, is the AR coefficient, is the error, i.e. white noise, is the MA coefficient, is the predicted value, is the independent variable, For function The amount of change.

[0046] As described in the model validation, a one-step forward forecast is performed using the forecast sample data, so that the average withdrawal amount and the average daily net cash flow forecast for the next 6 months are obtained every day.

[0047] The autoregressive model is used to describe the relationship between the current value and the historical value, and uses the historical data of the change itself to predict itself. The data must meet the requirements of stationarity, and it is only applicable to the phenomenon related to the prediction of its own previous period. The moving average model focuses on the accumulation of error terms in the autoregressive model. ARMA means: a random time series can be represented by an autoregressive moving average model, that is, the series can be explained by its own past or lagged values ​​and random disturbance terms; if the series is stationary, that is, its behavior does not change over time, then the past behavior of the series can be used to predict the future.

[0048] (III) The Miller-Orr model is used to calculate the optimal holding range and optimal holding amount of cash on hand.

[0049] 1. Set model parameters: 1.1 Set the lower limit of cash on hand: According to historical data and business needs, set the lower limit L of the withdrawal amount. For example, by observing the historical data of Bank W, its cash balance is always higher than the withdrawal amount. According to the principle of whichever is lower, take the average withdrawal amount of each bank branch as the lower limit of cash on hand; 1.2 Calculate the variance of daily net cash flow changes : .

[0050] in, is the monthly net cash flow of bank branches per day, is the average of the monthly daily net cash flow, is the number of data; 1.3 Set the conversion cost F (i.e. the transaction cost when adjusting cash): The cost incurred in each cash transfer is selected, and the head office signs a contract with the escort company every year to determine the amount. The county needs to comprehensively consider the storage cost, and the branch with agricultural outlets needs to consider the additional amount of escort for agricultural outlets; 1.4 Set the opportunity cost K (i.e. interest rate, such as the discount rate of securities): Select the average SHIBOR rate of 2 weeks in the past 12 months. This data can be adjusted according to the bank's lending frequency. If the bank's lending frequency is one month, it can be adjusted to the average SHIBOR rate of 1 month in the past 12 months as the opportunity cost.

[0051] 2. Model Validation: 2.1 According to the Miller-Orr model, the upper limit of cash balance H is calculated: , ; 2.2 According to the above steps, the optimal holding range (H, L) and optimal value Q of cash on hand are obtained through integration, and compared with the actual cash balance on hand of bank branches, as shown in the figure below: Figure 3 shown.

[0052] 3. Model prediction: The average forecast value of withdrawal amount and the average forecast value of daily net cash flow obtained by ARIMA model are imported into the model to calculate the optimal holding range (H, L) and optimal holding amount Q of cash in stock of each bank branch.

[0053] 4. Designing a cash management strategy 1. Design cash management strategies based on the cash inventory range and optimal cash inventory holdings derived from the Miller-Orr model: 1.1 When the cash balance reaches the upper limit H, the excess cash will be released through the interbank lending market or the bond market to obtain stable income for the bank; 1.2 When the cash balance drops to the lower limit L, the interbank lending market or bond market can be used to replenish the cash on hand to avoid a liquidity crisis; 1.3 Regularly adjust upper and lower limits and target values ​​to respond to business changes and market fluctuations.

[0054] 2. Write strategy rules: According to the Miller-Orr model and ARIMA model, write specific strategy codes and get the results as shown in Table 1: Table 1 Cash management strategy execution results

[0055] 3. Strategy Execution: When the cash balance reaches the upper limit H, the excess cash will be released through the interbank lending market or the bond market to obtain stable income for the bank; When the cash balance drops to the lower limit L, the interbank lending market or bond market can be used to replenish the cash on hand to avoid a liquidity crisis.

[0056] (V) Implementation and Monitoring: Write the above strategies into computer programs and integrate them into the bank's cash management system to achieve real-time tracking of cash balance changes and timely adjustment of strategies.

[0057] 1. Real-time monitoring: 1.1 Integrate the strategy code into the bank’s cash management system to track changes in cash balances in real time; 1.2 According to the preset alarm mechanism, when the cash balance approaches the upper and lower limits, an alarm is sent to notify relevant personnel.

[0058] 2. Regular evaluation and adjustment: 2.1 Regularly evaluate the effectiveness of the strategy and adjust the upper limit, lower limit and target value according to actual conditions; 2.2 Update transaction fees F and short-term financing rates i based on market changes and business needs.

[0059] (VI) Final delivery form of the present invention: The final delivery is the upper limit, lower limit and target balance of cash on hand at each bank branch.

[0060] An embodiment of the present invention provides an electronic device, including a processor, a memory and a bus, wherein the memory stores machine-readable instructions executable by the processor. When the electronic device is running, the processor communicates with the memory through the bus, and the processor executes the machine-readable instructions to perform the steps of the method for predicting the amount of cash holdings in inventory of any bank branch as described above.

[0061] Specifically, the above-mentioned memory and processor can be general-purpose memory and processor, which are not specifically limited here. When the processor runs the computer program stored in the memory, it can execute the above-mentioned method for predicting the amount of cash holdings in bank branches.

[0062] Corresponding to the method for starting the above-mentioned application, an embodiment of the present invention further provides a storage medium on which a computer program is stored. When the computer program is executed by a processor, the steps of the method for predicting the cash holdings in inventory of any bank branch are executed.

[0063] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the above embodiments, ordinary technicians in the relevant field should understand that the specific implementation methods of the present invention can still be modified or replaced by equivalents. Any modification or equivalent replacement that does not depart from the spirit and scope of the present invention should be covered within the scope of protection of the claims of the present invention.

Claims

1. A method for predicting the amount of cash held in bank branches, characterized in that: The steps include: Step S1, collecting and preprocessing historical data of cash flow of bank outlets to form a data set, wherein the cash flow includes deposit, withdrawal and transfer transaction records; Step S2, based on business needs and pre-processed historical data, using the time series ARIMA model to predict the future bank branch withdrawal amount and bank branch daily net cash flow; Step S3, based on the bank branch withdrawal amount and the bank branch daily net cash flow predicted by the ARIMA model, the Miller-Orr model is used to calculate the optimal cash holding amount and the optimal holding range of cash on hand; Step S4, performing cash inventory management of bank branches based on the optimal cash inventory holding amount and the optimal cash inventory holding range.

2. The method for predicting the amount of cash held in bank branches according to claim 1, characterized in that: The step S1 collects historical data of cash flow of bank outlets and performs preprocessing to form a data set, including: Step S11, extracting historical transaction data from the bank branch database, the historical transaction data including date, institution, transaction type, transaction amount and historical withdrawal amount, the transaction type including deposit and withdrawal; Step S12, deleting abnormal values ​​and standardizing the data format of historical transaction data; Step S13, based on the processed historical transaction data, summarize the total daily cash withdrawal amount and the total daily cash income amount of bank branches to construct a data set, wherein the total cash withdrawal amount is obtained by summarizing the withdrawal amount of current deposits, the withdrawal amount of time deposits and other cash expenditure transactions, and the total daily cash income amount is obtained by summarizing the deposit amount of current deposits, the deposit amount of time deposits and other cash income transactions.

3. The method for predicting the amount of cash held in bank branches according to claim 1, characterized in that: The step S2, based on business needs and pre-processed historical data, uses the time series ARIMA model to predict the future bank branch withdrawal amount and bank branch daily net cash flow, including: Step S21, establishing a withdrawal amount model and a bank branch daily net cash flow model based on the time series ARIMA model, and using the training set in the data set to train the model, obtaining relevant parameters of the withdrawal amount model and the bank branch daily net cash flow model, and obtaining the trained withdrawal amount model and bank branch daily net cash flow model; Step S22, input the training set data in the data set into the trained withdrawal amount model and bank branch daily net cash flow model to predict the future bank branch withdrawal amount and bank branch daily net cash flow.

4. The method for predicting the amount of cash held in bank branches according to claim 3, characterized in that: The step S3, based on the bank branch withdrawal amount and the bank branch daily net cash flow predicted by the ARIMA model, uses the Miller-Orr model to calculate the optimal cash holding amount and the optimal cash holding range, including: Step S31, setting the lower limit of cash on hand at bank branches based on the average withdrawal amount at bank branches in the past year; Step S32, calculate the variance of the daily net cash flow of the bank branch : ， in, The monthly net cash reserve of the bank is the total daily cash withdrawal minus the total daily cash receipt. is the average of the daily net cash flow of bank branches each month, is the number of data; Step S33, calculating the conversion cost of the cash on hand at each bank branch based on the escort fee between the bank branch and the vault, the storage fee of the county branch, and the additional fee of the agricultural support branch; Step S34, calculating the opportunity cost of the bank branch holding cash on hand based on the interbank lending rate; Step S35: calculate the lower limit of cash on hand at bank branches and the variance of daily net cash flow changes at bank branches. , the conversion cost of holding cash on hand and the opportunity cost of holding cash on hand are input into the Miller-Orr model, the optimal cash on hand holding amount Q and the upper limit of the cash on hand balance H are calculated, and the optimal holding range of cash on hand (H, L) is obtained: ， ， Among them, F is the conversion cost of cash on hand held by bank branches, K is the opportunity cost of cash on hand held by bank branches, and L is the lower limit of the cash balance on hand held by bank branches.

5. The method for predicting the amount of cash held in bank branches according to any one of claims 1 to 4, characterized in that: The step S4, based on the optimal cash holding amount and the optimal cash holding interval, performs cash holding amount management of bank branches, including: Step S41, when the cash balance of the bank branch reaches the upper limit of the optimal holding range of cash on hand, the excess cash is released through the interbank lending market or the bond market; Step S42, when the cash balance of the bank branch falls to the lower limit of the optimal holding range of cash on hand, the cash on hand is replenished by using the interbank lending market or the bond market; Step S43, tracking the changes in the cash balance of bank branches in real time, and regularly adjusting the upper and lower limits and target value of the optimal holding range of cash on hand.

6. A device for predicting the amount of cash held in bank branches, characterized in that: include: A data collection module is used to collect historical data of cash flow of bank branches and pre-process it to form a data set, wherein the cash flow includes deposit, withdrawal and transfer transaction records; Cash on hand forecasting module, which is used to forecast the future withdrawal amount and daily net cash flow of bank branches based on business needs and pre-processed historical data using the time series ARIMA model; Cash on hand calculation module, which is used to calculate the optimal cash on hand holding amount and the optimal cash on hand holding interval based on the bank branch withdrawal amount and the bank branch daily net cash flow predicted by the ARIMA model, and the Miller-Orr model; The inventory cash management module is used to manage the inventory cash of bank branches based on the optimal inventory cash holding amount and the optimal inventory cash holding range.

7. The device for predicting the amount of cash held in bank branches according to claim 6, characterized in that: The inventory cash forecast module is specifically used for: Based on the time series ARIMA model, a withdrawal amount model and a bank branch daily net cash flow model are established, and the model is trained using the training set in the data set to obtain the relevant parameters of the withdrawal amount model and the bank branch daily net cash flow model, and obtain the trained withdrawal amount model and bank branch daily net cash flow model; The training set data in the dataset is input into the trained withdrawal amount model and bank branch daily net cash flow model to predict the future bank branch withdrawal amount and bank branch daily net cash flow.

8. The device for predicting the amount of cash held in bank branches according to claim 7, characterized in that: The cash on hand calculation module is specifically used for: Set a lower limit on cash on hand at bank branches based on the average amount of cash withdrawals at bank branches in the past year; Calculate the variance of daily net cash flow changes of bank branches : ， in, The monthly net cash reserve of the bank is the total daily cash withdrawal minus the total daily cash receipt. is the average of the daily net cash flow of bank branches each month, is the number of data; Calculate the conversion cost of cash held in stock at each bank branch based on the escort fees between the bank branch and the vault, the storage fees of the county branch and the additional fees of the agricultural support branch; Calculate the opportunity cost of holding cash on hand at bank branches based on the interbank lending rate; The lower limit of cash on hand at bank branches and the variance of daily net cash flow changes at bank branches , the conversion cost of holding cash on hand and the opportunity cost of holding cash on hand are input into the Miller-Orr model, the optimal cash on hand holding amount Q and the upper limit of the cash on hand balance H are calculated, and the optimal holding range of cash on hand (H, L) is obtained: ， ， Among them, F is the conversion cost of cash on hand held by bank branches, K is the opportunity cost of cash on hand held by bank branches, and L is the lower limit of the cash balance on hand held by bank branches.

9. An electronic device, characterized in that: It includes a processor, a memory and a bus, the memory stores machine-readable instructions executable by the processor, when the electronic device is running, the processor and the memory communicate through the bus, and the processor executes the machine-readable instructions to perform the steps of the method for predicting the amount of cash holdings in bank branches as described in any one of claims 1-5.

10. A storage medium, characterized in that: The storage medium stores a computer program, which, when executed by a processor, executes the steps of the method for predicting the amount of cash held in bank branches as described in any one of claims 1 to 5.

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