RU2724798C1 - Method and system for optimization of cash collection facilities of cash circulation objects - Google Patents

Abstract

FIELD: data processing.SUBSTANCE: invention relates to method and system for optimization of cash collection facilities of cash circulation (CC) objects. Method, in which: obtaining CC object data for a given time period, wherein CC objects are self-service devices (SD) and / or bank compartments and data include location information of CC objects, history of statuses and operations of CC objects, as well as the amount of funds on each CC object; processing CC history data and object operations; based on processed status history data and operations of CC objects, performing movement of cash in each CC object by means of machine learning algorithm; method includes optimizing cash collection of CC objects based on the prediction done for a given time interval, forming a matrix of CC object conditions with encapsulation performance parameters.EFFECT: technical result consists in improvement of efficiency of collection of CC objects, due to automated forecasting of financial load of CC objects in a given time interval based on data on the state of each CC object.16 cl, 9 dwg

Description

FIELD OF TECHNOLOGY

[0001] This technical solution relates to the field of computer technology, in particular to a method and system for the automated optimization of cash-in-transit services for cash circulation facilities.

BACKGROUND

[0002] Currently, the process of organizing collection services in financial institutions is based on the manual processing of data received from various cash circulation facilities (NDOs), which include various types of self-service devices (ATMs) (ATMs, terminals, etc.) , and internal structural units (VSP) that provide customer service to financial institutions.

[0003] Based on the large fleet of CSS and VSP, there is a problem of predicting the load on the facilities of the NDO, which leads to an increase in the time for processing information on the need for collection and maintenance of these facilities to ensure their effective operation. Also, the collection process of NDO facilities is a labor-intensive operation and involves a share of risk based on the human factor, while minimizing the collection procedures should be clearly defined for priority NDO facilities, to exclude the possibility of their long downtime, which leads to financial costs and the inability to service customers regarding the issue of cash.

[0004] Currently, the process of managing cash liquidity of NDO facilities is organized using AC "OptiCash / OptiNet", however, this system does not provide the necessary level of automation taking into account changes in the process of managing cash liquidity and necessitates the use of additional tools (including t .n. “small automation”) to increase employee productivity and increase the effectiveness of the cash liquidity management process.

[0005] In the changing conditions of economic, financial and political factors surrounding the participants in the cash liquidity management process, the existing OptiCash system interaction schemes with other automated systems and their modules do not allow for prompt response to emerging force majeure circumstances, incidents or other critical changes in parameters process.

[0006] Automated cash flow control systems in the state of the art are also known from the prior art. From US patent No. 10,169,947 (Innovative Tech Ltd, 01/01/2019), a system for calculating and predicting the exhaustion of a given type of currency in ATMs is known for their timely provision of cash.

[0007] In US patent application No.20190034842 (NCR Corp., January 31, 2019), an approach is described in terms of optimizing the collection service of CSS, which consists in modeling and predicting the financial load of CSS in a given time interval.

[0008] The disadvantages of existing solutions is the lack of effectiveness in predicting the financial burden on the facilities of NDO.

SUMMARY OF THE INVENTION

[0009] The technical problem to be solved is to eliminate the disadvantages inherent in the analogues of their prior art.

[0010] The technical result is to increase the efficiency of optimization of collection of NDO objects, due to the automated forecasting of the financial load of NDO objects in a given time interval, taking into account data on the state of each NDO object.

[0011] An additional technical result is the reduction of time for servicing NDO facilities, by ensuring the construction of data on the financial load of the NDO facilities and prioritizing the maintenance of the said facilities with the required amount of cash.

[0012] In order to achieve the claimed technical result, in a preferred embodiment of the claimed solution, a computer-implemented method for optimizing the collection service of cash circulation facilities (NDO) is performed using a processor and comprising stages in which:

- receive data of the objects of the NDO for a given time period, and the objects of the NDO are self-service devices (CSS) and / or bank branches and the data will include at least information about the location of the objects of the NDO, the history of the status and operations of the NDO facilities, as well as the number funds at each NDO facility;

- perform data processing of the history of statuses and operations of the objects of the NDO;

- on the basis of the processed data on the history of statuses and operations of objects of NDOs, cash flows are forecasted in each object of NDOs using at least one machine learning algorithm;

- they carry out optimization of cash collection services for objects of the VAT based on the forecast performed for a given time period, during which form a state matrix of the objects of the VAT with the parameters of the collection.

[0013] In one of the private embodiments of the method, the CAs are selected from the group: ATM, payment terminal, information and payment terminal, automatic currency exchange terminal, or combinations thereof.

[0014] In another private embodiment of the method, according to the data of bank branches, data is filtered by statuses that do not change or change the amount of funds for this branch in a given range at different time intervals.

[0015] In another particular embodiment of the method, device downtimes are determined for device history data as the device’s inactive status time during the day.

[0016] In another particular embodiment of the method for a CA with a small number of downtimes, a first historical slice is constructed containing points that reflect the status of cash flow in the CSS in the context of a given period of time.

[0017] In another particular embodiment of the method for a DC with the number of downtimes above a predetermined threshold value, a second time slice is constructed that reflects the status of cash flow in the DC in the context of a given time interval, and normalization is performed for the said second historical slice based on a comparison with the first historical by cut.

[0018] In another particular embodiment of the method, at least part of the second historical slice is replaced with indicators from the first historical slice during normalization, and the replacement is performed based on the correlation of indicators such as: frequency of access to the addressee, accessibility mode of the addressee, territorial proximity between CSS

[0019] In another particular embodiment of the method, if there are points in the first historical slice characterizing the idle days of the asset, such days are replaced based on a moving average based on indicators of neighboring points.

[0020] In another particular embodiment of the method, at the forecasting stage, an analysis is made of future volumes of cash transactions for each object of the NDO.

[0021] In another private embodiment of the method, the volume of operations is determined based on the amount of cash and / or the number of sheets of notes.

[0022] In another particular embodiment of the method, anomalies are determined in a predetermined time interval for forecasting, which characterize periodic mass client arrivals / withdrawals - salary payments.

[0023] In another particular embodiment of the method, the optimization step is performed using a stochastic optimization algorithm.

[0024] In another private embodiment of the method, the calculation of the number of downloads / unloads of funds for each object of the NDO is carried out.

[0025] In another particular embodiment of the method, as a result of optimization, a digital map is formed indicating the objects of the NDO with the collection date and additional information.

[0026] In another private embodiment of the method, the additional information includes at least one of: the amount of cash, the time of availability of the object of the VAT, the balance of cash.

[0027] The achievement of the claimed technical result is also achieved by optimizing the collection service of the objects of the NDO, which contains at least one processor and at least one storage device containing machine-readable instructions that, when executed by the processor, perform the above method.

BRIEF DESCRIPTION OF THE DRAWINGS

[0028] The features and advantages of the present invention will become apparent from the following detailed description of the invention and the accompanying drawings, in which:

[0029] FIG. 1 illustrates a general device interaction diagram.

[0030] FIG. 2 illustrates a flowchart of the steps of the claimed method.

[0031] FIG. 3 illustrates an example of a processing schedule for normalizing the historical slice of the financial history of the equity.

[0032] FIG. 4 illustrates a cash flow chart for NFI facilities with HFIs.

[0033] FIG. 5 illustrates an event generation table.

[0034] FIG. 6 illustrates a schedule for fixing anomalies.

[0035] FIG. 7 illustrates a display table of standard features.

[0036] FIG. 8 illustrates an example of a forecasted graph.

[0037] FIG. 9 illustrates an example of a computing device for implementing a technical solution.

DETAILED DESCRIPTION OF THE INVENTION

[0038] The concepts and terms necessary for understanding this technical solution will be described below.

[0039] In this technical solution, a system is meant, including a computer system, a computer (electronic computer), CNC (numerical control), PLC (programmable logic controller), computerized control systems, and any other devices capable of performing a given , clearly defined sequence of operations (actions, instructions).

[0040] An instruction processing device is understood to mean an electronic unit or an integrated circuit (microprocessor) executing machine instructions (programs).

[0041] The command processing device reads and executes machine instructions (programs) from one or more data storage devices. Hard disk drives (HDD), flash memory, ROM (read-only memory), solid-state drives (SSD), optical drives can act as storage devices.

[0042] A program is a sequence of instructions for execution by a computer control device or an instruction processing device.

[0043] FIG. 1 shows a general scheme (100) of the interaction of the elements of the claimed solution. Optimization of collection services is carried out for NDO objects, which include various types of CSS (110) and VSP (120). US (110) can be: an ATM, a payment terminal, an information and payment terminal, an automatic currency exchange terminal and another type of device that allows you to carry out transaction operations using cash. VSP (120) is understood, for example, as branches of financial organizations, in particular banks, that provide services to individuals / legal entities and that receive / issue cash.

[0044] Each of the NDO facilities (110, 120) has a unique set of parameters, which includes information such as: the location of the NDO facilities, a history of the status and operations of the NDO facilities, as well as the amount of funds at each NDO facility. This information is transmitted from the objects of the NDO (110, 120) through the data transmission network (130) to the central computing device (150), for example, a server, server cluster, or other suitable type of computing device for performing the required functions.

[0045] As the data transmission network (130), the Internet is used, which can be organized at each of the NDO facilities (110, 120) using appropriate technical means, for example, using data transmission means via a cellular communication network ( 2G / 3G / 4G / 5G), wireless WLAN, WAN, etc. The specific principle of organizing the reception / transmission of data streams between the objects of the NDO (110, 120) and the server (150) is selected based on the specific type of the object of the NDO, for example, the CSS type (110), or the most preferred type of communication for the VSP (120), for example, based on the territorial location, using well-known technical means that provide the required functionality in terms of ensuring information interaction.

[0046] Next, we consider the main process of the claimed method. In FIG. Figure 2 shows a method (200) for optimizing collection services of objects of non-commissioned assets. At the initial stage (201), information is collected by the server (150) received from the NDO objects (110, 120). As mentioned above, such information is the geographical location of the facilities, the history of the status and operations of the facilities, and the amount of funds at each facility.

[0047] As the location information, the address of the US (110) installation / placement location, the name of the installation object (shopping center, store, VSP, etc.), the US (110) location floor, and geo-coordinates can be used. Also, for each NDO object (110, 120), a corresponding unique identifier (UID) is assigned. Based on the UID, the corresponding data set is stored in the server database (150), which is necessary to perform the method (200) in terms of performing the required computing operations.

[0048] Based on the information received, at step (202), data processing on the objects of the NDO (110, 120) is performed. During processing, for each object of NDO (110, 120), on the basis of the information received at step (201), information is displayed in the time section that displays cash flow movements. For CSS (110), such information may be the historical information of device downtime, in particular, indications of the activity and content of funds in a given time period. For VSP (120), data is filtered by statuses that do not change or change the amount of money in a given range at different time intervals. This filtering allows you to exclude types of cash transactions that do not significantly change the cash balance in VSP (120) at a given threshold value. For example, such operations may include: utility payments, withdrawal of funds from a deposit and repayment of a loan by the same means (or re-deposit of these funds), etc.

[0049] During the data processing in step (202), for the data received from the CSS (110), the analysis and replacement of their historical data is performed to generate weighted information on their historical statuses in a time slice. Based on the information on cash flows at each DC (110), an analysis of the downtime and / or movement of devices is carried out, in particular, the date of inactive status in the daily context and the cash flow for DC (110) are taken into account. The analysis is carried out for each cash processing module of each CA (110), in particular, the module for cash dispensing (dispenser) and cash acceptance (deposit).

[0050] A set of CSS (110) with a small number of downtimes from the total data set obtained in step (201) is detected, in particular, the percentage of downtimes of which does not exceed the established threshold value. CSS (110) with a small number of downtimes are marked as devices with a good fictitious history (HFI). Using the mentioned set of CSS (110), the first historical slice is constructed with HFI, containing points that reflect the status of cash flow in the CSS in the context of a given period of time.

[0051] Finding a data set for CSS (110) with HFI can be performed on the basis of such historical data of CSS (110) that contain a small amount of downtime, for example, <5% of the entire history, and there are no long periods of downtime (omissions or percentage downtime above threshold <14 consecutive days). In addition, downtime data processing can be performed for DC (110) with HFI, because small gaps or a percentage of downtime above a threshold value on some days may occur. To do this, replace days with downtimes, moving averages based on readings of neighboring points. Min-Max Scaling can also be performed on the changed story based on the unchanged.

[0052] As one example, the calculation of the readings of the idle time of the DC (110) is determined as a percentage on the basis of the idle time of the DC (110) on a specific day, when the minutes of the idle time of the DC (110) are summed up within the range of the availability time of a certain DC (110) and dividing the obtained value by the total availability time (due, for example, to the operating time of the object on which the CSS is located, etc.).

[0053] For CSS (110) with the number of downtimes above a given threshold value, with a poor financial history, a second time slice is constructed that reflects the cash flow status in CSS (110) in the context of a given period of time. For the second historical slice, its indicators are normalized based on a comparison with the first historical slice. During normalization, at least part of the second historical slice is replaced by indicators from the first historical slice, and replacement is performed based on the correlation of indicators such as: frequency of access to the public address, mode of accessibility of the public service, territorial proximity between the public service. For DC (110) with a poor financial history, the replacement of the entire part of the historical slice up to the last long downtime with a similar part of the similar one can be performed.

[0054] FIG. Figure 3 shows graphs with a normalized financial history according to CSS (110). Chart (A) shows an example of a historical slice for the selected set of CSS (110), and graph (B) shows a smoothed historical series for a set of CSS (110) with HFI.

[0055] At step (203), based on the processed information on the history of statuses and operations of the objects of the NDO, at step (202), cash flow forecasting is performed in each object of the NDO using one or more machine learning algorithms. The algorithm receives information on the normalized HFI of the NDO objects (110, 120), event data, and the availability time of the NDO objects (110, 120).

[0056] FIG. 4 is a graph of input cash flow forecasting data. For each type of NDO object, its inherent cash flow mechanism is analyzed. An analysis of the information at step (203) is necessary for the purpose of modeling the forecast for cash withdrawal / deposit to predict the objects of operations (in the amount or number of sheets) for a given future period (for example, 14 days).

[0057] In the case of CSS (110), made in the form of ATMs and / or payment terminals, cash flow can consist of cash dispensing, cash depositing. For VSP (120) and RATM (an ATM with a recirculation function), the key indicators for predicting the financial load are the following indicators: balance and bottom (minimum cumulative balance with an aggregation period of 30 minutes) of customers' cash.

[0058] In FIG. Figure 5 presents an example of a table with the formation of events by which anomalies (bursts / surges of activity) are recorded in a time slice. Anomalies characterize periodic mass client arrivals / withdrawals - salary payments, etc. For NDO objects (110, 120), an analysis of periodic anomalies is performed. Periodic anomalies are not processed. Non-periodic are smoothed using a moving average or a simplified forecast. The processed data is then used in the forecasting phase.

[0059] At the forecasting step (203), the processing of incoming data is performed using one or more (ensemble, committee) machine learning models. The following can be used as machine learning algorithms: linear regression, linear regression LASSO, linear regression RIDGE, decision tree, random forest, gradient boosting on trees, recurrent neural network, etc.

[0060] At step (203), when constructing the signs for each subsequent forecasted day in a given time interval, all values for the previous day are used, including those already predicted. This approach is used to predict the day T + X, where T is the current day, X is an arbitrary day in the forecast horizon. To do this, you need to get the signs of the day T + X-1 in order to use them as relevant indicators in calculating data for a given time range.

[0061] In FIG. 6 shows an example of a table of events reflection in the time range. Based on the identified events at specified time intervals, in particular, the identified anomalies, further enrichment (expansion) of the attribute space is performed with calendar signs - days of the week and detected periodic anomalies.

[0062] Next, the generation of standard features is performed:

- Logs of a given depth (demand values shifted in time to a given depth);

- Moving statistics in the window (mean, std, median, min, max);

- Moving statistics by day of the week (mean, std, median, min, max);

- Statistics for the month (mean, std, median, min, max).

[0063] An example of feature formation is shown in FIG. 7. Based on the generated standard features, a forecast is generated for a fixed number of days at each point in time, using the data already predicted (the forecast is built both gross for all denominations, and for each denomination separately).

[0064] At the output of step (203), forecasting the movement of cash at the facilities of the NDO (110, 120) for a given time period is constructed. At the same time, retro-prediction is also carried out to assess the quality of the forecast model. Based on this assessment, the optimal parameters of the forecast models are selected at which the forecast error is minimized. In FIG. 8 is a graph showing a generated forecast. Using the generated indicators in the time interval, it is possible to further optimize the collection of NDO objects (110, 120).

[0065] At step (204), the collection service of the NDO objects is optimized (110, 120) based on the prediction performed for a given time period during which a state matrix of the NDO objects with the collection execution parameters is generated. The input parameters for the implementation of step (204) are:

- current funding rate (for example, 0.7);

- The selected optimization period, for example, week, month, etc .;

- limits (the maximum capacity of cash in the unitary asset (110), while taking into account the maximum amount and number of notes, the number of units of US (110);

- cash pre-order limits for VSP (120);

- time series of the power of the cache center, in particular the maximum number of collections per day;

- a list of the MIA of the objects of the NDO;

- incoming balance at the beginning of the period of optimization of the objects of NDO;

- costs of collection for each object of NDO;

- availability of collection;

- daily gross balances and bottom forecasted for limits (for VSP and RATM) or daily gross withdrawals / top-ups (for CS with deposit function).

[0066] The optimization algorithm is performed using a stochastic optimization algorithm. Taking into account the analysis of the input data, a state matrix is initialized that displays the objects of the NDO (110, 120), for which collection is required. Using the stochastic optimization algorithm, valid load indicators for NDO objects (110, 120) are constructed in a given time interval taking into account the limits and the bottom (minimum daily load of CSS / VSP) and optimal in terms of total costs.

где ϕ_i прогноз снятий на день или дна (для кэшцентра в таблице состояний количество единиц в столбцах не превышает мощности за день), N - величина максимальной загрузки. С учетом входящих параметров рассчитываются загрузки/выгрузки и остатки. По прогнозным значениям сальдо и дна, а также с помощью вектора состояния рассчитываются минимальные загрузки/выгрузки для объектов НДО (110, 120), по которым строится таблица инкассаций и последующая матрица состояний. На основании минимизации затрат на осуществление инкассаций для конкретных объектов НДО (110, 120) принимается решение о проведении инкассации.[0067] For each object of the NDO (110, 120), a state vector (for the cache center state table) is calculated, in which for the distance d between units it satisfies the inequality

where ϕ _{i is the} forecast of withdrawals per day or bottom (for the cache center in the state table the number of units in the columns does not exceed the capacity per day), N is the maximum load value. Based on the input parameters, load / unload and balances are calculated. Based on the predicted balance and bottom values, as well as using the state vector, the minimum load / unload for the NDO objects (110, 120) is calculated, based on which the collection table and the subsequent state matrix are constructed. On the basis of minimizing the costs of collection for specific objects of the NDO (110, 120), a decision is made to conduct collection.

[0068] Information about collection at NDO facilities (110, 120) can be generated in the form of various kinds of data displayed, for example, in the form of reports, graphs, etc. Data on optimization of collection of NDO objects (110, 120) can be displayed on a digital GIS map with addresses, UID of NDO objects and other additional information, for example, amount of funds, time of availability of an NDO object, cash balance.

[0069] In FIG. 9 shows an example of a general view of a computing system (300) based on a computing device that provides implementation of the claimed method (200). The computing device may be a server (150), as previously indicated in the application materials.

[0070] In general, the system (300) comprises one or more processors (301) connected by a common data bus (310), memory tools such as RAM (302) and ROM (303), input / output interfaces (304) , input / output devices (305), and a device for network communication (306).

[0071] A processor (301) (or multiple processors, a multi-core processor) can be selected from a variety of currently widely used devices, for example, Intel ™, AMD ™, Apple ™, Samsung Exynos ™, MediaTEK ™, Qualcomm Snapdragon ™, and etc. Under the processor, it is also necessary to take into account a graphic processor, for example, an NVIDIA or ATI GPU, which is also suitable for the full or partial execution of the method (200). In this case, the available memory capacity of the graphics card or graphics processor may be a memory tool.

[0072] RAM (302) is a random access memory and is intended to store machine-readable instructions executed by the processor (301) to perform the necessary operations for logical data processing. RAM (302), as a rule, contains executable instructions of the operating system and corresponding software components (applications, program modules, etc.).

[0073] The ROM (303) is one or more permanent storage devices, for example, a hard disk drive (HDD), a solid state drive (SSD), flash memory (EEPROM, NAND, etc.), optical storage media ( CD-R / RW, DVD-R / RW, BlueRay Disc, MD), etc.

[0074] Various types of I / O interfaces (304) are used to organize the operation of components of a computing system (300) and organize the operation of external connected devices. The choice of appropriate interfaces depends on the particular computing device, which can be, but not limited to: PCI, AGP, PS / 2, IrDa, FireWire, LPT, COM, SATA, IDE, Lightning, USB (2.0, 3.0, 3.1, micro, mini, type C), TRS / Audio jack (2.5, 3.5, 6.35), HDMI, DVI, VGA, Display Port, RJ45, RS232, etc.

[0075] Various means (305) of I / O information, for example, a keyboard, a display (monitor), a touch screen, a touch pad, a joystick, a mouse, a light pen, a stylus, are used to provide user interaction with a computer system (300), touchpad, trackball, speakers, microphone, augmented reality, optical sensors, tablet, light indicators, projector, camera, biometric identification tools (retina scanner, fingerprint scanner, voice recognition module), etc.

[0076] The network interaction tool (306) provides data transfer by the system (300) via an internal or external computer network, for example, an Intranet, the Internet, a LAN, and the like. As one or more means (306) may be used, but not limited to: Ethernet card, GSM modem, GPRS modem, LTE modem, 5G modem, satellite communications module, NFC module, Bluetooth and / or BLE module, Wi-Fi module and other

[0077] Additionally, satellite navigation aids as part of the device (200), for example, GPS, GLONASS, BeiDou, Galileo, can also be used.

[0078] The application materials presented disclose preferred examples of implementing a technical solution and should not be construed as limiting other, particular examples of its implementation, not going beyond the scope of the claimed legal protection, which are obvious to specialists in the relevant field of technology.

Claims (20)

1. A computer-implemented method for optimizing the collection service of cash objects (NDO), performed using a processor and containing stages in which: - receive data of the objects of the NDO for a given time period, and the objects of the NDO are self-service devices (CSS) and / or bank branches and the data will include at least information about the location of the objects of the NDO, the history of the status and operations of the NDO facilities, as well as the number funds at each NDO facility; - perform data processing of the history of statuses and operations of the objects of the NDO; - on the basis of the processed data on the history of statuses and operations of objects of NDOs, cash flows are forecasted in each object of NDOs using at least one machine learning algorithm; - they carry out the optimization of collection services of objects of the NDO on the basis of the forecast made for a given time period, during which form a state matrix of the objects of the NDO with the parameters of the collection. 2. The method according to p. 1, characterized in that the CS are selected from the group: ATM, payment terminal, information and payment terminal, automatic currency exchange terminal or a combination thereof. 3. The method according to claim 1, characterized in that according to the data of bank branches, data is filtered by statuses that do not change or change in the specified range in different time periods the amount of money for this branch. 4. The method according to p. 1, characterized in that for the data of the history of the device are determined the downtime of the devices as the inactive status of the device during the day. 5. The method according to p. 4, characterized in that for a DC with a small number of downtimes, the first historical section is constructed containing points that reflect the status of cash flow in the DC in the context of a given period of time. 6. The method according to claim 5, characterized in that for a DC with a number of downtimes above a predetermined threshold value, a second time slice is constructed that reflects the status of cash flow in the CSS in the context of a given period of time, and normalization is performed for the second historical slice based on comparison with the first historical cut. 7. The method according to p. 6, characterized in that during normalization at least part of the second historical slice is replaced with indicators from the first historical slice, moreover, replacement is based on the correlation of such indicators as: frequency of access to CSS, accessibility mode of CSS, territorial proximity between the CA. 8. The method according to claim 4, characterized in that if there are points in the first historical slice characterizing the idle days of the asset, such days are replaced based on a moving average based on indicators of neighboring points. 9. The method according to p. 1, characterized in that at the forecasting stage, an analysis of future volumes of cash transactions for each object of NDO is carried out. 10. The method according to p. 9, characterized in that the volume of operations is determined based on the amount of cash and / or the number of sheets of notes. 11. The method according to p. 9, characterized in that in a given time interval for forecasting, anomalies are determined that characterize periodic mass client arrivals / withdrawals - salary payments. 12. The method according to p. 1, characterized in that the optimization step is performed using a stochastic optimization algorithm. 13. The method according to p. 12, characterized in that the calculation of the number of downloads / unloads of funds for each object NDO. 14. The method according to claim 1, characterized in that, based on the results of optimization, a digital map is formed indicating the objects of the NDO with the collection date and additional information. 15. The method according to p. 14, characterized in that the additional information includes at least one of: the amount of cash, the time of availability of the object of the NDO, the balance of cash. 16. A system for optimizing collection services of objects of NDO, containing at least one processor and at least one storage device containing machine-readable instructions that, when executed by the processor, perform the method according to any one of claims. 1-15.

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