V Международная студенческая научная конференция Студенческий научный форум - 2013

One of the core factors influencing the successful business management is how the businesses organize the process of financial report preparation. In other words, to run the business effectively, companies are in need of getting relevant and structured data from external sources and extracting valuable information on their base [].

Preparing financial reports the majority of businesses comply with requirements of state statistical reporting. State statistical reporting is a determined form of providing public bodies with necessary information about financial and business operations. There are two main types of state statistical reporting: standard and specialized reporting. Standard type of reporting suits to every small businesses, performing their activity in state economy. Meanwhile, specialized type of reporting depends on the features of every particular sphere of business. State statistical reporting is based on accounting and operational accounting in the organization. State statistical reporting is the official document, presenting statistical data about the activity of the business, number of its employees, money transfers, wage-funds and financial operations.

However, report preparation itself does not allow businesses to obtain the valuable information for decision-making process and to improve their performance. By illuminating this problem, companies are likely to be able not only to create and use qualitative reports but to evaluate their performance and develop further management decisions.

One effective solution to the problems stated above is field-oriented analytical reporting system (ARS) with Data Mining techniques implemented. Development and implementation of such a system will, first, optimize the process of report preparation and further operations with them, accounting for features of the subject domain. Second, it will present an effective instrument for automatic extraction of valuable information from the data, which in their turn will be used in decision-making process via Data mining methods.

In this work, the problems concerning financial reporting will be illustrated on the example of Payment Management Center. Therefore, the ultimate goal of this paper is to improve the performance indicators of PMC and to provide informational support in decision-making process. To achieve the goal the following tasks are to be undertaken:

• to analyze the business-process “Report preparation” (model AS-IS);

• to analyze the existing solutions in the sphere;

• to prepare the draft proposal;

• to choose the software for optimization;

• to develop the system “Report preparation and audit” (TO-BE model);

• to prove economic effectiveness of the project.

Detecting and analyzing problems in PMC

PMC is a company providing the information and technological interaction between organizations rendering services to the public, dealers and customers. PMC owns a modern payment processing center and operates in all regions of Russian Federation. The company maintains payment processing for telecommunication and mobile services, Internet, satellite and cable television, utilities, sale and booking of tickets and other services.

The staff of PMC performs a set of functions among which creating daily and monthly financial reporting is a core one. Employees check the payments registered and form payment documents, which further on are used for mutual settlements between dealers, PMC and banks. Moreover, these documents represent leverage in case of disagreement in mutual settlements. Hence, the financial reporting, created by employees of PMC has to present correct and uncontradictory data about payments as it influences the correctness and timeliness in mutual settlements between participants of payment transfer. Thus, accredited reporting system is a necessary instrument in PMC.

Today, the staff of PMC uses its own reporting system, which is deployed on corporate portal of the organization. The key problem with this system is that, according to the performance indicators in PMC, it fails to provide an effective tool for report preparation and decision-making. To solve these problems it was proposed to develop a new reporting system with implementation of Data Mining techniques.

Before development of a new system for PMC the AS-IS model of the report preparation was designed to define problems in details. The model was created via MS Visio 2007 in IDEF-0 notation. As it was previously mentioned the key role of financial reporting in PMC is to provide correct and timeliness data to counterparts (dealers and banks) for further productive cooperation. To go into the process of preparing financial reporting at large the following business-processes were determined: creating a report, forwarding it to counterparts and receiving the confirmation from them. The simplified AS-IS model is demonstrated below.

However, during the process of designing the AS-IS model the following problems were detected:

• enormous amount of incoming data from several external systems;

• inefficiency of currently used reporting system;

• necessity of repeating daily routine operations;

• lack of opportunities to save the adjusted settings of the reports;

• difficulty in data manipulations in reports;

• non-optimized and costly process of report confirmation;

• absence of proper and reliable decision-making tools.

Picture 1. AS-IS model of PMC

Developing the requirements for a new system in PMC

Before directly going into the process of developing a new system, the requirements for the system, based on the detected problems, have been created. The requirements are mostly dedicated to financial mechanisms, the types of preset reports for daily and monthly reporting and the scheme of report confirmation by the counterparts. Financial mechanisms that are required to be implemented represent to broad categories:

• Calculations of basic sums and the period of their disbursement.

• Commission calculations and their payout.

Each of them has its own features which are specified in terms of an agreement.

The preset types of reports required by PMC management are discussed under the following headings:

• Consolidated report by the payment receiver. Payment receiver is an organization which provides various services to the public.

• Specification of the consolidated report by payment receivers.

• Consolidated report by the payment sender. Payment sender is an organization, namely payment agent, which receives payments from consumers and carries them out to PMC.

• Specification of the consolidated report by the payment sender.

• Mutual settlement report.

Besides preset reports, users have to be enabled to set their own reports.

A new scheme of report confirmation will be developed by the corporate portal modifications. It is planned, that Reporting sector on the portal will be available for counterparts where they will confirm the data of the reports on-line. As this paper is mostly oriented on analytical part of the system, the process of optimizing report confirmation is described only in a conceptual form.

Furthermore, the Data Mining technics that are planned to be developed in the new system will allow employees of PMC to:

• Predict the most and least perspective counterparts;

• Predict potential profit;

• Propose proper actions to be taken to increase profit.

It is supposed that the clustering, classification and neural networks algorithms will be applied [].

Choosing middleware toolkit for a new ARS in PMC

Before, choosing the most effective and appropriate instrument for system development, the proper software was analyzed. Based on the investigations of Gartner – the world's leading information technology research and advisory company, the following BI vendors were considered:

• Business Objects.

• Cognos.

• Microsoft.

• Oracle.

• SAP.

• SAS.

Briefly describing every of these venders mentioned above, Business Objects provides qualitative platform with reliable and easy-to-use technology of report preparation. This platform in considered to be an all-purpose, however, OLAP mechanisms is the blind side of Business Objects. Meanwhile, Cognos platform is capable to interact with IBM technologies. But, Data Mining mechanisms are significantly poorer than those offered by competitors such as Oracle. Oracle is famous for its high-power OLAP Engine and is capable to be integrated with MS Office components. Despite this fact, the technical support of Oracle is not developed enough. The main advantage of SAP platform is that it allows users not only to form preset reports but also to create self-service report one. Nevertheless, this platform is considered to be rather costly. And finally, in comparison with its competitors, SAS-platform supplies users and developers with the most powerful tool for advanced analytical solutions, however, many SAS-applications require knowledge of technical programming language – SAS [].

Every of these platforms provide developers and users with an integration tools, instruments for visualizing the data and instruments for intellectual analysis and OPAL technology. But in spite of this fact, Microsoft BI products were chosen. MS BI products supply customers with beneficial pricing debugged OLAP technology, broad spectrum of Data Mining methods, and integration with MS Office. The last of mentioned is especially attractive for PMC as they operate on MS Office products []. Thus, employees of PMC will be able to connect to data cube from MS Excel 2007 and further versions, create reports and publish them on the corporate portal of organization for further confirmation by their business associates.

General principles of a new ARSin PMC

While developing a new analytical reporting system for PMC, the replication on external sources will be set vie MS SQL Server Management Studio 2005 and its Database Engine. The source code of tables, view, procedures and functions will be created and stored in MS Visual Studio 2008 via Analysis Services project. All these objects will be available from MS SQL Server Management Studio for more convenient management.

The data processing is supposed to be executed daily every ten seconds in order to keep permanent income of actual data into the system.

The multi-dimensional model of this system will be also designed via Analysis Services. The multi-dimensional model of the system is supposed to supply users with the following information []:

• Information on the payment sender and receiver.

• Information on the mutual settlements between counterparts and PMC.

• Time of creation of the payment, its processing and receiving.

• Conditions of basic sums and commission payments.

• Payment operation, including creation, processing, canceling and denying states of payment.

Every of these kinds of information will be presented in the form of one of a five specific cube dimensions. In addition, the multi-dimensional model of the system will have one fact table. All calculated measures will be saved there. The basic required calculated measures are:

• Quantity of payments.

• Sum of payments.

• Sum of payment senders’ transfers. Payment senders’ transfers are the amount of money sent to the PMC from counterparts.

• Sum of PMC’s transfers. PMC’s transfers are the monetary expression of the commission sums paid to counterparts.

• Quantity of canceled payments.

• Sum of canceled payments.

• Quantity of denied payments.

• Sum of denied payments.

The predictive mechanisms will be developed via MS Analyses Services and its Data Mining mechanisms. For extracting valuable information for PMS three Data Mining mechanisms were chosen: clustering, classifications and neural networks.

Finally, to accomplish this practical work, the detailed reports such as a specification of the consolidated report by the payment receivers will be developed via MS Reporting Services.

Expected results

As a result of this study, the developed system will optimize the process of report preparation and provide an effective and reliable tool for decision-making process. Thus, the system will enable employees to:

• receive correct and uncontradictory data about payments;

• use preset report forms to prepare daily and monthly financial reporting;

• set their own reports;

• get counterparts’ confirmations through the Web-interface;

• arrange the dependency, classification and prediction analysis of interaction with business associates by implemented Data Mining algorithms.

Turning to the practical evidence, with the implementation of the system developed the following indicators of a company’s performance are expected to be improved []:

• the increase in the quality of reports: compliance with data types and formats, minimization of calculation errors;

• the decrease in labor and time costs on report preparation in organization due to the easy-manipulated instrument and preset reports;

• the simplification of interaction between PMC and its business associates on account of Web-interface confirmation;

• the increase in the quantity and quality of proper and valuable management decision in the organization due to implemented Data Mining tools.

Conclusion

Overall, this paper has given an account of and reason for the widespread necessity of analytical reporting systems, particularly in financial sphere. This study was undertaken to determine the particular problems of report preparation and to develop an effective solution.

During this study the AS-IS model was analyzed, including detailed analysis of report preparation process. The result of this analysis was a set of problems revealed. For better perception of the AS-IS model the IDEF-0 notation was used. To eliminate the problems detected the overview of proper solutions in the subject area was prepared. After that, the draft proposal was created to unite all requirements for the new system. Based on the draft proposal the proper solution of the problem was provided and the software choice was validated. The proposed solution was taken as a basis for TO-BE model.

Therefore, the findings of this study suggest that developed system is expected to become an efficient instrument for report preparation. It is also shown that, the system developed is aimed at stabilization of business connections between PMC and business associates. Finally, the Data Mining mechanisms, which are suggested to be implemented into the system, are intended to provide the company with valuable information for further management decisions and constant growth of productivity.

References

1. Fabrice Guillet, Howard J. Hamilton (Eds.). Quality Measures in Data Mining // Polish Academy of Science, Systems Research Institute, Poland.

2. Gartner, Inc [Electronic resource] //Article: Magic Quadrant for Business Intelligence and Analytics Platforms. URL: http://www.gartner.com/technology/reprints.do?id=1-1DZLPEP&ct=130207&st=sb

3. Sanjay Goil, Alok Choudhary. High Performance Multidimensional Analysis and Data Mining // Center for Parallel and Distributed Computing, Department of Electrival&Computer Engineering, Northwestern University, Technological Institute.

4. Stefan Zemke. Data Mining for Prediction Financial Series Case, Doctoral Thesis// The Royal Institute of Technology. Department of Computer and System Sciences, December, 2003.

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Scientific adviser: Doctor of Science, associat professor, Elena B. Zamyatina

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