Self-service Systems Performance Evaluation and Improvement Model

Abstract. The paper analyses the topic of service system productivity and profitability. Main focus of the research is self-service area, namely, the increase of ATM network productivity. Paper presents performance evaluation of self-service systems and improvement model for its increasing profitability. This model combines internal and external quality criteria and provides detailed understanding of the main components of productivity evaluation and methods. Using the model it is possible to create evaluation and improvement tools for increasing productivity of self-service systems. Experimental result shows that using the developed productivity model, ANN method and optimization procedure, productivity of ATM cash management could be increased by approximately 33 percent

Aptarnavimo sistemų pelningumo tyrimai, realaus laiko sprendimų priėmimui, taikant intelektines sistemas

Summary presents self-service networks operational performance improvement and management system, which is adapted to manage the supply of ATMs’ cash flows. The system is created using multi-agent technologies and artificial neural networks. Adaptive neural network model has been created for the forecast of ATMs’ cash demand. Flexibility of the network is regulated in real time mode by limiting neural network weights, depending on process complexity, therefore such network better forecasts in real life situations. Evaluation and process improvement methodology has been created to optimize productivity of self-service systems, which includes: value-based self-service quality and performance criteria’s, and performance evaluation models. Using these models is possible to increase productivity of self-service systems. Using theoretical studies results a computer program enabling real-time monitoring and management of ATM cash flows was created. Analysis of high and low intensity of ATM network profitability showed that the created flexible neural network forecast method is more superior than classical methods of time series forecast (moving average, Holt, Winters, and ARMA), and is able to quite accurately forecast various time series of ATM cash demand. Based on studies found that using the created ANN method and optimization procedure, ATM cash management productivity may be approximately increased by 33 percent

Financial Data Anomaly Discovery Using Behavioral Change Indicators

In this article we present an approach to financial data analysis and anomaly discovery. In our view, the assessment of performance management requires the monitoring of financial performance indicators (KPIs) and the characteristics of changes in KPIs over time. Based on this assumption, behavioral change indicators (BCIs) are introduced to detect and evaluate the changes in traditional KPIs in time series. Three types of BCIs are defined: absolute change indicators (BCI-A), relative change indicators (ratio indicators BCI-RE), and delta change indicators (D-BCI). The technique and advantages of using BCIs to identify unexpected deviations and assess the nature of KPI value changes in time series are discussed and illustrated in case studies. The architecture of the financial data analysis system for financial data anomaly detection is presented. The system prototype uses the Camunda business rules engine to specify KPIs and BCI thresholds. The prototype was successfully put into practice for an analysis of actual financial records (historical data)

Financial Data Anomaly Discovery Using Behavioral Change Indicators

In this article we present an approach to financial data analysis and anomaly discovery. In our view, the assessment of performance management requires the monitoring of financial performance indicators (KPIs) and the characteristics of changes in KPIs over time. Based on this assumption, behavioral change indicators (BCIs) are introduced to detect and evaluate the changes in traditional KPIs in time series. Three types of BCIs are defined: absolute change indicators (BCI-A), relative change indicators (ratio indicators BCI-RE), and delta change indicators (D-BCI). The technique and advantages of using BCIs to identify unexpected deviations and assess the nature of KPI value changes in time series are discussed and illustrated in case studies. The architecture of the financial data analysis system for financial data anomaly detection is presented. The system prototype uses the Camunda business rules engine to specify KPIs and BCI thresholds. The prototype was successfully put into practice for an analysis of actual financial records (historical data)

Identifying irregular financial operations using accountant comments and natural language processing techniques

Featured Application The paper presents application of natural language processing techniques on accountant left comments to identify potentially irregular financial operations. Finding not typical financial operations is a complicated task. The difficulties arise not only due to the sophisticated actions of fraudsters but also because of the large number of financial operations performed by business companies. This is especially true for large companies. It is highly desirable to have a tool to reduce the number of potentially irregular operations significantly. This paper presents an implementation of NLP-based algorithms to identify irregular financial operations using comments left by accountants. The comments are freely written and usually very short remarks used by accountants for personal information. Implementation of content analysis using cosine similarity showed that identification of the type of operation using the comments of accountants is very likely. Further comment content analysis and financial data analysis showed that it could be expected to reduce the number of potentially suspicious operations significantly: analysis of more than half a million financial records of Dutch companies enabled the identification of 0.3% operations that may be potentially suspicious. This could make human financial auditing easier and more robust task

Identifying Irregular Financial Operations Using Accountant Comments and Natural Language Processing Techniques

Finding not typical financial operations is a complicated task. The difficulties arise not only due to the sophisticated actions of fraudsters but also because of the large number of financial operations performed by business companies. This is especially true for large companies. It is highly desirable to have a tool to reduce the number of potentially irregular operations significantly. This paper presents an implementation of NLP-based algorithms to identify irregular financial operations using comments left by accountants. The comments are freely written and usually very short remarks used by accountants for personal information. Implementation of content analysis using cosine similarity showed that identification of the type of operation using the comments of accountants is very likely. Further comment content analysis and financial data analysis showed that it could be expected to reduce the number of potentially suspicious operations significantly: analysis of more than half a million financial records of Dutch companies enabled the identification of 0.3% operations that may be potentially suspicious. This could make human financial auditing easier and more robust task