Search results

This chapter introduces a risk control framework on credit card fraud instead of providing a solely binary classifier model. The anomaly detection approach is adopted to identify fraud events as the outliers of the reconstruction error of a trained autoencoder (AE). The trained AE shows fitness and robustness on the normal transactions and heterogeneous behavior on fraud activities. The cost of false-positive normal transactions is controlled, and the loss of false-negative frauds can be evaluated by the thresholds from the percentiles of reconstruction error of trained AE on normal transactions. To align the risk assessment of the economic and financial situation, the risk manager can adjust the threshold to meet the risk control requirements. Using the 95th percentile as the threshold, the rate of wrongly detecting normal transactions is controlled at 5% and the true positive rate is 86%. For the 99th percentile threshold, the well-controlled false positive rate is around 1% and 83% for the truly detecting fraud activities. The performance of a false positive rate and the true positive rate is competitive with other supervised learning algorithms.

Current prices, future estimates, and price statistics cannot reflect specific transaction experience generated by enterprise management. Instead, overt transaction data in accounts continue to constitute evidence of management decision-actions, even after apportioned among fiscal periods. And the interrelation of enterprise capital employed productively, and the income generated by its use, will continue whether or not accounts are kept. Yet, the significance of factual integrity for enterprise transaction experience is strongly influenced by the functional logic inherent in accounting.

In class action antitrust litigation, the standards for acceptable economic analysis at class certification have continued to evolve. The most recent event in this evolution is the United States Supreme Court’s decision in Comcast Corp. v. Behrend, 133 S. Ct. 1435 (2013). The evolution of pre-Comcast law on this topic is presented, the Comcast decision is thoroughly assessed, as are the standards for developing reliable economic analysis. This article explains how economic evidence of both antitrust liability and damages ought to be developed in light of the teachings of Comcast, and how liability evidence can be used by economists to support a finding of common impact for certification purposes. In addition, the article addresses how statistical techniques such as averaging, price-dispersion analysis, and multiple regressions have and should be employed to establish common proof of damages.

The term ‘strategy’ is one of the most frequently used terms in business, and its application in marketing is particularly common. Company strategy, market strategy, marketing strategy, sales strategy, promotion strategy, distribution strategy, low pricing strategy – it would take a long time to list all of them. Although this term is so commonly in use, its definition is not as straightforward and it can be interpreted in different ways. In comparison with tactical decisions, strategy is much more significant for an organisation as it brings long-lasting consequences. It is implemented by higher level managers on a regular basis, and it is based on external, often subjective information, so decisions – especially at the time they are made – are difficult to evaluate.

Taking into consideration the fact that strategy refers to a long-term rather than a short-term period, strategic decisions serve as the basis for undertaking operational activities. However, marketing refers to the market and the competition. It is possible to claim that marketing strategy is trying to find an answer to the question to which path an organisation should follow in order to achieve its goals and objectives. If, for example, a company has a goal to generate a profit of PLN 1 million by selling 100,000 pieces of a product, the market strategy should answer at least the following two questions:

1. Who will be our target group, for example, who will purchase the 100,000 pieces of the product?

2. Why is it us from whom a potential buyer should purchase the product?

Who will be our target group, for example, who will purchase the 100,000 pieces of the product?

Why is it us from whom a potential buyer should purchase the product?

The target market will be defined if a reply to the first question is provided. The second question identifies the foundations of competitive advantage. These two issues, that is, target market and competitive advantage are the strategic marketing issues. You cannot change your target group unexpectedly while competitive advantage is the basis for changing decisions regarding prices, promotions and sales.

This chapter describes the measures of marketing activities which refer to strategic aspects and testify a company’s market position – the measures of the performance of target groups and competitive advantage. Readers’ attention should be also focused on the indices that are less popular in Poland and, therefore, may be underestimated. It seems that some of them, for example, the index of marketing resources allocation and the marketing risk index, provide a lot of valuable information and, at the same time, make it possible to show the value of marketing investments. Their wider use in the near future is only a matter of time.

Management engages in self-examination by studying the account data reporting the results of prior decision-actions. Actual transaction experience data will be more significant than data that are hypothetical. This is so because back of financial statements lie consummated transactions reflecting planned efforts on the part of the buyer and planned performance on the part of the seller. With this in mind, profit as motivation can be over emphasized, although in reality it is still a good indication of sound prior policies, plans, and decisions of management. Standardized accounting, however, is not without its limitations, as it cannot reflect the individuality of competitive enterprise and their management.

For central banks who study the use of cash, acceptance of card payments is an important factor. Surveys to measure levels of card acceptance and the costs of payments can be complicated and expensive. In this paper, we exploit a novel data set from Hungary to see the effect of stratified random sampling on estimates of payment card acceptance and usage. Using the Online Cashier Registry, a database linking the universe of merchant cash registers in Hungary, we create merchant and transaction level data sets. We compare county (geographic), industry and store size stratifications to simulate the usual stratification criteria for merchant surveys and see the effect on estimates of card acceptance for different sample sizes. Further, we estimate logistic regression models of card acceptance/usage to see how stratification biases estimates of key determinants of card acceptance/usage.

The digital economy has significantly altered the way business is conducted and financial information is communicated. A rapidly growing number of organizations are conducting business and publishing business and financial reports online and in real-time. Real-time financial reporting is likely to necessitate continuous auditing to provide continuous assurance about the quality and credibility of the information presented. The audit process has, by necessity, evolved from a conventional manual audit to computer-based auditing and is now confronted with creating continuous electronic audits. Rapidly emerging information technology and demands for more timely communication of information to business stakeholders requires auditors to invent new ways to continuously monitor, gather, and analyze audit evidence. Continuous auditing is defined here as “a comprehensive electronic audit process that enables auditors to provide some degree of assurance on continuous information simultaneously with, or shortly after, the disclosure of the information.” This paper is based on a review of related literature, innovative continuous auditing applications, and the experiences of the authors. An approach for building continuous audit capacity is presented and audit data warehouses and data marts are described. Ever improving technology suggests that the real-time exchange of sensitive financial data will place constant pressure on auditors to update audit techniques. Most of the new techniques that will be required will involve creation of new software and audit models. Future research should focus on how continuous auditing could be constantly improved in various auditing domains including assurance, attestation, and audit services.