Biliana Alexandrova-Kabadjova

Evolutionary Learning of the Optimal Pricing Strategy in an Artificial Payment Card Market

This chapter introduces an artificial payment card market in which we model the interactions between consumers, merchants and competing card issuers with the aim of determining the optimal pricing structure for card issuers. We allow card issuers to charge consumers and merchants fixed fees, provide net benefits from card usage and engage in marketing activities. The demand by consumers and merchants is only affected by the size of the fixed fees and the optimal pricing structure consists of a sizeable fixed fee to consumers, no fixed fee to merchants, negative net benefits to consumers and merchants as well as a high marketing effort.

The Mexican Experience in How the Settlement of Large Payments is Performed in the Presence of a High Volume of Small Payments

Payment systems play a key role in the financial infrastructure of all modern economies. Participants of payment systems need access to intraday liquidity to fulfill their payment obligations. They do that either using their own funds, which are costly, or recycling incoming payment. In order to rely on incoming payments, banks could delay the settlement of their own payment obligations. From the regulators’ point of view it is important to know to what degree participants rely on the payments they receive from others. In Mexico, this is among the first studies that analyze from this perspective the intraday liquidity management of the Real Time Settlement Payment System, SPEI. We examine a data set of transactions from April 7 to May 7, 2010 in order to get insights of the participants’ behavior regarding the delay of sending payment orders.

An agent-based model of interactions in the payment card market

We develop an agent-based model of the competition between payment cards by focusing on the interactions between consumers and merchants determining the subscription and usage of cards. We find that after a short period of time the market will be dominated by a small number of cards, even though there do not exist significant differences between cards and the market is fully competitive. In contrast to the existing literature we focus on the dynamics of market shares and emergence of multi-homing rather than equilibrium outcomes.

Simulation in Computational Finance and Economics Tools and Emerging Applications

The works presented in this book can be used as an inspiration for economic researchers interested in creating their own computational models in their respective fields.

Competition is Bad for Consumers: Analysis of an Artificial Payment Card Market

This paper investigates the competition between payment card issuers in an artificial payment card market. In the market we model the interactions between consumers, merchants and competing card issuers and obtain the optimal pricing structure for card issuers. We allow card issuers to charge consumers and merchants with fixed fees, provide net benefits from card usage and engage in marketing activities. We establish that consumers benefit from a reduction of competing payment cards through lower fees and higher net benefits while merchants remain largely unaffected. The two-sided nature of the market leads to the result that more competitors do actually reduce competition for customers.

Understanding Bank Failure: A Close Examination of Rules Created by Genetic Programming

This paper presents a novel method to predict bankruptcy, using a Genetic Programming (GP) based approach called Evolving Decision Rules (EDR). In order to obtain the optimum parameters of the classifying mechanism, we use a data set, obtained from the US Federal Deposit Insurance Corporation (FDIC). The set consists of limited financial institutions’ data, presented as variables widely used to detect bank failure. The outcome is a set of comprehensible decision rules, which allows to identify cases of bankruptcy. Further, the reliability of those rules is measured in terms of the true and false positive rate, calculated over the whole data set and plot over the Receiving Operating Characteristic (ROC) space. In order to test the accuracy performance of the mechanism, we elaborate two experiments: the first, aimed to test the degree of the variables’ usefulness, provides a quantitative and a qualitative analysis. The second experiment completed over 1000 different re-sampled cases is used to measure the performance of the approach. To our knowledge this is the first computational technique in this field able to give useful insights of the method’s predictive structure. The main contributions of this work are three: first, we want to bring to the arena of bankruptcy prediction a competitive novel method which in pure performance terms is comparable to state of the art methods recently proposed in similar works, second, this method provides the additional advantage of transparency as the generated rules are fully interpretable in terms of simple financial ratios, third and final, the proposed method includes cutting edge techniques to handle highly unbalanced samples, something that is very common in bankruptcy applications.

Bankruptcy Prediction for Banks: An Artificial Intelligence Approach to Improve Understandability

Artificial Intelligence (AI) is a prominent field within Computer Science whose main goal is automatic problem solving. Some of the foundations of this area were established by Alan M. Turing in his two seminal papers about machine intelligence [39] and [40]. Machine Learning (ML) is an important branch within the AI field which currently is on an intensive stage of development due to its wide range of applications. In particular, ML techniques have recently gained recognition in finance, since they are capable to produce useful models. However, the difficulty, and even the impossibility, to interpret these models, has limited the use of ML techniques in some problems where the interpretability is an important issue. Bankruptcy prediction for banks is a task which demands understandability of the solution. Furthermore, the analysis of the features (input variables), to create prediction models, provides better knowledge about the conditions which may trigger bank defaults. The selection of meaningful features before executing the learning process is beneficial since it reduces the dimensionality of the data by decreasing the size of the hypothesis space. As a result, a compact representation is obtained which is easier to interpret. The main contributions of this work are: first, the use of the evolutionary technique called Multi-Population Evolving Decision Rules MP-EDR to determine the relevance of some features from Federal Deposit Insurance Corporation (FDIC) data to predict bank bankruptcy. The second contribution is the representation of the features’ relevance by means of a network which has been built by using the rules and conditions produced by MP-EDR. Such representation is useful to disentangle the relationships between features in the model, this representation is aided by metrics which are used to measure the relevance of such features.

Profit-Maximizing Strategies for an Artificial Payment Card Market. Is Learning Possible?

In this paper, we study the dynamics of competition in the payment card market. This is done through a multi-agent based model, which captures explicitly the commercial transactions at the point of sale between consumers and mer-chants. Through simulation, we attempt to model the demand for payment instruments on both sides of the market. Constrained by this complex demand, a Generalised Population Based Incremental Learning (GPBIL) algorithm is applied to find a profit-maximizing strategy, which in addition has to achieve an average number of card transactions. In the present study we compare the performance of a profit-maximizing strategies obtained by the GPBIL algorithm versus the performance of randomly selected strategies. We found that under the search criteria used, GPBIL was capable of improving the price structure and price level over randomly selected strategies.

Direct Participants’ Behavior Through the Lens of Transactional Analysis: The Case of SPEI®

This paper presents a methodology to study the flow of funds in large value payment systems (LVPSs). The presented algorithm separates the flow of payments in two categories: (1) external funds, i.e. funds transferred from other financial market infrastructures (FMIs) or provided by the central bank and (2) the reuse of incoming payments. Our method further studies the flow of intraday liquidity under the framework of its provision within the Mexican FMIs. The aim is to evaluate the impact of the intraday liquidity provision, and understand how liquidity is transmitted to participants in the Mexican LVPS SPEI®;.