Credit Scoring

The main objective of this chapter is to propose a hybrid evolutionary feature selection approach for solving credit scoring problems subject to constraints. A hybrid scheme combining filter and wrapper-based approaches is proposed to develop an accurate credit scoring model with a high predictive performance. Initially, the minimum redundancy maximum relevance algorithm is applied to find an optimal set of features that is mutually and maximally dissimilar and can represent the response variable effectively, allowing for an ordering of features by their importance. Subsequently, an iterative procedure, where supervised machine learning algorithms such as the logistic regression and the linear-discriminant analysis are combined with an evolutionary optimization algorithm like the genetic algorithm, is applied to choose the feature subset that maximizes an appropriate classification measure according to the predefined features and subject to the predefined constraints. The performance of the proposed method is illustrated using standard credit scoring datasets.

Extremely Fast Heuristic Event-Driven Job Shop Scheduler With a New Class of Extended Petri Nets

A very fast scheduling system is proposed and experimentally investigated. The system consists of a job shop manager and dynamic models of machines. A schedule is created in the course of a close cooperation with models of the machines that generate driving events for the scheduler. The system is implemented with a new class of extended Petri nets and runs in the environment of the Petri-net tool WINSIM. The scheduler creates a schedule sequentially, without any form of enumerative search. To investigate the scheduler performance, a large number of experiments were conducted with the use of few strategies. Due to a unique mechanism of monitoring of triggering events in the Petri net, the developed scheduler runs at least hundreds of times faster than any known single-processor job shop scheduler.

The Impact of AI on Disintermediation Processes in the Tourism Industry

As an innovative technology, artificial intelligence (AI) leads to disruption and automation of almost every process and business model in almost any industry today. AI contributes to the process of disintermediation of value chains resulting in shrinking the producer-customer links. The chapter examines the influence of AI on disintermediation in the tourism industry. It investigates the changes and transformation of the value creation process and marketing in the tourism industry affected by AI technology. AI could transform and revolutionize every segment of the tourism industry. Thus, it can make tourism more efficient, with new value-added customer services. AI is considered an enabling tool for the creation of the so-called “smart tourism” as a new age of tourism development. As a digital tool, AI enables the tourism industry to get insight into the customer, understand tourist profiles and provide consumers with personalized and niche travel experiences.

Marketing and Artificial Intelligence

Technologies are changing marketing due to the amount of information available to consumers, along with information being generated by consumers. Marketers face a challenge with greater volume and variety of data generated at a faster rate than ever before along with fragmentation of channels. This data when combined with artificial intelligence presents an opportunity to marketers to provide value add at a granular level and a personalized customer experience round the clock 24/7/365. Treating customers as individuals by offering an optimized personalized offering, sending the right personalized message at the right time through their preferred channel is the promise of data fed into AI algorithms. Artificial intelligence has the potential to transform companies by making sense out of an insanely voluminous variety of data being generated with its ability to serve customers more effectively and efficiently, personalizing at scale.

Convolutional Neural Networks for Real-Time Eye Tracking in Interactive Applications

Face alignment and eye tracking for interactive applications should be performed with very low latency or users will notice the delay. In this chapter, a face alignment method for real-time applications is introduced featuring a convolutional neural network architecture for face and pose alignment. The performance of the novel method is compared to a face alignment algorithm included in the freely available OpenFace toolkit, which also focuses on real-time applications. The approach exceeds OpenFace's performance on both our own and the 300W test sets in terms of accuracy and robustness but requires significant parallel processing power, currently provided by the GPU. For the eye tracking application, stereo cameras are used as input to determine the position of a user's eyes in three-dimensional space. It does not require synchronized recordings, which may contain redundant information, and instead prefers staggered recordings, which maximize the number of possible model updates.

Non-Invasive Personalized In-Store Location-Based Marketing

This chapter shows a practical end-to-end solution that allows the integration of noninvasive location-based marketing advertisements finally binding physical and virtual in-store customer presence. The goal of the solution is to digitalize the business and improve the customer experience with the indoor proximity-based iBeacon technology for personalized marketing advertising. The architecture uses cheap battery powered iBeacon devices, Android App and a recommender system for sending noninvasive advertisement in the right moment to the right customer. The intelligent combination of loyalty programs, personalized location-based marketing campaigns, and connection to existing CRM systems will enable the desirable increase in customer loyalty by also creating ideal circumstances for custom omnichannel marketing.

Use of Finite Markov Chains in Business Problems Involving Decision Making and Case-Based Reasoning

Artificial intelligence (AI) is the branch of computer science focusing on the creation of intelligent machines that mimic human reasoning and behaviour. Probability theory is among the mathematical tools used in AI applications to deal with situations of uncertainty caused by randomness. In particular, the Markov chain (MC) theory is a smart combination of probability and linear algebra that offers ideal conditions for modelling such situations. International business is about the trade of goods, services, technology, capital, and knowledge at a global level, while decision making (DM) and case-based reasoning (CBR) are among the processes that are frequently used in this field. In this chapter, an absorbing and an ergodic MC model are developed on the steps of DM and CBR respectively for representing mathematically those two processes, thus providing valuable information about their evolution. The examples presented are connected to international business applications.

Fuzzy Logic in Portfolio Selection

This chapter deals with possibilities of using fuzzy logic in the process of selecting stocks for the portfolio. Often investors observe specific cognitive uncertainty problems within the portfolio selection. This is where fuzzy logic can help with the final decision. After the description of the selected fuzzy logic concepts and comparison with other similar approaches, an empirical section provides detailed insights into the applications of such methodology. The analysis utilizes weekly data for the period January 2018 – April 2019 for 20 selected stocks in order to exhibit the usefulness of the observed approach in the portfolio selection.

Finance in the World of Artificial Intelligence and Digitalization

This chapter considers finance in the world of artificial intelligence and digitalization with a special focus on the role and physical forms of money throughout history, modes of executing financial transactions, development of banking and investment industry, and its overall importance for financial management. Innovative and alternative business solutions in the financial industry result from the development of modern technologies, information and communication networks, smart devices, and various applications. Digital money and cryptocurrencies are extremely interesting current phenomena producing a broad range of speculations and mystery about their role in the future. Electronic payments bring many benefits and opportunities for corporate finance, capital markets, and investment and banking activities. Digitalization of business operations and use of artificial intelligence applications increases competitiveness and efficiency of all controlling processes and improves corporate risk management and at the same time decreases information asymmetry in the market.

Reinforcement Learning in Social Media Marketing

In this chapter, the authors describe an architecture for reinforcement learning in social media marketing. The rule bases used for action selection within the architecture build upon many-valued (fuzzy) logic. Action evaluation and internal learning is based on neural network like structures. In using variables measuring the effect of advertising, we must understand direction of influence between advertiser, owning the content of the advertisement, and advertisee, as the target of an advertisement, and as facilitated by social media marketing. Examples are drawn from Facebook marketing.