Die kommerzielle Mikrofinanz in Lateinamerika

Wenn die Mikrofinanz kommerziell betrieben wird, wie zunehmend in Lateinamerika, zeichnet sich diese Branche dadurch aus, dass sie praktisch ohne Spenden und Subventionen auskommt, und sie nach wirtschaftlichen Prinzipien funktioniert. Die führenden Mikrofinanz Institutionen (MFIs) sehen zunehmend wie traditionelle Banken aus, die als Zielgruppe ärmere Personen haben. Der Wettbewerb zwischen MFIs in Lateinamerika ist mittlerweile stark. Es wird manchmal nicht transparent genug vorgegangen, und man beginnt sich von der ursprünglichen Tätigkeit – die Vergabe von Investitionskrediten – zu entfernen. Durch hohe Zinsen für die Kunden und eine steigende Effizienz erreichen in Lateinamerika manche kommerziellen Mikrofinanz Institutionen sehr hohe Gewinne. Die andere Seite der Medaille ist die steigende Kundenüberschuldung, die zu einem erstrangigen Problem geworden ist. In dieser Arbeit wird nicht die Frage gestellt, ob die Mikrofinanz kommerziell betrieben werden soll oder nicht. Aufgrund mancher Argumente1 ist man hier dem kommerziellen Ansatz nämlich nicht ganz abgeneigt. Nur: Diese Argumente werden kritisch betrachtet, denn die Grundannahme dieser Arbeit ist, dass die Hauptaufgabe der Mikrofinanz bei der Armutslinderung liegt. Es wird eine Bestandsaufnahme der Situation (Die kommerzielle Mikrofinanz in Lateinamerika) gemacht. Dafür werden folgende Fragen gestellt: Warum wird die Mikrofinanz in Lateinamerika kommerziell und wie findet diese Wandlung statt? Welche sind die aktuellen Gefahren dieser Branche? Was lässt erahnen, dass die Mikrofinanz für ihre Kundschaft Positives bringt? Warum sind die Zinsen für Mikrokredite so hoch? Trägt die Mikrofinanz zur Armutslinderung überhaupt bei? Abschließend wird festgestellt, dass die kommerzielle Mikrofinanz in Lateinamerika sowohl für Betreiber als auch für Kunden Potential aufweist, dass aber, um Missbrauch gegenüber letzteren zu vermeiden, dringend Schranken und Aufsichten notwendig sind. Die Bedeutung vom lateinamerikanischen Fall ist groß, weil in dieser Region die Mikrofinanz besonders kommerziell ist. Dieser Ansatz gewinnt derzeit weltweit an Bedeutung.When microfinance is operated commercially, as it increasingly is in Latin America, this industry is characterized by the fact that it uses virtually no donations or subsidies, and works in accordance with economic principles. The leading microfinance institutions (MFIs) are now very similar to traditional banks, with the difference being that their clients are the poor. The competition between commercial MFIs in Latin America is now strong. There is an increasing lack of transparency, and MFIs are beginning to detach themselves from their original purpose - the allocation of investment loans. Due to high rates for customers and a rising level of efficiency, some institutions are gaining very high profits. The reverse side of the coin is the increasing customer debt, which has become a first-tier issue. This paper does not question whether microfinance should be commercially driven or not. Due to some of its advantages this paper is inclined to consider that the commercial approach is desirable2. However, the arguments are considered critically because the basic assumption is that the main task of microfinance lies in poverty alleviation. The aim of this paper is to make an inventory of the situation (the commercial microfinance in Latin America). For this purpose, the following questions are being asked: - Why is microfinance in Latin America becoming increasingly commercial? - How is this transformation taking place? - What are the current risks in this industry? - What indicators suggest that microfinance has positive effects on its customers? - Why are interest rates for micro loans so high? And finally: - Does microfinance contribute to poverty alleviation? The conclusion to this paper is that commercial microfinance in Latin America has a good potential for both its administors and customers but, in order to prevent abuse against the latter, oversight and supervision is urgently needed. The Latin American case is significant because in this region the commercial approach is being broadly applied. This approach is gaining importance worldwide

Airport passenger flow prediction using LTSM Recurrent Neural Networks

Passenger flow management is an important issue at many airports around the world. There are high concentrations of passengers arriving and leaving the airport in waves of large volumes in short periods, particularly in big hubs. This might cause congestion in some locations depending on the layout of the terminal building. With a combination of real airport data, as well as synthetic data obtained through an airport simulator, a Long Short-Term Memory Recurrent Neural Network has been implemented to predict the possible trajectories that passengers may travel within the airport depending on user-defined passenger profiles. The aim of this research is to improve passenger flow predictability and situational awareness to make a more efficient use of the airport, that could also positively impact communication with public and private land transport operators

Data mining in MRO

Data mining seems to be a promising way to tackle the problem of unpredictability in MRO organizations. The Amsterdam University of Applied Sciences therefore cooperated with the aviation industry for a two-year applied research project exploring the possibilities of data mining in this area. Researchers studied more than 25 cases at eight different MRO enterprises, applying a CRISP-DM methodology as a structural guideline throughout the project. They explored, prepared and combined MRO data, flight data and external data, and used statistical and machine learning methods to visualize, analyse and predict maintenance. They also used the individual case studies to make predictions about the duration and costs of planned maintenance tasks, turnaround time and useful life of parts. Challenges presented by the case studies included time-consuming data preparation, access restrictions to external data-sources and the still-limited data science skills in companies. Recommendations were made in terms of ways to implement data mining – and ways to overcome the related challenges – in MRO. Overall, the research project has delivered promising proofs of concept and pilot implementation

Airport passenger flow prediction using simulation data farming and machine learning

Passenger flow management is an important issue at many airports around the world. There are high concentrations of passengers arriving and leaving the airport in waves of large volumes in short periods, particularly in big hubs. This might cause congestion in some locations depending on the layout of the terminal building. With a combination of real airport data, as well as synthetic data obtained through an airport simulator, a Long Short-Term Memory Recurrent Neural Network has been implemented to predict the possible trajectories that passengers may travel within the airport depending on user-defined passenger profiles. The aim of this research is to improve passenger flow predictability and situational awareness to make a more efficient use of the airport, that could also positively impact communication with public and private land transport operators