A data mining framework to model consumer indebtedness with psychological factors

Modelling Consumer Indebtedness has proven to be a problem of complex nature. In this work we utilise Data Mining techniques and methods to explore the multifaceted aspect of Consumer Indebtedness by examining the contribution of Psychological Factors, like Impulsivity to the analysis of Consumer Debt. Our results confirm the beneficial impact of Psychological Factors in modelling Consumer Indebtedness and suggest a new approach in analysing Consumer Debt, that would take into consideration more Psychological characteristics of consumers and adopt techniques and practices from Data Mining

A data mining framework to model consumer indebtedness with psychological factors

Modelling Consumer Indebtedness has proven to be a problem of complex nature. In this work we utilise Data Mining techniques and methods to explore the multifaceted aspect of Consumer Indebtedness by examining the contribution of Psychological Factors, like Impulsivity to the analysis of Consumer Debt. Our results confirm the beneficial impact of Psychological Factors in modelling Consumer Indebtedness and suggest a new approach in analysing Consumer Debt, that would take into consideration more Psychological characteristics of consumers and adopt techniques and practices from Data Mining

The regulation of consumer credit information systems: Is the EU missing a chance?

This article examines the legal framework of consumer credit information systems in the EU in view of a single retail credit market. It puts forward the proposition that positive law is inadequate to strike a balance between legitimate concerns over consumers' civil liberties, institutional guarantees, and the needs of the credit industry. It suggests that the EU should enact industry-specific legislation, and the new consumer credit directive should represent the appropriate forum for its regulation. So far, however, the proposed directive maintains the status quo and is far from satisfactory, leading to the conclusion that the EU is missing a chance to re-think a regulatory model to support a healthy single consumer credit market in which consumers receive adequate protection

Overcoming over–indebtedness with AI - A case study on the application of AutoML to research

Dissertation presented as the partial requirement for obtaining a Master's degree in Data Science and Advanced AnalyticsThis research examines how artificial intelligence may contribute to better understanding and overcoming over-indebtedness in contexts of high poverty risk. This study uses a field database of 1,654 over-indebted households to identify distinguishable clusters and to predict its risk factors. First, unsupervised machine learning generated three overindebtedness clusters: low-income (31.27%), low credit control (37.40%), and crisis-affected households (31.33%). These served as basis for a better understanding on the complex issue that is over-indebtedness. Second, a predictive model was developed to serve as a tool for policymakers and advisory services by streamlining the classification of overindebtedness profiles. On building such model, an AutoML approach was leveraged achieving performant results (92.1% accuracy score). Furthermore, within the AutoML framework, two techniques were employed, leading to a deeper discussion on the benefits and inner workings of such strategy. Ultimately, this research looks to contribute on three fronts: theoretical, by unfolding previously unexplored characteristics on the concept of over-indebtedness; methodological, by proposing AutoML as a powerful research tool accessible to investigators on many backgrounds; and social, by building real-world applications that aim at mitigating over-indebtedness and, consequently, poverty risk

Augmented neural networks for modelling consumer indebtness

Consumer Debt has risen to be an important problem of modern societies, generating a lot of research in order to understand the nature of consumer indebtness, which so far its modelling has been carried out by statistical models. In this work we show that Computational Intelligence can offer a more holistic approach that is more suitable for the complex relationships an indebtness dataset has and Linear Regression cannot uncover. In particular, as our results show, Neural Networks achieve the best performance in modelling consumer indebtness, especially when they manage to incorporate the significant and experimentally verified results of the Data Mining process in the model, exploiting the flexibility Neural Networks offer in designing their topology. This novel method forms an elaborate framework to model Consumer indebtness that can be extended to any other real world application

Potential of psychological information to support knowledge discovery in consumer debt analysis

In this work, we develop a Data Mining framework to explore the multifaceted nature of consumer indebtedness. Data Mining with its numerous techniques and methods poses as a powerful toolbox to handle the sensitivity of these data and explore the psychological aspects of this social phenomenon. Thus, we begin with a series of transformations that deal with any inconsistencies the data may contain but more importantly they capture the essential psychological information hidden in the data and represent it in a new feature space as behavioural data. Then, we propose a novel consensus clustering framework to uncover patterns of consumer behaviour which draws upon the ability of cluster ensembles to reveal robust clusters from diffcult datasets. Our Homals Consensus, models successfully the relationships between different clusterings in the cluster ensemble and manages to uncover representative clusters that are more suitable for explaining the complex patterns of a socio-economic dataset. Finally under a supervised learning approach the behavioural aspects of consumer indebtedness are assessed. In more detail, we take advantage of the exibility Neural Networks provide in determining their architecture in order to propose a novel Neural Network solution, named TopDNN, that can handle non-linearities in the data and takes into account the extracted behavioural knowledge by incorporating it in the model. All the above sketch an elaborate framework that can reveal the potential of the behavioural data to support Knowledge Discovery in Consumer Debt Analysis on one hand and the ability of Data Mining to supplement existing models and theories of complex and sensitive nature on the other

Potential of psychological information to support knowledge discovery in consumer debt analysis

In this work, we develop a Data Mining framework to explore the multifaceted nature of consumer indebtedness. Data Mining with its numerous techniques and methods poses as a powerful toolbox to handle the sensitivity of these data and explore the psychological aspects of this social phenomenon. Thus, we begin with a series of transformations that deal with any inconsistencies the data may contain but more importantly they capture the essential psychological information hidden in the data and represent it in a new feature space as behavioural data. Then, we propose a novel consensus clustering framework to uncover patterns of consumer behaviour which draws upon the ability of cluster ensembles to reveal robust clusters from diffcult datasets. Our Homals Consensus, models successfully the relationships between different clusterings in the cluster ensemble and manages to uncover representative clusters that are more suitable for explaining the complex patterns of a socio-economic dataset. Finally under a supervised learning approach the behavioural aspects of consumer indebtedness are assessed. In more detail, we take advantage of the exibility Neural Networks provide in determining their architecture in order to propose a novel Neural Network solution, named TopDNN, that can handle non-linearities in the data and takes into account the extracted behavioural knowledge by incorporating it in the model. All the above sketch an elaborate framework that can reveal the potential of the behavioural data to support Knowledge Discovery in Consumer Debt Analysis on one hand and the ability of Data Mining to supplement existing models and theories of complex and sensitive nature on the other

Bolivia during the global crisis 1998-2004: towards a ‘macroeconomics of microfinance

The macroeconomic role of microfinance appears to have varied enormously between country cases, as notably exposed by the recent wave of macro-economic crises. For example, in Indonesia in the late 1990s microfinance appears to have played a notably counter-cyclical role, whereas in Bolivia, the main focus of this paper, its role was in most cases to intensify rather than restrain the crisis. We find part of the explanation for this in the behaviour of government towards microfinance (much more conciliatory towards defaulting debtors in the Bolivian case) and in the structure of demand (unfavourable, in Bolivia, to the distribution and service sector which is the main market for microenterprise). However, closer examination of the Bolivian case suggests that institutional design also played an important role. In particular, those organisations which provided savings, training and quasi-insurance services bucked the trend of rising default rates and falling lending through the crisis and did particularly well, whereas the new breed of consumer-credit microfinance organisations did particularly badly and in several cases went out of business. This experience suggests,in particular, that it may be appropriate to call into question the fashionable´ minimalist´ (credit-only) model of microfinance, as certainly in Bolivia it was principally the credit-plus institutions which proved more financially disciplined and more resilient to crisis

Digital Consumption and Over-Indebtedness Among Young Adults in Sweden

This LUii report presents empirical results from studies on consumption and over-indebtedness in Swedish young adults in a digital context. The studies have been conducted through in-depth interviews with municipal financial counsellors as well as a quantitative survey with approximately 1,100 respondents in a sample representative of Swedes from 18 to 25 years old. The report includes an extensive literature review on over-indebtedness and consumption in a digital context. The purpose of the project has been to form a better understanding of in what ways the digitization of our everyday lives – including consumption, credit handling and overall communication – influences economic vulnerability among young adults. The research report is written by researchers linked to Lund University Internet Institute (LUii) and has been funded by the Swedish Enforcement Authority. The research has also been conducted in connection with a wider interdisciplinary research theme on ”The Credit Society”, at the Pufendorf Institute for Advanced Studies at Lund University. The research group is led by Stefan Larsson, Associate Professor in Technology and Social Change, and involves Lupita Svensson, PhD in Social Work, and Hanna Carlsson, PhD in Information Science. The work with the literature review received invaluable help from Fredrik Åström, bibliometrician and Associate Professor at Lund University. The empirical research was conducted during 2015 and early 2016

Quality of life, well-being and regional economic development: a thought piece

This think piece, sets out the case that the next round of Regional Economic Strategies (RESs) produced by RDAs, should explicitly make quality of life and well-being the organising principles at the heart of their visions and their strategies. An explicit focus on people’s quality of life potentially offers rigorous methodologies for differentiating between ‘good’ and ‘bad’ growth. In addition enhancing people’s well-being will support RDAs in successfully achieving their goals regarding enterprise, investment, skills, productivity and sustainable communities