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The debt crisis of the 1980s officially started in August 1982 when Mexico defaulted on its commercial bank loans and led to a 'lost decade' of non-growth for developing countries in Latin America and elsewhere.. But its genesis dates to the growth of developing country debt following the oil crises of the early and late seventies. This chapter analyses the reasons for the debt crisis and the responses of the international community to the problems faced by two groups of developing countries: middle income countries heavily indebted to commercial banks and highly indebted poor countries, especially in Sub-Sahara Africa whose debt was to official donors.

In general agreement with the predictive coding approach to cognitive processing, this chapter develops the thesis that our own mechanisms for decision-making and planning are also used concurrently for testing hypothetical explanations of the actions of others. It argues that in this inverse use, the top-down inputs to planning – typically classified as beliefs, desires, and emotions – would default to agent-neutral coding. That is, inputs remain undifferentiated between self and other, unless corrected, e.g., in response to predictive error. In such corrections, the inputs are tweaked (perhaps by way of Bayesian inference) with the aim of (re)producing the observed action. The default agent-neutral coding of inputs would have notable consequences. It would help explain the phenomenology of a shared world: why we seem to understand actions primarily in terms of a shared, mutually known world, and only secondarily (chiefly, when this proves inadequate) in terms of the mental states of individuals. It would also help explain why factivity and knowledge are central to our understanding of others. Finally, by defaulting to “one code fits all,” such a system for action interpretation would achieve maximum code compression – reason for confidence that it is the system we actually have.

As part of bad economic planning carried out by people, they sometimes declare themselves debtors. Debts are a legal effect that causes a socio-economic impact. The present work proposes a method to measure the legal and socioeconomic effect in debtors declared in default. The method bases its operation on fuzzy logic through user experience. A case study was implemented in the canton of Pastaza with the aim of measuring the socioeconomic legal effect from which a working tool for decision-making is obtained.

In the lending industry, such as banks or finance companies, investors provide loans to borrowers in exchange for the promise of repayment with interest. This two-way collaboration has many potential risks that need to be assessed. If the borrower repays the loan, then the lender would make a profit from the interest. However, if the borrower fails to repay the loan, then the lender loses money. Therefore, lenders face predicting the risk of a borrower being unable to repay a loan at any step of the credit process. Many financial institutes calculate FICO scores of a customer based on some factors such as the Payment History (35%), Amounts owed (30%), Length of credit history (15%), Credit mix (10%), and New credit (10% of the total point) of the customer, that score qualifies how risk customers are, how repayment ability of the customer. FICO score (Fair Isaac Corporation) is a traditional approach used broadly in finance fields to estimate risks and give a worthy credit amount to the customer. In this study, a new way is to use Artificial intelligence to predict the above problems. It bases on credit information and more personal information of the customers to predict its repayment ability. The data collected from VietCredit Finances used for training several Machine Learning models to determine if the borrower can repay its loan. Some linear, nonlinear models involved are Logistic Regression, Linear Discriminant Analysis, K Nearest Neighbors, Decision Tree, Naive Bayes, Support Vector Machine. The algorithm turning and Boosting and Bagging ensemble methods were also used to determine the best model. As a result, the Gradient Boosting model was the outperform and optimal predictive model (PR-AUC metric = 0.957).

In many economic studies, the dependent variable is discrete, indicating, for example, that a household purchased a car or that an individual is unemployed or that he or she joined a labor union or defaulted on a loan or was denied credit.

With a controller servlet, all the power and convenience of Java can be used in the web development process. Development is simplified with the addition of auxiliary classes. A powerful class that can be added to a web application is one that contains all the data that was entered by the user. The request object contains the data from the user, but the request object also contains a lot of other information that is not related to the user’s data. A better design is to create a new class that only contains the data. Such a class is known as a bean. With the introduction of a bean, it is a simple matter to add validation to the web application. One type of validation is default validation. In default validation, the user’s data must meet criteria. If the data does not meet the criteria, then a default value is used in place of the data that the user entered. While servlets are very powerful tools for implementing dynamic content on the web, they do have a limitation: member variables. Member variables are useful when designing object-oriented programs but are dangerous to use in a servlet.

The transformation into Industry 4.0 comes with many challenges for organisations all over the world. During the past years, many publications have focused on highlighting the challenges of restructuring, technological defaults, and lack of infrastructure to implement Industry 4.0 transformational challenges. This chapter does not focus on the challenges of Industry 4.0 in terms of hardware or technology, but rather on the emotional aspects of the transformational process.

Financial inclusion is a social need that is gaining more and more strength in developing countries. Microcredit is an effective way to enable financial inclusion, but it represents a challenge in portfolio management. This work applies data analytics and machine learning techniques to predict the behavior of the loan default in a non-financial entity. Decision trees have shown the best prediction performance to determine whether a loan will be paid or become irrecoverable after running five predictive models.

Careful attention to choice architecture promises to open up new possibilities for reducing greenhouse gas emissions—possibilities that go well beyond, and that may supplement or complement, the standard tools of economic incentives, mandates, and bans. How, for example, do consumers choose between climate-friendly products or services and alternatives that are potentially damaging to the climate but less expensive? The answer may well depend on the default rule. Indeed, climate-friendly default rules may well be a more effective tool for altering outcomes than large economic incentives. The underlying reasons include the power of suggestion; inertia and procrastination; and loss aversion. If well-chosen, climate-friendly defaults are likely to have large effects in reducing the economic and environmental harms associated with various products and activities. In deciding whether to establish climate-friendly defaults, choice architects (subject to legal constraints) should consider both consumer welfare and a wide range of other costs and benefits. Sometimes that assessment will argue strongly in favor of climate-friendly defaults, particularly when both economic and environmental considerations point in their direction. Notably, surveys in 17 countries worldwide show that majorities in many nations are in favor of climate-friendly defaults.

This paper proposes an approach to decompose the RR/LGD model development process with two stages, specifically, for the RR/LGD rating model, and to calibrate the model using a linear form that minimizes residual risk. The residual risk in the recovery of defaulted debts is determined by the high uncertainty of the recovery level according to its average expected level. Such residual risk should be considered in the capital requirements for unexpected losses in the loan portfolio. This paper considers a simple residual risk model defined by one parameter. By developing an optimal RR/LGD model, it is proposed to use a residual risk metric. This metric gives the final formula for calibrating the LGD model, which is proposed for the linear model. Residual risk parameters are calculated for RR/LGD models for several open data sources for developed and developing markets. An implied method for updating the RR/LGD model is constructed with a correction for incomplete recovery through the recovery curve, which is built on the training sets. Based on the recovery curve, a recovery indicator is proposed which is useful for monitoring and collecting payments. The given recommendations are important for validating the parameters of RR/LGD model.

This article reports on a scheme to exploit human computation and data science for fostering financial markets’ efficiency. The scheme consists in incentivizing market’s agents to assess their own risk of defaulting to financial contracts (that is, human computation) and in an eigenvector model of credit risk (that is, data science) aggregating agents’ assessments of their defaulting risk estimates into a global, or systemic, credit risk rating. The systemic credit risk rating in turn informs the market’s agents what nudges them to decisions beneficial not only to themselves, but also to the market as a whole.

The global financial infrastructure is a network of players and transactions. And a large set of transactions runs through a distinct number of financial players. A material part of these transactions is cleared through a central clearing party (CCP). Triggering uniformity of transactions, those CCPs also take out counterparty risk for its members. But a very material part of market transactions aren’t cleared and processed in the private market (over the counter [OTC]). The financial crisis documented that the counterparty risk can then be substantial, particularly in the derivatives markets. And so more transactions needed to be cleared was the regulator’s view. But that is not without complications. Besides the fact that the number of CCPs is limited and are now processing more transactions making them potentially systemically relevant, there are many open and problematic questions. How should they be regulated and supervised? What kind of balance sheet should they hold and should they be regulated like banks although they are not? What if one or more of their clients default? What does it mean for the CCP and its remaining clients? And which capital and other buffers should be built in? And what if the CCP itself defaults? How will members be protected? The choices made to answer these questions will be critical for the functioning of the industry and the stability of the entire financial system. But the jury is still out on many of them.

Default Logic refers to a family of formalisms designed to carry out non-monotonic reasoning over a monotonic logic (in general, Classical First-Order or Propositional Logic). Traditionally, default logics have been defined and dealt with via syntactic consequence relations. Here, we introduce a family of default logics defined over modal logics. First, we present these default logics syntactically. Then, we elaborate on an algebraic counterpart. We do the latter by extending the notion of a modal algebra to acommodate for the main elements of default logics: defaults and extensions. Our algebraic treatment of default logics concludes with an algebraic completeness result. To our knowledge, our approach is novel, and it lays the groundwork for studying default logics from a dynamic logic perspective.

Predicting loan default in peer-to-peer (P2P) lending has been a widely researched topic in recent years. While one can identify a large number of contributions predicting loan default on primary market of P2P platforms, there is a lack of research regarding the assessment of analytical methods on secondary market transactions. Reselling investments offers a valuable alternative to investors in P2P market to increase their profit and to diversify. In this article, we apply machine learning algorithms to build classification models that can predict the success of secondary market offers. Using data from a leading European P2P platform, we found that random forests offer the best classification performance. The empirical analysis revealed that in particular two variables have significant impact on success in the secondary market: (i) discount rate and (ii) the number of days the loan had been in debt when it was put on the secondary market.

The interest is calculated such that all the non-defaulting clients are also paying for the defaulting ones – this is a kind of insurance-like approach. Statistically in each year a certain fraction of clients will default. If the calibration of the probabilities of default (PD) forecast by the rating systems is lower than the realized default rates the bank has to do additional write-offs. If there are securities like mortgages these write-offs are reduced as the bank gets back some of the money.

OpenBazaar, a decentralized electronic commerce marketplace, has received significant attention since its development was first announced in early 2014. Using multiple daily crawls of the OpenBazaar network over approximately 14 months (June 25, 2018–September 3, 2019), we measure its evolution over time. We observed 6,651 unique participants overall, including 980 who used Tor at one point or another. More than half of all users (3,521) were only observed on a single day or less, and, on average, only approximately 80 users are simultaneously active on a given day. As a result, economic activity is, unsurprisingly, much smaller than on centralized anonymous marketplaces. Furthermore, while a majority of the 24,379 distinct items listed seem to be legal offerings, a majority of the measurable economic activity appears to be related to illicit products. We also discover that vendors are not always using prudent security practices, which makes a strong case for imposing secure defaults. We conclude that OpenBazaar, so far, has not gained much traction to usher in the new era of decentralized, private, and legitimate electronic commerce it was promising. This could be due to a lack of user demand for decentralized marketplaces, lack of integration of private features, or other factors, such as a higher learning curve for users compared to centralized alternatives.

A fifth program design criteria—information security—is described from a programming perspective. Information security concepts introduced in this chapter include data input validation, data output validation, exception handling, fail-safe defaults, and type-safe languages.

In this chapter we delve into the analysis of loan portfolios. As one of the main raw materials of securitization, large portfolios of loans require specific formal and practical tools for their analysis and we go through the main ones in this part of the book. The first section categorizes the various types of loans that one might encounter, and explains why at the core all these sorts of loans can be analyzed in a similar way. We nevertheless contrast corporate and consumer loans and how their differences lead to a differential treatment in securitization. Then we look into a loan’s life cycle and how that can be represented as a transition matrix. Next, we discuss loan performance metrics and a few conventions. We then address loan behavior and discuss the drivers of prepayments and defaults, without going into the details of advanced modeling. We apply the general framework of loan behavior analysis to loan-level data on agency mortgages. We study refinancings and other forms of prepayment patterns, delinquencies and loss severities, and propose basic empirical models that capture their essential drivers. Next, we lay out practical methods for modeling loan cash flows, which are essential as an input into structures. Finally, we take a look at some formal ways of representing loan performance.

In 1876, Egypt defaulted on its foreign debt, opening the way to European powers’ involvement in the country’s finances, which ultimately culminated in the British occupation of 1882. While the case of Egypt has been analyzed as a classic case of international financial control, this chapter revisits this trajectory by focusing on the role of experts and expertise. It shows how European experts and creditors sought to develop specific statistical and accounting instruments to manage public debt. Yet, rather than analyzing this process as a transfer of expert knowledge from Europe to the “periphery,” the chapter shows how technical debates intersected with political struggles, involving multiple actors. Furthermore, this chapter pays attention to transnational flows of expertise that shaped Egypt’s debt liquidation and it analyzes, in turn, how the Egyptian experiment informed later cases of international financial control (IFC).

During the nineteenth century, sovereign debt defaults often led to active intervention by governments from creditor countries to defend the claims of their bondholders. This chapter investigates why foreign control episodes were mostly absent in Latin America despite recurrent debt crises. Focusing on two case studies from the mid-nineteenth century, Mexico and Peru, it analyzes how public and private control was pursued to secure debt repayment and the development of international trade. It uncovers how the existence of divergent interest groups within those countries, but also the disagreements between creditors and governments in Britain and France, better explain historical outcomes than the notion of “supersanctions.”

In this paper we present some decision making tools for reducing poverty, by microfinance industry, in order to insure a sustainable development in many counties where the poverty is a real challenge. The services offered by the Microfinance Institutions MFIs, especially microcredit, are for the population that has no real substantive safeguards that allow them access to traditional financial services. This lack of safeguards which illustrates a shape of the asymmetric information it generates higher risk products. The MFI offers two types of contracts, individual lending and group lending, characterized by some parameters. The most important are the interest rate and joint liability in group lending. The characteristic of the latter is that each borrower declares reimburse shares of its defaulting partners and the decision to repay depends on the whole group. As for the individual loan, the decision is up to the single borrower. In this paper we present the techniques of modeling and simulation of some optimization problems in microfinance. We will give the fundamental aspects for the design of a financial contract, to minimize the risk of non-repayment linked to the random behavior of micro-borrowers.

The history of modern Greece can be described as an economic history of defaults episodes closely related to its external indebtedness. This chapter presents the four defaults that make Greece a dependent state throughout its recent history: (1) The default of 1826, in the midst of the Greek Independence War. (2) The defaults of the periods 1837–1838 and 1843–1844. (3) The default of 1893, which coincided with the current crisis which plagued the poorly diversified Greek economy. (4) The default of 1932 just a few months after the wake of the 1931 banking crisis. On these events the (unacknowledged) default of 2010–2012, again in the aftermath of the US 2007–2008 subprime crisis could be added.

Based on the current corresponding indicators for determining overcapacity electricity consumption, the screening rules for suspected overcapacity electricity customers are scientifically formulated with data analysis methods in this paper. Overcapacity Electricity Consumption refers to the electricity customer’s violation of the power supply contract, which exceeds the capacity requested. This is a typical behavior of defaulting electricity and disrupting the normal order of electricity supply and consumption. At present, the inspection system lacks an intuitive visual interface for displaying results analysis and the support from a more scientific and flexible algorithm system. Through the expert’s empirical analysis, we have summarized the characteristic indicators of overcapacity customers and formulated the rules for identifying suspected overcapacity electricity customers.