Liquidity in Credit Networks with Constrained Agents

In order to scale transaction rates for deployment across the global web, many cryptocurrencies have deployed so-called "Layer-2" networks of private payment channels. An idealized payment network behaves like a Credit Network, a model for transactions across a network of bilateral trust relationships. Credit Networks capture many aspects of traditional currencies as well as new virtual currencies and payment mechanisms. In the traditional credit network model, if an agent defaults, every other node that trusted it is vulnerable to loss. In a cryptocurrency context, trust is manufactured by capital deposits, and thus there arises a natural tradeoff between network liquidity (i.e. the fraction of transactions that succeed) and the cost of capital deposits. In this paper, we introduce constraints that bound the total amount of loss that the rest of the network can suffer if an agent (or a set of agents) were to default - equivalently, how the network changes if agents can support limited solvency guarantees. We show that these constraints preserve the analytical structure of a credit network. Furthermore, we show that aggregate borrowing constraints greatly simplify the network structure and in the payment network context achieve the optimal tradeoff between liquidity and amount of escrowed capital.Comment: To be published in TheWebConf 202

Economics of payment cards: a status report

This article surveys the recent theoretical literature on payment cards (focusing on debit and credit cards) and studies this research's possible implications for the current public policy debate over payment card networks and the pricing of their services for both consumers and merchants.Payment systems ; Credit cards

Improving RFC5865 Core Network Scheduling with a Burst Limiting Shaper

We define a novel core network router scheduling architecture to carry and isolate time constrained and elastic traffic flows from best-effort traffic. To date, one possible solution has been to implement a core DiffServ network with standard fair queuing and scheduling mechanisms as proposed in the well-known “A Differentiated Services Code Point (DSCP) for Capacity-Admitted Traffic” from RFC5865. This architecture is one of the most selected solutions by internet service provider for access networks (e.g. Customer-Premises Equipment or satellite PEP). In this study, we argue that the proposed standard implementation does not allow to efficiently quantify the reserved capacity for the AF class. By using a novel credit based shaper mechanism called Burst Limiting Shaper, we show that we can provide the same isolation for the time constrained EF class while better quantifying the part allocated to the AF class

What determines credit participation and credit constraints of the poor in peri-urban areas, Vietnam?

This paper uses a novel dataset collected by the first author from peri-urban areas of Ho Chi Minh City, Vietnam in 2008 to examine how the poor use their loans, and factors affecting their credit participation and credit constraints. The paper finds the presence of many commercial banks in the areas does not help the poor, but the poor rely heavily on informal credit. Loans in the peri-urban areas are mainly used for non-productive purposes, which stresses the importance of consumption smoothing motives. Further, households in more rural wards have a higher probability of borrowing than more urban households, thanks to better community relationships and higher interpersonal trust. Competition by borrowing neighbours adversely affects the opportunity for borrowing in urban wards where the poor households’ borrowings rely much more on subsidized credit funds. A closer look at specified microcredit sources reveals that household behaviours differ in each market segment. Furthermore, the poor are highly credit-constrained. Wealthier households, in terms of asset holdings and phone possession, among the poor group appear less credit-constrained. However, except in the most rural part of the study area, the likelihood of credit constraints increases with distance to the nearest banks, which suggests that supply-side intervention could help in overcoming credit constraints. Overall, the poor in urban wards are more credit-constrained because of exclusion by commercial banks and weak interpersonal trust

Towards a Comprehensible and Accurate Credit Management Model: Application of four Computational Intelligence Methodologies

The paper presents methods for classification of applicants into different categories of credit risk using four different computational intelligence techniques. The selected methodologies involved in the rule-based categorization task are (1) feedforward neural networks trained with second order methods (2) inductive machine learning, (3) hierarchical decision trees produced by grammar-guided genetic programming and (4) fuzzy rule based systems produced by grammar-guided genetic programming. The data used are both numerical and linguistic in nature and they represent a real-world problem, that of deciding whether a loan should be granted or not, in respect to financial details of customers applying for that loan, to a specific private EU bank. We examine the proposed classification models with a sample of enterprises that applied for a loan, each of which is described by financial decision variables (ratios), and classified to one of the four predetermined classes. Attention is given to the comprehensibility and the ease of use for the acquired decision models. Results show that the application of the proposed methods can make the classification task easier and - in some cases - may minimize significantly the amount of required credit data. We consider that these methodologies may also give the chance for the extraction of a comprehensible credit management model or even the incorporation of a related decision support system in bankin

Household-level Credit Constraints in Urban Ethiopia

Empirical evidence on determinants of credit constraints and the amount borrowed by urban household in Sub-Saharan Africa is almost non-existent. Using an extended direct approach by virtue of the unique data set we have (the Fourth Round Ethiopian Urban Household Survey), we analysed the determinants of credit constraints and the amount borrowed by urban households. We find a high percentage of credit-constrained households, the majority of which constitute discouraged borrowers. Discrete choice models that control for potential endogeneity and selectivity bias have been fitted to our data. Our analysis shows current household resources, number of dependants, and location as significant correlates.credit constrained households; credit rationing; endogeneity; instrumental variables; urban Ethiopia; Africa