Dynamic Credit Research Articles

Optimizing the credit term decisions in supply chain finance

We study a credit term determination problem in the context of a supplier-buyer supply chain. The supplier's credit term decision is simultaneously made with its production and inventory decisions, and most importantly, it is impacted by the buyer's order quantity. We present a new game-theoretic framework to model this problem, which captures the interaction between the supplier's credit term decision and the buyer's order decision in a multi-period setting. An exact method based on nonlinear programming is implemented to obtain the optimal solutions. We apply our methodologies on a real world case. The computational results show that our approach significantly outperforms the heuristics with fixed credit terms, and either a short or a long credit term can be sub-optimal for the supplier in profitability. Our work offers the first data-driven model and solution approach that assists purchasing and supply managers to make optimal dynamic credit term decision in conjunction with production, ordering and inventory decisions in a game-theoretic setting.

Managing congestion and emissions in transportation networks with dynamic carbon credit charge scheme

Traffic congestion contributes to the air pollution problem due to the increased idling, braking and acceleration behaviors. This research puts forward an optimal dynamic credit charge scheme that can redistribute the traffic flows to attain mobility and emission goals. First, cell transmission model is used as dynamic network loading model to capture the flow propagation and dynamic user equilibrium (DUE) is formulated to investigate the flow redistribution in terms of simultaneous path and departure time choices under carbon credit charge scheme. Based on that, a bi-level formulation is proposed to describe the Stackelberg game between road manager and road users. For the higher level, road manager seeks to minimize system travel time and emissions by implementing credit charge schemes. For the lower level, heterogeneous users find dynamic user equilibrium by simultaneous path and departure time choices under given credit charge schemes. Given the non-convex property of the bi-level model, this paper proposes the pattern search algorithm embedded with projection method. Computational results on X-shape, Ziliaskopolous’ and Nyguen-Dupuis network are given to show the applicability of the proposed algorithm on reasonable-size networks. Results show that minimum travel time design does not always generate effective path and departure time switches across all user groups, especially for users with high value of travel time, while minimum emissions credit design yields desirable behavioral adjustments. Besides, minimum travel time credit design does not always generate minimum carbon emissions in the network, especially in networks with complex O–D pairs and paths. This research casts light on how to schedule a time-varying credit charge scheme to attain mobility and emission goals. Policy implications of credit charge scheme are provided.

Implementation of dynamic credit rating method based on clustering and classification technology

In order to adapt to the actual situation of the complex network environment, and make the credit rating results be more dynamic and authentic. We divide the sellers’ transaction history into several time frames, and define four concepts including recent credit, long-term credit, cumulative negative credit and evaluator credibility. The model is more suitable for the reliability and authenticity of the credit compared with the traditional credit rating model. Through studying the existing credit rating platform, this paper analyzes the existing problems and the advantages of the system itself, and establishes a new credit model by taking many kinds of factors into consideration, which include various objective factors, such as the amount and completion time of the transaction, the buyer’s credit degree, etc. In the end, the implementation of the credit rating model is given based on the above factors.

Aggregate consequences of dynamic credit relationships

I investigate the aggregate consequences of canonical financial frictions in the supply of credit to firms: private information and limited enforcement. I propose a general equilibrium model in which entrepreneurs finance their firm through a long-term contract with a financial intermediary. The contract is inefficient because firm cash flow is costly to monitor, and borrowers that repudiate cannot be excluded from capital markets. By investing in enforcement capacity, an intermediary can delay debt repayment and maintain incentive compatibility. Reforms that seek to decrease either the cost of monitoring or enforcing contracts, or both, affect firm dynamics and can generate complementaries. Estimating the model with data on Colombian manufacturing firms in the 1980s and 1990s, I find that financial frictions are responsible for a significant aggregate output loss. Most of this distortion can be attributed to private information. Reforms that only reduce private information create significant economic growth and welfare gains, while those that only improve enforcement do not. There are significant complementarities between different types of reforms, as moral hazard is less significant when contracts are more enforceable.

Aggregate Consequences of Dynamic Credit Relationships

Abstract I investigate the aggregate consequences of canonical financial frictions in the supply of credit to firms: private information and limited enforcement. I propose a general equilibrium model in which entrepreneurs finance their firm through a long-term contract with a financial intermediary. The contract is inefficient because firm cash flow is costly to monitor, and borrowers that repudiate cannot be excluded from capital markets. By investing in enforcement capacity, an intermediary can delay debt repayment and maintain incentive compatibility. Reforms that seek to decrease either the cost of monitoring or enforcing contracts, or both, affect firm dynamics and can generate complementaries. Estimating the model with data on Colombian manufacturing firms in the 1980s and 1990s, I find that financial frictions are responsible for a significant aggregate output loss. Most of this distortion can be attributed to private information. Reforms that only reduce private information create significant economic growth and welfare gains, while those that only improve enforcement do not. There are significant complementarities between different types of reforms, as moral hazard is less significant when contracts are more enforceable.

Research on Credit Evaluation of Mobile Medical APP Interactive Online Consultation Service-Take Haodaifu APP Online Payment Service as an Example

Mobile medical industry is developing rapidly, but the relevant credit evaluation system is still in the exploratory stage. Haodaifu in the interactive online consulting services more representatives and has a credit evaluation system, but the system is not perfect. There are many problems in the system, such as credits indicators cannot better reflect the results of user’s evaluation, the lack of user qualification supervision, evaluation index system and assessment channels are not perfect. On the basis of drawing on the advantages of E-commerce evaluation system and combining with the characteristics of mobile medical itself, two suggestions are put forward: Improve the credit evaluation model (Generate physician static credit index, Change the doctor 's dynamic credit index algorithm, Update the dynamic credit index), Improve the basic mechanism of credit evaluation.

Database Implementation and Testing of Dynamic Credit Card Fraud Detection System

Database Implementation and Testing of Dynamic Credit Card Fraud Detection System

Dynamic credit allocation in scientific literature

Collaboration among researchers is an essential component of the scientific process, playing a particularly important role in findings with significant impact. While extensive efforts have been dev...

Design dynamic credit risk model with fuzzy rules for auto dealers

Credit risk scoring is one of the risk assessment tools of auto dealers, but it is complicated, which is affected by various variables and factors in different behaviour mechanisms. This paper incorporates the behaviour style of auto dealers in their ability to pay debt in a dynamic system with fuzzy rules for uncertainty. We discuss credit risk assessment of auto dealers by establishing a relevant credit risk evaluation system and present a dynamic fuzzy credit scoring model to forecast the credit risk of auto dealers. Our work focuses on two aspects: 1) we are investigating the credit risk of auto dealers in a dynamic system; 2) fuzzy rules are also integrated into a dynamic system as fuzzy inference system. Simulation results of a dynamic fuzzy credit risk model are presented in five different classes. Numerical results of simulation are analysed, a dynamic trend of management variety, sell rate and credit risk score are also provided.

Design dynamic credit risk model with fuzzy rules for auto dealers

Design dynamic credit risk model with fuzzy rules for auto dealers

Multi agent system-based dynamic trust calculation model and credit management mechanism of online trading

AbstractNow all kinds of malicious acts appear in C2C online auctions, particularly the phenomenon of trust lack and credit fraud is very outstanding. Therefore, how to build an effective trust model has become a burning problem. Based on analyzing limitations of the existing online trust transaction mechanism, and according to characteristics (such as dynamic, innominate and suppositional) of online transaction trust problem, the article proposes a dynamic trust calculation model and reputation management mechanism of online trading based on multi-Agent system. The model consists of three parts. The first part is the trust of user domain, to put importance on the influence on current trust by recent credibility status, to motivate users to adopt an agreed cooperative strategy. The second part is the weighted average of reputation feedback score. The weighted part mainly considers the trust from the reputation feedback score person (the credibility of the feedback score), the value of the transaction (to ...

Dynamic credit score modeling with short-term and long-term memories: the case of Freddie Mac’s database

Dynamic credit score modeling with short-term and long-term memories: the case of Freddie Mac’s database

Robust Optimization of Credit Portfolios

We introduce a dynamic credit portfolio framework where optimal investment strategies are robust against misspecifications of the reference credit model. The risk-averse investor models his fear of credit risk misspecification by considering a set of plausible alternatives whose expected log likelihood ratios are penalized. We provide an explicit characterization of the optimal robust bond investment strategy, in terms of default state dependent value functions associated with the max-min robust optimization criterion. The value functions can be obtained as the solutions of a recursive system of Hamilton-Jacobi-Bellman (HJB) equations. We show that each HJB equation is equivalent to a suitably truncated equation admitting a unique bounded regular solution. The truncation technique relies on estimates for the solution of the master HJB equation that we establish.

Aggregate Consequences of Dynamic Credit Relationships

Aggregate Consequences of Dynamic Credit Relationships

Dynamic Credit Default Swaps Curves in a Network Topology

Systemically important banks are connected and have dynamic dependencies of their default probabilities. An extraction of default factors from cross-sectional credit default swaps (CDS) curves allows to analyze the shape and the dynamics of the default probabilities. Extending the Dynamic Nelson Siegel (DNS) model, we propose a network DNS model to analyze the interconnectedness of default factors in a dynamic fashion, and forecast the CDS curves. The extracted level factors representing long-term default risk demonstrate 85.5% total connectedness, while the slope and the curvature factors document 79.72% and 62.94% total connectedness for the short-term and middle-term default risk, respectively. The issues of default spillover and systemic risk should be weighted for the market participants with longer credit exposures, and for regulators with a mission to stabilize financial markets. The US banks contribute more to the long-run default spillover before 2012, whereas the European banks are major default transmitters during and after the European debt crisis either in the long-run or short-run. The outperformance of the network DNS model indicates that the prediction on CDS curve requires network information.

Detection and Elimination of Gray Hole Attack using Dynamic Credit based Technique in MANET

ad hoc network (MANET) is constructed from various number of nodes, that can be move anywhere and at any time, without any infrastructure. MANETs use wireless connections to connect various networks, without any fixed infrastructure or any centralized administration. Due to this nature of MANET, Ad hoc networks are open to different types of security attacks. The gray hole attack is the attack performed by the node called malicious node, which forwards and drops the selective packets only. Here, in this paper, we have proposed an algorithm which detects and eliminates the gray hole attack using Dynamic Credit Based Technique using AODV routing protocol. The gray hole node is detected based on credit value, which increases or decreases. The simulation results are compared with different situation and attempt to improve the performance of AODV protocol for the parameters like Packet Delivery Fraction, Throughput and End-to-End delay.

Dynamic credit investment in partially observed markets

We consider the problem of maximizing the expected utility for a power investor who can allocate his wealth in a stock, a defaultable security, and a money market account. The dynamics of these security prices are governed by geometric Brownian motions modulated by a hidden continuous-time finite-state Markov chain. We reduce the partially observed stochastic control problem to a complete observation risk-sensitive control problem via the filtered regime switching probabilities. We separate the latter into predefault and postdefault dynamic optimization subproblems and obtain two coupled Hamilton–Jacobi–Bellman (HJB) partial differential equations. We prove the existence and uniqueness of a globally bounded classical solution to each HJB equation and give the corresponding verification theorem. We provide a numerical analysis showing that the investor increases his holdings in stock as the filter probability of being in high-growth regimes increases, and decreases his credit risk exposure as the filter probability of being in high default risk regimes gets larger.

Aggregate Consequences of Dynamic Credit Relationships

Which financial frictions matter in the aggregate? This paper presents a general equilibrium model in which entrepreneurs finance a firm with a long-term contract. The contract is constrained efficient because firm revenue is costly to monitor and entrepreneurs may default. The cost of monitoring firms and the entrepreneurs' outside options determine the significance of moral hazard relative to limited enforcement for financial contracting. Calibrating the model to the U.S. economy, I find that the relative welfare loss from financial frictions is about 5 percent in terms of aggregate consumption with moral hazard, while it is 1 percent with limited enforcement. Reforms designed to strengthen contract enforcement increase aggregate consumption in the short-run, but their long-run effects are modest when monitoring costs are high. Weak contract enforcement contributes to aggregate fluctuations by amplifying the effect of aggregate technological shocks, but moral hazard does not.

Dynamic credit scoring using B & B with incremental-SVM-ensemble

Purpose – Previous researches on credit scoring mainly focussed on static modeling on panel sample data set in a certain period of time, and did not pay enough attention on dynamic incremental modeling. The purpose of this paper is to address the integration of branch and bound algorithm with incremental support vector machine (SVM) ensemble to make dynamic modeling of credit scoring. Design/methodology/approach – This new model hybridizes support vectors of old data with incremental financial data of corporate in the process of dynamic ensemble modeling based on bagged SVM. In the incremental stage, multiple base SVM models are dynamically adjusted according to bagged new updated information for credit scoring. These updated base models are further combined to generate a dynamic credit scoring. In the empirical experiment, the new method was compared with the traditional model of non-incremental SVM ensemble for credit scoring. Findings – The results show that the new model is able to continuously and dynamically adjust credit scoring according to corporate incremental information, which helps produce better evaluation ability than the traditional model. Originality/value – This research pioneered on dynamic modeling for credit scoring with incremental SVM ensemble. As time pasts, new incremental samples will be combined with support vectors of old samples to construct SVM ensemble credit scoring model. The incremental model will continuously adjust itself to keep good evaluation performance.

Aggregate Consequences of Dynamic Credit Relationships

Aggregate Consequences of Dynamic Credit Relationships