arXiv research

Extracts credit-relevant information from earnings calls.

Paper proposes a method to evaluate SME credit risk using meta paths.

Unified framework connects credit risk metrics with information theory.

NetDP predicts loan defaults using network data, addressing cold-start issues.

The role of credit rating agencies has been under severe scrutiny after the subprime crisis. In this paper we explore the relationship between credit ratings and informational efficiency of a sample of thirty nine corporate bonds of US oil and energy companies from April 2008 to November 2012. For that purpose, we use …

We study the pricing of credit derivatives with asymmetric information. The managers have complete information on the value process of the firm and on the default threshold, while the investors on the market have only partial observations, especially about the default threshold. Different information structures are dis…

Private credit markets have expanded significantly, offering unique lending technology to private equity firms.

The AAA credit rating may have been overly precise given available data.

AI improves MSME credit scoring using bank statement data.

Small Medium-sized Enterprises (SMEs) face many obstacles when they try to access credit market. These obstacles are increased if the SMEs are innovative. In this case, financial data are insufficient or even not reliable. Thus, when building a judgemental rating model, mainly based on qualitative criteria (soft inform…

Synthetic data improves credit scoring models' performance without compromising borrower privacy.

One of the key elements in the banking industry rely on the appropriate selection of customers. In order to manage credit risk, banks dedicate special efforts in order to classify customers according to their risk. The usual decision making process consists in gathering personal and financial information about the borr…

Paper uses RL for better credit scoring and underwriting.

Simplified matrix generator resolves credit migration model calibration issues.

We consider the problem of efficient credit assignment in reinforcement learning. In order to efficiently and meaningfully utilize new data, we propose to explicitly assign credit to past decisions based on the likelihood of them having led to the observed outcome. This approach uses new information in hindsight, rathe…

In this paper incomplete-information models are developed for the pricing of securities in a stochastic interest rate setting. In particular we consider credit-risky assets that may include random recovery upon default. The market filtration is generated by a collection of information processes associated with economic…

Big data from phone calls improves credit scoring models and profits.

Derives metrics for DeFi vaults, addressing credit risk.

EWS-GCN improves credit scoring by analyzing money transfer connections.

The paper uses daily bond price data to estimate corporate default spreads, improving credit risk assessment.

This paper proposes a definition of system health in the context of multiple agents optimizing a joint reward function. We use this definition as a credit assignment term in a policy gradient algorithm to distinguish the contributions of individual agents to the global reward. The health-informed credit assignment is t…

We investigate the impact of available information on the estimation of the default probability within a generalized structural model for credit risk. The traditional structural model where default is triggered when the value of the firm's asset falls below a constant threshold is extended by relaxing the assumption of…

In this paper we use a hybrid Monte Carlo-Optimal quantization method to approximate the conditional survival probabilities of a firm, given a structural model for its credit defaul, under partial information. We consider the case when the firm's value is a non-observable stochastic process $(V_t)_{t \geq 0}$ and inver…

We set up a structural model to study credit risk for a portfolio containing several or many credit contracts. The model is based on a jump--diffusion process for the risk factors, i.e. for the company assets. We also include correlations between the companies. We discuss that models of this type have much in common wi…

This paper provides an alternative approach to Duffie and Lando [Econometrica 69 (2001) 633-664] for obtaining a reduced form credit risk model from a structural model. Duffie and Lando obtain a reduced form model by constructing an economy where the market sees the manager's information set plus noise. The noise makes…

The study models credit risk using Merton's framework and binomial trees.

Study integrates climate and text data to improve credit default prediction.

Traditional methods outperform LLMs in forecasting corporate credit ratings.

The study examines how social biases are reinforced in machine learning models used for credit scoring.

Study predicts firm defaults using machine learning on Italian credit data.

Paper uses AI to predict tail risks in US financial markets.

The paper analyzes XVA reduction strategies in financial crises using Mandatory Breaks, Restructuring, and Resets.

Unified framework models credit cycles and systemic risk.

Paper proposes a framework for precise daily default risk prediction of Chinese credit bonds.

Many households in developing countries lack formal financial histories, making it difficult for firms to extend credit, and for potential borrowers to receive it. However, many of these households have mobile phones, which generate rich data about behavior. This article shows that behavioral signatures in mobile phone…

The paper develops a filtering framework for estimating hazard rates with jumps in financial and insurance applications.

The paper explores how planning with models improves credit assignment in reinforcement learning.

This research investigated the potential for improving Peer-to-Peer (P2P) credit scoring by using "private information" about communications and travels of borrowers. We found that P2P borrowers' ego networks exhibit scale-free behavior driven by underlying preferential attachment mechanisms that connect borrowers in a…

This paper uses graph neural networks to predict SME default risk using transaction and ownership networks.

Broker uses multi-task dynamic pricing to learn competitive prices in credit markets.

A new method detects and removes false trailing balances in credit data.

Paper uses BERT to assess P2P borrowers' credit risk from loan descriptions.

Workflow improves credit default prediction using machine learning.

This work is attached to the BRICS 2013 competition. We propose a two-stage model for dealing with the temporal degradation of credit scoring models. This methodology produced motivating results in a 1-year horizon. We anticipate that it can be extended to other applications of risk assessment with great success. Futur…

LICA learns credit assignment for cooperative agents without explicit formulation.

Credibility theory provides tools to obtain better estimates by combining individual data with sample information. We apply the Credibility theory to a Uniform distribution that is used in testing the reliability of forecasting an interest rate for long term horizons. Such empirical exercise is asked by Regulators (CRR…

Graph machine learning and Super-App data improve credit risk prediction for financial inclusion.

Credit Value Adjustment (CVA) is the difference between the value of the default-free and credit-risky derivative portfolio, which can be regarded as the cost of the credit hedge. Default probabilities are therefore needed, as input parameters to the valuation. When liquid CDS are available, then implied probabilities …