Study shows how macroprudential policies affect credit growth in Israel, especially in housing and business sectors.
problem Impact of macroprudential policies on credit growth in Israel.
method Bank-level panel data analysis for Israel, 2004-2019; interaction of monetary and macroprudential policies.
result Accommodative monetary policy interacts with macroprudential policies to increase total credit growth.
Paper uses CT-IV to estimate causal effects in non-randomized settings.
problem Estimating causal effects in non-randomized observational studies.
method Modified Causal Tree (CT-IV) algorithm combining CART and IV framework.
result Demonstrates efficiency in handling heterogeneity of causal effects.
Credit scores misclassify borrowers, especially minorities, leading to inequitable access.
problem Misclassification of borrowers by credit scores, particularly minorities.
method Benchmarked a widely used credit score against a machine learning model.
result Machine learning model improves predictive accuracy for low-quality data, leading to more equitable access.
New measure predicts Dutch housing market downturns.
problem Understanding causes of Dutch housing boom and bust.
method Modelled household lending capacity using bank formulas.
result New measure outperforms traditional measures in forecasting housing prices.
ProMP improves Meta-RL by better handling credit assignment.
problem Poor credit assignment in Meta-RL leads to suboptimal performance.
method Develops a novel meta-learning algorithm controlling pre-adaptation and adapted policies.
result Significantly improves sample-efficiency and asymptotic performance.
Complex network theory models China's credit system to control systemic risk.
problem Insufficient understanding of China's credit network structure during financial crises.
method Constructed bipartite financial institution-firm network and analyzed its typological properties.
result Credit network structure can amplify local risks to the whole economy.
The paper introduces a health-informed policy gradient method for multi-agent reinforcement learning.
problem Optimizing joint reward functions in multi-agent systems with varying agent health.
method Health-informed credit assignment in a multi-agent proximal policy optimization algorithm.
result Significant improvement in learning performance compared to traditional methods.
Study finds strict collection policies improve portfolio quality of microfinance banks.
problem Improving portfolio quality of microfinance banks through better credit collection policies.
method Multi-stage sampling, regression analysis, descriptive statistics.
result Collection policy has a higher effect on portfolio quality.
This paper examines the dividend and investment policies of a cash constrained firm that has access to costly external funding. We depart from the literature by allowing the firm to issue collateralized debt to increase its investment in productive assets resulting in a performance sensitive interest rate on debt. We f…
Method debiases alternative data for fair credit underwriting.
problem Bias in alternative data affecting credit underwriting fairness.
method Causal inference applied to machine learning models.
result Improves model accuracy across racial groups without discrimination.
LICA learns credit assignment for cooperative agents without explicit formulation.
problem Credit assignment among cooperative agents.
method Centralized critic as hypernetwork, adaptive entropy regularization.
result Significantly outperforms previous methods on benchmarks.
We present a model of credit card profitability, assuming that the card-holder always pays the full outstanding balance. The motivation for the model is to calculate an optimal credit limit, which requires an expression for the expected outstanding balance. We derive its Laplace transform, assuming that purchases are m…
Study examines factors influencing lending to SMEs by Kenyan banks.
problem Lack of creditworthiness makes SMEs difficult to finance by banks.
method Descriptive research design, census of 43 banks, secondary data analysis.
result Bank size and liquidity significantly influence lending to SMEs, while credit risk and interest rates do not.
Unified view on selective credit assignment for reinforcement learning.
problem Efficient credit assignment in reinforcement learning.
method Unified temporal-difference algorithms with selective weightings.
result New algorithms for backward credit assignment and off-policy learning.
We explain a persistent cost-of-carry spread in EUA market and suggest ECB policy change.
problem Persistent cost-of-carry spread in EUA market.
method Cointegration analysis of EUA spread with credit spread and risk-free rate.
result Cointegration found between EUA spread, credit spread, and risk-free rate.
New method learns credit prices offline without interaction.
problem Dynamic pricing of consumer credit.
method Offline deep reinforcement learning with Q-Learning.
result Effective personalized pricing policy learned without online interaction.
New eco-systemic prudential policies aim to finance green companies, reducing systemic financial risk.
problem Insufficient financing for green companies despite available savings and monetary management.
method Reorient corporate accounting towards socio-environmental solvency, facilitating access with public guarantees.
result Green financing increases, reducing systemic financial risk and promoting less leveraged investments.
This article examines arbitrage investment in a mispriced asset when the mispricing follows the Ornstein-Uhlenbeck process and a credit-constrained investor maximizes a generalization of the Kelly criterion. The optimal differentiable and threshold policies are derived. The optimal differentiable policy is linear with …
Assessment of risk levels for existing credit accounts is important to the implementation of bank policies and offering financial products. This paper uses cluster analysis of behaviour of credit card accounts to help assess credit risk level. Account behaviour is modelled parametrically and we then implement the behav…
The problem of distributed learning and channel access is considered in a cognitive network with multiple secondary users. The availability statistics of the channels are initially unknown to the secondary users and are estimated using sensing decisions. There is no explicit information exchange or prior agreement amon…
Paper models transition risk using jump-diffusion model to price credit swaps.
problem Capturing transition risk in financial markets.
method Calibrated jump-diffusion model to CDS term structure, using quantile regression.
result Jump-diffusion model captures transition risk, jumps represent green policies.
With negative growth in real production in many countries and debt levels which become an increasing burden on developed societies, the calls for a change in economic policy and even the monetary system become louder and increasingly impatient. We research the consequences of a system of credit and debt, that still all…
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…
Optimizes credit index option hedging with reinforcement learning.
problem Finding the best strategy for hedging credit index options.
method Applied reinforcement learning with TRVO algorithm in a realistic setting.
result The derived hedging strategy outperforms traditional methods.
We use a simple agent based model of value investors in financial markets to test three credit regulation policies. The first is the unregulated case, which only imposes limits on maximum leverage. The second is Basle II and the third is a hypothetical alternative in which banks perfectly hedge all of their leverage-in…
Paper presents a novel time series clustering algorithm for financial inclusion.
problem Difficulty in understanding consumer financial behavior without restrictive credit scoring.
method Developed a novel time series clustering algorithm.
result Allows institutions to offer unique financial products based on customer needs.
We explicitly test if the reliability of credit ratings depends on the total number of admissible states. We analyse open access credit rating data and show that the effect of the number of states in the dynamical properties of ratings change with time, thus giving supportive evidence that the ideal number of admissibl…
Improved GFlowNets learn more efficiently with trajectory balance.
problem Inefficient credit assignment in GFlowNets leads to suboptimal learning.
method Proposed trajectory balance as a new learning objective.
result Trajectory balance leads to more efficient and robust GFlowNet learning.
Study agnostic RL in large state spaces with weak function approximation.
problem Statistical intractability of agnostic policy learning in various environments.
method Investigates agnostic policy learning with different forms of environment access.
result Agnostic policy learning remains statistically intractable with certain forms of environment access.
We introduce a simple approach for testing the reliability of homogeneous generators and the Markov property of the stochastic processes underlying empirical time series of credit ratings. We analyze open access data provided by Moody's and show that the validity of these assumptions - existence of a homogeneous genera…
Synthetic data improves credit scoring models' performance without compromising borrower privacy.
problem Scarcity of real data for credit scoring models due to privacy concerns.
method Privacy-preserving training with synthetic data.
result Credit scoring models trained with synthetic data show a reduction of 3% in AUC and 6% in KS compared to real data models.
This paper studies the optimal timing to liquidate credit derivatives in a general intensity-based credit risk model under stochastic interest rate. We incorporate the potential price discrepancy between the market and investors, which is characterized by risk-neutral valuation under different default risk premia speci…
AI improves MSME credit scoring using bank statement data.
problem Lack of access to financing for MSMEs due to traditional credit scoring methods.
method Developed a cash flow-based pipeline using bank statement data for machine learning credit scoring.
result Bank statement features significantly improve credit scoring models, achieving AUROC of 0.806.
GASIL encourages agents to imitate past good trajectories in reinforcement learning.
problem Long-term credit assignment in sparse and delayed reward environments.
method Generative Adversarial Imitation Learning (GASIL) framework.
result GASIL improves performance in reinforcement learning tasks with delayed rewards.
Minimalistic attacks reveal deep RL policies' vulnerabilities with little perturbation.
problem Tackling the vulnerability of deep reinforcement learning policies to minimal perturbations.
method Three key settings: black-box policy access, fractional-state adversary, and tactically-chanced attack. Formulated adversarial attacks on six Atari games.
result Deep RL policies can be significantly fooled by minimal perturbations, even in 0.01% of the input state.
Enhances initial margin to manage warehoused credit risk effectively.
problem Limited access to hedging services for high-risk counterparties.
method Extend CVA risk-neutral framework to compute Specific Initial Margin (SIM) based on counterparty credit quality.
result Transforms effective credit rating to AAA, ensuring operational viability of warehoused risk management.
This paper improves credit line impact analysis by considering spending as a distribution.
problem Previous studies on credit lines' impact on spending have overlooked the distributional nature of spending.
method Developed a distribution-valued estimator framework to extend existing real-valued estimators.
result Credit lines positively influence spending across all quantiles, but more towards luxuries as they increase.
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 (Vt)t≥0 and inver…
The Basel II internal ratings-based (IRB) approach to capital adequacy for credit risk plays an important role in protecting the Australian banking sector against insolvency. We outline the mathematical foundations of regulatory capital for credit risk, and extend the model specification of the IRB approach to a more g…
Study assesses risks of European Safe Bonds using credit risk models.
problem Risks associated with European Safe Bonds and related securities.
method Affine credit risk model with regime switching.
result ESBies are not truly risk-free, impacting market and policy implications.
Interbank lending and borrowing occur when financial institutions seek to settle and refinance their mutual positions over time and circumstances. This interactive process involves money creation at the aggregate level. Coordination mismatch on interbank credit may trigger systemic crises. This happened when, since sum…
Study uses RL to optimize credit card limits, achieving better results than traditional methods.
problem Optimizing credit card limit adjustments in banking.
method Reinforcement learning with offline learning strategy.
result Double Q-learning agent outperforms other strategies in generating optimal policy.
This paper improves credit risk analysis by incorporating state-dependent recovery rates into a factor model.
problem Accurate default forecasting in credit risk analysis.
method Extends a one-factor Gaussian copula model to include state-dependent recovery rates and a common factor.
result The proposed model outperforms other models in default prediction, especially during hectic periods.
The paper compares ML models for credit scoring and investment decisions using explainable AI.
problem The opacity of machine learning models in financial services.
method Comparison of various machine learning models (single classifiers, ensembles, neural networks) and explainability techniques (LIME, SHAP).
result Ensemble classifiers and neural networks outperform in credit scoring models.
Study finds implicit government guarantee improves municipal investment bond ratings.
problem Questioning the objectivity of municipal investment bond ratings due to implicit government guarantee.
method Text mining of policy documents and PMC index model for implicit guarantee strength calculation.
result Implicit government guarantee boosts municipal investment bond ratings, especially in less developed regions.
Proposes a fairness criterion for multi-objective optimization in classification.
problem Ensuring fairness in classification models across different groups.
method Formulates a minimax Pareto fairness criterion and provides an optimization algorithm.
result Demonstrates improved fairness compared to existing methods on various real-world datasets.
We model a network economy with three sectors: downstream firms, upstream firms, and banks. Agents are linked by productive and credit relationships so that the behavior of one agent influences the behavior of the others through network connections. Credit interlinkages among agents are a source of bankruptcy diffusion…
Parrot learns optimal cache replacement policies using imitation learning.
problem Improving cache hit rates in complex access patterns.
method Imitation learning approach using Belady's oracle policy.
result Parrot increases cache hit rates by 61% on a web search benchmark.