Hamiltonian Neural Networks for Robust Out-of-Time Credit Scoring

• URL: http://arxiv.org/abs/2410.10182v1
• Date: Mon, 14 Oct 2024 06:08:26 GMT
• Title: Hamiltonian Neural Networks for Robust Out-of-Time Credit Scoring
• Authors: Javier Marín,
• Abstract summary: We develop a symplectic and a new financial loss function to capture the complex dynamics of credit risk evolution. Our method shows superior discriminative power in OOT scenarios, as measured by the Area Under the Curve (AUC) The Hamiltonian-inspired approach shows particular strength in maintaining consistent performance between in-sample and OOT test sets.
• Score: 0.5439020425819
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: This paper introduces a novel Hamiltonian-inspired neural network approach to credit scoring, designed to address the challenges of class imbalance and out-of-time (OOT) prediction in financial risk management. Drawing from concepts in Hamiltonian mechanics, we develop a symplectic optimizer and a new loss function to capture the complex dynamics of credit risk evolution. Using the Freddie Mac Single-Family Loan-Level Dataset, we evaluate our model's performance against other machine learning approaches. Our method shows superior discriminative power in OOT scenarios, as measured by the Area Under the Curve (AUC), indicating better ranking ability and robustness to class imbalance. The Hamiltonian-inspired approach shows particular strength in maintaining consistent performance between in-sample and OOT test sets, suggesting improved generalization to future, unseen data. These findings suggest that physics-inspired techniques offer a promising direction for developing more robust and reliable credit scoring models, particularly in uncertain economic situations.

Related papers

• KACDP: A Highly Interpretable Credit Default Prediction Model [2.776411854233918]
  This paper introduces a method based on Kolmogorov-Arnold Networks (KANs) KANs is a new type of neural network architecture with learnable activation functions and no linear weights. Experiments show that the KACDP model outperforms mainstream credit default prediction models in performance metrics.
  arXiv  Detail & Related papers  (2024-11-26T12:58:03Z)
• Recurrent Neural Goodness-of-Fit Test for Time Series [8.22915954499148]
  Time series data are crucial across diverse domains such as finance and healthcare. Traditional evaluation metrics fall short due to the temporal dependencies and potential high dimensionality of the features. We propose the REcurrent NeurAL (RENAL) Goodness-of-Fit test, a novel and statistically rigorous framework for evaluating generative time series models.
  arXiv  Detail & Related papers  (2024-10-17T19:32:25Z)
• Applying Hybrid Graph Neural Networks to Strengthen Credit Risk Analysis [4.457653449326353]
  This paper presents a novel approach to credit risk prediction by employing Graph Convolutional Neural Networks (GCNNs) The proposed method addresses the challenges faced by traditional credit risk assessment models, particularly in handling imbalanced datasets. The study demonstrates the potential of GCNNs in improving the accuracy of credit risk prediction, offering a robust solution for financial institutions.
  arXiv  Detail & Related papers  (2024-10-05T20:49:05Z)
• GARCH-Informed Neural Networks for Volatility Prediction in Financial Markets [0.0]
  We present a new, hybrid Deep Learning model that captures and forecasting market volatility more accurately than either class of models are capable of on their own. When compared to other time series models, GINN showed superior out-of-sample prediction performance in terms of the Coefficient of Determination ($R2$), Mean Squared Error (MSE), and Mean Absolute Error (MAE)
  arXiv  Detail & Related papers  (2024-09-30T23:53:54Z)
• Infusing Self-Consistency into Density Functional Theory Hamiltonian Prediction via Deep Equilibrium Models [30.746062388701187]
  We introduce a unified neural network architecture, the Deep Equilibrium Density Functional Theory Hamiltonian (DEQH) model. DEQH model inherently captures the self-consistency nature of Hamiltonian. We propose a versatile framework that combines DEQ with off-the-shelf machine learning models for predicting Hamiltonians.
  arXiv  Detail & Related papers  (2024-06-06T07:05:58Z)
• Attention-based Dynamic Multilayer Graph Neural Networks for Loan Default Prediction [4.0990577062436815]
  We present a model for credit risk assessment leveraging a dynamic multilayer network built from a Graph Neural Network and a Recurrent Neural Network. We test our methodology in a behavioural credit scoring context using a dataset provided by U.S. mortgage financier Freddie Mac.
  arXiv  Detail & Related papers  (2024-02-01T03:20:53Z)
• The Risk of Federated Learning to Skew Fine-Tuning Features and Underperform Out-of-Distribution Robustness [50.52507648690234]
  Federated learning has the risk of skewing fine-tuning features and compromising the robustness of the model. We introduce three robustness indicators and conduct experiments across diverse robust datasets. Our approach markedly enhances the robustness across diverse scenarios, encompassing various parameter-efficient fine-tuning methods.
  arXiv  Detail & Related papers  (2024-01-25T09:18:51Z)
• Robust Graph Neural Networks via Unbiased Aggregation [18.681451049083407]
  adversarial robustness of Graph Neural Networks (GNNs) has been questioned due to the false sense of security uncovered by strong adaptive attacks. We provide a unified robust estimation point of view to understand their robustness and limitations.
  arXiv  Detail & Related papers  (2023-11-25T05:34:36Z)
• A Bayesian Approach to Robust Inverse Reinforcement Learning [54.24816623644148]
  We consider a Bayesian approach to offline model-based inverse reinforcement learning (IRL) The proposed framework differs from existing offline model-based IRL approaches by performing simultaneous estimation of the expert's reward function and subjective model of environment dynamics. Our analysis reveals a novel insight that the estimated policy exhibits robust performance when the expert is believed to have a highly accurate model of the environment.
  arXiv  Detail & Related papers  (2023-09-15T17:37:09Z)
• When Demonstrations Meet Generative World Models: A Maximum Likelihood Framework for Offline Inverse Reinforcement Learning [62.00672284480755]
  This paper aims to recover the structure of rewards and environment dynamics that underlie observed actions in a fixed, finite set of demonstrations from an expert agent. Accurate models of expertise in executing a task has applications in safety-sensitive applications such as clinical decision making and autonomous driving.
  arXiv  Detail & Related papers  (2023-02-15T04:14:20Z)
• Bayesian Bilinear Neural Network for Predicting the Mid-price Dynamics in Limit-Order Book Markets [84.90242084523565]
  Traditional time-series econometric methods often appear incapable of capturing the true complexity of the multi-level interactions driving the price dynamics. By adopting a state-of-the-art second-order optimization algorithm, we train a Bayesian bilinear neural network with temporal attention. By addressing the use of predictive distributions to analyze errors and uncertainties associated with the estimated parameters and model forecasts, we thoroughly compare our Bayesian model with traditional ML alternatives.
  arXiv  Detail & Related papers  (2022-03-07T18:59:54Z)
• Multi-head Temporal Attention-Augmented Bilinear Network for Financial time series prediction [77.57991021445959]
  We propose a neural layer based on the ideas of temporal attention and multi-head attention to extend the capability of the underlying neural network. The effectiveness of our approach is validated using large-scale limit-order book market data.
  arXiv  Detail & Related papers  (2022-01-14T14:02:19Z)
• SyMetric: Measuring the Quality of Learnt Hamiltonian Dynamics Inferred from Vision [73.26414295633846]
  A recently proposed class of models attempts to learn latent dynamics from high-dimensional observations. Existing methods rely on image reconstruction quality, which does not always reflect the quality of the learnt latent dynamics. We develop a set of new measures, including a binary indicator of whether the underlying Hamiltonian dynamics have been faithfully captured.
  arXiv  Detail & Related papers  (2021-11-10T23:26:58Z)
• Probabilistic robust linear quadratic regulators with Gaussian processes [73.0364959221845]
  Probabilistic models such as Gaussian processes (GPs) are powerful tools to learn unknown dynamical systems from data for subsequent use in control design. We present a novel controller synthesis for linearized GP dynamics that yields robust controllers with respect to a probabilistic stability margin.
  arXiv  Detail & Related papers  (2021-05-17T08:36:18Z)
• Federated Learning with Unreliable Clients: Performance Analysis and Mechanism Design [76.29738151117583]
  Federated Learning (FL) has become a promising tool for training effective machine learning models among distributed clients. However, low quality models could be uploaded to the aggregator server by unreliable clients, leading to a degradation or even a collapse of training. We model these unreliable behaviors of clients and propose a defensive mechanism to mitigate such a security risk.
  arXiv  Detail & Related papers  (2021-05-10T08:02:27Z)
• Sequential Deep Learning for Credit Risk Monitoring with Tabular Financial Data [0.901219858596044]
  We present our attempts to create a novel approach to assessing credit risk using deep learning. We propose a new credit card transaction sampling technique to use with deep recurrent and causal convolution-based neural networks. We show that our sequential deep learning approach using a temporal convolutional network outperformed the benchmark non-sequential tree-based model.
  arXiv  Detail & Related papers  (2020-12-30T21:29:48Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.