Semi-structured multi-state delinquency model for mortgage default

• URL: http://arxiv.org/abs/2603.26309v1
• Date: Fri, 27 Mar 2026 11:22:22 GMT
• Title: Semi-structured multi-state delinquency model for mortgage default
• Abstract summary: We propose a semi-structured discrete-time multi-state model to analyse mortgage delinquency transitions.<n>This model combines an easy-to-understand structured additive predictor, with a flexible neural network component that captures complex nonlinearities and higher-order interactions.
• Score: 7.0177704222466994
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We propose a semi-structured discrete-time multi-state model to analyse mortgage delinquency transitions. This model combines an easy-to-understand structured additive predictor, which includes linear effects and smooth functions of time and covariates, with a flexible neural network component that captures complex nonlinearities and higher-order interactions. To ensure identifiability when covariates are present in both components, we orthogonalise the unstructured part relative to the structured design. For discrete-time competing transitions, we derive exact transformations that map binary logistic models to valid competing transition probabilities, avoiding the need for continuous-time approximations. In simulations, our framework effectively recovers structured baseline and covariate effects while using the neural component to detect interaction patterns. We demonstrate the method using the Freddie Mac Single-Family Loan-Level Dataset, employing an out-of-time test design. Compared with a structured generalised additive benchmark, the semi-structured model provides modest but consistent gains in discrimination across the earliest prediction spans, while maintaining similar Brier scores. Adding macroeconomic indicators provides limited incremental benefit in this out-of-time evaluation and does not materially change the estimated borrower-, loan-, or duration-driven effects. Overall, semi-structured multi-state modelling offers a practical compromise between transparent effect estimates and flexible pattern learning, with potential applications beyond credit-transition forecasting.

Related papers

• $\ exttt{findr}$: Transparent and Fair Credit Risk Decisions through Semi-Structured Regressions [5.58905274289646]
  $texttfindr$ is a semi-structured framework for binary credit risk modelling.<n>It decomposes the logit into an interpretable structured component and a neural residual.<n>It measures the structured component's contribution to logit variation, decision agreement, and local directional consistency.
  arXiv  Detail & Related papers  (2026-08-25T14:08:07Z)
• One Step Closer to Ground Truth: A Multi-Scale Residual-Aware Representation Learning Pipeline for Predicting Time Series Data [6.704099492625706]
  We introduce a two-stage, model-agnostic framework that explicitly decouples forecasting and residual learning into distinct stages of representation learning.<n>By formalizing this pipeline as a hypothesis space expansion, our framework addresses approximation limitations inherent in single-stage architectures.
  arXiv  Detail & Related papers  (2026-06-09T10:31:54Z)
• AnchorMoE: Interpretable Time Series Classification via Anchor-Routed MoE [51.994752158953084]
  AnchorMoE is an interpretable-by-construction classification framework.<n>It encodes multi-view representations of local patches and routes them to specialized experts.<n>Experiments on real-world and synthetic benchmarks demonstrate that AnchorMoE achieves highly competitive classification performance.
  arXiv  Detail & Related papers  (2026-06-02T13:30:54Z)
• Factorize to Generalize: Retrieval-Guided Invariant-Dynamic Decomposition for Time Series Forecasting [64.2640656619583]
  Time series foundation models (TSFMs) have recently achieved strong zero-shot forecasting performance through large-scale pretraining and retrieval-augmented prediction.<n>We propose a Retrieval-guided Invariant-Dynamic DEcomposition framework for time series forecasting.
  arXiv  Detail & Related papers  (2026-05-24T07:31:55Z)
• CatalyticMLLM: A Graph-Text Multimodal Large Language Model for Catalytic Materials [61.546562658308005]
  We propose QE-Catalytic-V2, a unified graph--text multimodal large language model for catalytic materials.<n>It integrates property prediction and inverse design within the same model and shared representation space.<n>Under this unified framework, QE-Catalytic-V2 can not only perform reliable property prediction but also generate and screen physically feasible CIF candidates.
  arXiv  Detail & Related papers  (2026-05-17T04:31:46Z)
• K-Models: a Flexible and Interpretable Method for Ordinal Clustering with Application to Antigen-Antibody Interaction Profiles [65.21921642886367]
  K-Models is a novel framework that integrates ordinal constraints and estimates key underlying elements of the random process generating the observed functional profiles.<n>The proposed method is evaluated through simulations and real-world applications.
  arXiv  Detail & Related papers  (2026-05-14T13:35:44Z)
• Three-Stage Learning Unlocks Strong Performance in Simple Models for Long-Term Time Series Forecasting [6.603558172147427]
  STAIR aims to unlock the capacity of simple temporal mapping models without introducing complex architectural modules.<n>It first learns common temporal dynamics across variables through a shared temporal mapping, then adapts the shared model to each variable via channel-wise fine-tuning to capture variable-specific patterns, and finally complements the backbone with cross-variable information through residual learning.
  arXiv  Detail & Related papers  (2026-05-13T15:36:46Z)
• CombinationTS: A Modular Framework for Understanding Time-Series Forecasting Models [15.276272594513427]
  We argue that progress requires a shift from model selection to modular attribution.<n>We propose CombinationTS, a self-contained probabilistic evaluation framework.<n>We show that explicit structural priors yield a more favorable performance-stability trade-off than increasing complexity.
  arXiv  Detail & Related papers  (2026-05-02T04:08:51Z)
• Causal-INSIGHT: Probing Temporal Models to Extract Causal Structure [4.718641165826564]
  Causal-INSIGHT is a model-agnostic, post-hoc interpretation framework for extracting model-implied (predictor-dependent) directed, time-lagged influence structure from trained temporal predictors.<n>We introduce Qbic, a sparsity-aware graph selection criterion that balances predictive fidelity and structural complexity without requiring ground-truth graph labels.
  arXiv  Detail & Related papers  (2026-03-26T14:16:37Z)
• Mixture-of-Models: Unifying Heterogeneous Agents via N-Way Self-Evaluating Deliberation [0.0]
  This paper introduces the N-Way Self-Evaluating Deliberation (NSED) protocol, a Mixture-of-Models (MoM) architecture that constructs emergent composite models from a plurality of distinct expert agents.<n>Unlike traditional Mixture-of-Experts (MoE) which rely on static gating networks, NSED employs a Dynamic Expertise Broker that treats model selection as a variation of the Knapsack Problem.
  arXiv  Detail & Related papers  (2026-01-23T16:11:54Z)
• Drift No More? Context Equilibria in Multi-Turn LLM Interactions [58.69551510148673]
  contexts drift is the gradual divergence of a model's outputs from goal-consistent behavior across turns.<n>Unlike single-turn errors, drift unfolds temporally and is poorly captured by static evaluation metrics.<n>We show that multi-turn drift can be understood as a controllable equilibrium phenomenon rather than as inevitable decay.
  arXiv  Detail & Related papers  (2025-10-09T04:48:49Z)
• Towards Efficient General Feature Prediction in Masked Skeleton Modeling [59.46799426434277]
  We propose a novel General Feature Prediction framework (GFP) for efficient mask skeleton modeling.<n>Our key innovation is replacing conventional low-level reconstruction with high-level feature prediction that spans from local motion patterns to global semantic representations.
  arXiv  Detail & Related papers  (2025-09-03T18:05:02Z)
• Approximate learning of parsimonious Bayesian context trees [0.0]
  The proposed framework is tested on synthetic and real-world data examples. It outperforms existing sequence models when fitted to real protein sequences and honeypot computer terminal sessions.
  arXiv  Detail & Related papers  (2024-07-27T11:50:40Z)
• Bayesian Semi-structured Subspace Inference [0.0]
  Semi-structured regression models enable the joint modeling of interpretable structured and complex unstructured feature effects. We present a Bayesian approximation for semi-structured regression models using subspace inference. Our approach exhibits competitive predictive performance across simulated and real-world datasets.
  arXiv  Detail & Related papers  (2024-01-23T18:15:58Z)
• Time-series Generation by Contrastive Imitation [87.51882102248395]
  We study a generative framework that seeks to combine the strengths of both: Motivated by a moment-matching objective to mitigate compounding error, we optimize a local (but forward-looking) transition policy. At inference, the learned policy serves as the generator for iterative sampling, and the learned energy serves as a trajectory-level measure for evaluating sample quality.
  arXiv  Detail & Related papers  (2023-11-02T16:45:25Z)
• Anomaly Detection of Time Series with Smoothness-Inducing Sequential Variational Auto-Encoder [59.69303945834122]
  We present a Smoothness-Inducing Sequential Variational Auto-Encoder (SISVAE) model for robust estimation and anomaly detection of time series. Our model parameterizes mean and variance for each time-stamp with flexible neural networks. We show the effectiveness of our model on both synthetic datasets and public real-world benchmarks.
  arXiv  Detail & Related papers  (2021-02-02T06:15:15Z)
• Out-of-distribution Generalization via Partial Feature Decorrelation [72.96261704851683]
  We present a novel Partial Feature Decorrelation Learning (PFDL) algorithm, which jointly optimize a feature decomposition network and the target image classification model. The experiments on real-world datasets demonstrate that our method can improve the backbone model's accuracy on OOD image classification datasets.
  arXiv  Detail & Related papers  (2020-07-30T05:48:48Z)
• Supporting Optimal Phase Space Reconstructions Using Neural Network Architecture for Time Series Modeling [68.8204255655161]
  We propose an artificial neural network with a mechanism to implicitly learn the phase spaces properties. Our approach is either as competitive as or better than most state-of-the-art strategies.
  arXiv  Detail & Related papers  (2020-06-19T21:04:47Z)
• Semi-Structured Distributional Regression -- Extending Structured Additive Models by Arbitrary Deep Neural Networks and Data Modalities [0.0]
  We propose a general framework to combine structured regression models and deep neural networks into a unifying network architecture. We demonstrate the framework's efficacy in numerical experiments and illustrate its special merits in benchmarks and real-world applications.
  arXiv  Detail & Related papers  (2020-02-13T21:01:26Z)

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.