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Found 16 papers, 6 papers with code
Attention-based Dynamic Multilayer Graph Neural Networks for Loan Default Prediction
We enhance the model by using a custom attention mechanism that weights the different time snapshots according to their importance.
INFLECT-DGNN: Influencer Prediction with Dynamic Graph Neural Networks
We compare the results of various models to demonstrate the importance of capturing graph representation, temporal dependencies, and using a profit-driven methodology for evaluation.
Optimizing Credit Limit Adjustments Under Adversarial Goals Using Reinforcement Learning
Second, given the particularities of our problem, we used an offline learning strategy to simulate the impact of the action based on historical data from a super-app in Latin America to train our reinforcement learning agent.
Multi-Modal Deep Learning for Credit Rating Prediction Using Text and Numerical Data Streams
In this paper, we present an analysis of the most effective architectures for the fusion of deep learning models for the prediction of company credit rating classes, by using structured and unstructured datasets of different types.
Assessment of creditworthiness models privacy-preserving training with synthetic data
Credit scoring models are the primary instrument used by financial institutions to manage credit risk.
Influencer Detection with Dynamic Graph Neural Networks
Leveraging network information for prediction tasks has become a common practice in many domains.
On the dynamics of credit history and social interaction features, and their impact on creditworthiness assessment performance
Application scoring is used to decide whether to grant a credit or not, while behavioral scoring is used mainly for portfolio management and to take preventive actions in case of default signals.
Deep residential representations: Using unsupervised learning to unlock elevation data for geo-demographic prediction
We consider the suitability of this data not just on its own but also as an auxiliary source of data in combination with demographic features, thus providing a realistic use case for the embeddings.
On the combination of graph data for assessing thin-file borrowers' creditworthiness
Here we introduce a framework to improve credit scoring models by blending several Graph Representation Learning methods: feature engineering, graph embeddings, and graph neural networks.
A transformer-based model for default prediction in mid-cap corporate markets
In this paper, we study mid-cap companies, i. e. publicly traded companies with less than US $10 billion in market capitalisation.
Improving healthcare access management by predicting patient no-show behaviour
In this context many researchers have used multiple regression models to identify patient and appointment characteristics than can be used as good predictors for no-show probabilities.
Multilayer Network Analysis for Improved Credit Risk Prediction
We present a multilayer network model for credit risk assessment.
Evolution of Credit Risk Using a Personalized Pagerank Algorithm for Multilayer Networks
Our personalized PageRank algorithm for multilayer networks allows for quantifying how credit risk evolves across time and propagates through these networks.
Super-App Behavioral Patterns in Credit Risk Models: Financial, Statistical and Regulatory Implications
In this paper we present the impact of alternative data that originates from an app-based marketplace, in contrast to traditional bureau data, upon credit scoring models.
The value of text for small business default prediction: A deep learning approach
Compared to consumer lending, Micro, Small and Medium Enterprise (mSME) credit risk modelling is particularly challenging, as, often, the same sources of information are not available.
The Value of Big Data for Credit Scoring: Enhancing Financial Inclusion using Mobile Phone Data and Social Network Analytics
In terms of profit, the best model is the one built with only calling behavior features.
