218 citations · 811 across the 19 of their papers we have counts for
19 papers
INFLECT-DGNN: Influencer Prediction with Dynamic Graph Neural Networks
Elena Tiukhova, Emiliano Penaloza, María Óskarsdóttir +3
Leveraging network information for predictive modeling has become widespread in many domains. Within the realm of referral and targeted marketing, influencer detection stands out a…
Optimizing Credit Limit Adjustments Under Adversarial Goals Using Reinforcement Learning
Sherly Alfonso-Sánchez, Jesús Solano, Alejandro Correa-Bahnsen +2
Reinforcement learning has been explored for many problems, from video games with deterministic environments to portfolio and operations management in which scenarios are stochasti…
Multi-Modal Deep Learning for Credit Rating Prediction Using Text and Numerical Data Streams
Mahsa Tavakoli, Rohitash Chandra, Fengrui Tian +1
Knowing which factors are significant in credit rating assignment leads to better decision-making. However, the focus of the literature thus far has been mostly on structured data,…
Assessment of creditworthiness models privacy-preserving training with synthetic data
Ricardo Muñoz-Cancino, Cristián Bravo, Sebastián A. Ríos +1
Credit scoring models are the primary instrument used by financial institutions to manage credit risk. The scarcity of research on behavioral scoring is due to the difficult data a…
Influencer Detection with Dynamic Graph Neural Networks
Elena Tiukhova, Emiliano Penaloza, María Óskarsdóttir +5
Leveraging network information for prediction tasks has become a common practice in many domains. Being an important part of targeted marketing, influencer detection can potentiall…
Statistical network isomorphism
Pierre Miasnikof, Alexander Y. Shestopaloff, Cristián Bravo +1
Graph isomorphism is a problem for which there is no known polynomial-time solution. Nevertheless, assessing (dis)similarity between two or more networks is a key task in many area…
On the dynamics of credit history and social interaction features, and their impact on creditworthiness assessment performance
Ricardo Muñoz-Cancino, Cristián Bravo, Sebastián A. Ríos +1
For more than a half-century, credit risk management has used credit scoring models in each of its well-defined stages to manage credit risk. Application scoring is used to decide…
Deep residential representations: Using unsupervised learning to unlock elevation data for geo-demographic prediction
Matthew Stevenson, Christophe Mues, Cristián Bravo
LiDAR (short for "Light Detection And Ranging" or "Laser Imaging, Detection, And Ranging") technology can be used to provide detailed three-dimensional elevation maps of urban and…
On the combination of graph data for assessing thin-file borrowers' creditworthiness
Ricardo Muñoz-Cancino, Cristián Bravo, Sebastián A. Ríos +1
The thin-file borrowers are customers for whom a creditworthiness assessment is uncertain due to their lack of credit history; many researchers have used borrowers' relationships a…
A transformer-based model for default prediction in mid-cap corporate markets
Kamesh Korangi, Christophe Mues, Cristián Bravo
In this paper, we study mid-cap companies, i.e. publicly traded companies with less than US $10 billion in market capitalisation. Using a large dataset of US mid-cap companies obse…
Improving healthcare access management by predicting patient no-show behaviour
David Barrera Ferro, Sally Brailsford, Cristián Bravo +1
Low attendance levels in medical appointments have been associated with poor health outcomes and efficiency problems for service providers. To address this problem, healthcare mana…
Multilayer Network Analysis for Improved Credit Risk Prediction
María Óskarsdóttir, Cristián Bravo
We present a multilayer network model for credit risk assessment. Our model accounts for multiple connections between borrowers (such as their geographic location and their economi…
Super-App Behavioral Patterns in Credit Risk Models: Financial, Statistical and Regulatory Implications
Luisa Roa, Alejandro Correa-Bahnsen, Gabriel Suarez +3
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. These alt…
Evolution of Credit Risk Using a Personalized Pagerank Algorithm for Multilayer Networks
Cristián Bravo, María Óskarsdóttir
In this paper we present a novel algorithm to study the evolution of credit risk across complex multilayer networks. Pagerank-like algorithms allow for the propagation of an influe…
The value of text for small business default prediction: A deep learning approach
Matthew Stevenson, Christophe Mues, Cristián Bravo
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 availa…
The Value of Big Data for Credit Scoring: Enhancing Financial Inclusion using Mobile Phone Data and Social Network Analytics
María Óskarsdóttir, Cristián Bravo, Carlos Sarraute +2
Credit scoring is without a doubt one of the oldest applications of analytics. In recent years, a multitude of sophisticated classification techniques have been developed to improv…
Credit Scoring for Good: Enhancing Financial Inclusion with Smartphone-Based Microlending
María Óskarsdóttir, Cristián Bravo, Carlos Sarraute +2
Globally, two billion people and more than half of the poorest adults do not use formal financial services. Consequently, there is increased emphasis on developing financial techno…
Social Network Analytics for Churn Prediction in Telco: Model Building, Evaluation and Network Architecture
María Óskarsdóttir, Cristián Bravo, Wouter Verbeke +3
Social network analytics methods are being used in the telecommunication industry to predict customer churn with great success. In particular it has been shown that relational lear…
A Comparative Study of Social Network Classifiers for Predicting Churn in the Telecommunication Industry
Maria Óskarsdóttir, Cristián Bravo, Wouter Verbeke +3
Relational learning in networked data has been shown to be effective in a number of studies. Relational learners, composed of relational classifiers and collective inference method…