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Found 7 papers, 0 papers with code
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.
Proactive Detractor Detection Framework Based on Message-Wise Sentiment Analysis Over Customer Support Interactions
In this work, we propose a framework relying solely on chat-based customer support (CS) interactions for predicting the recommendation decision of individual users.
Feature-Level Fusion of Super-App and Telecommunication Alternative Data Sources for Credit Card Fraud Detection
In this paper, we review the effectiveness of a feature-level fusion of super-app customer information, mobile phone line data, and traditional credit risk variables for the early detection of identity theft credit card fraud.
Relational Graph Neural Networks for Fraud Detection in a Super-App environment
Large digital platforms create environments where different types of user interactions are captured, these relationships offer a novel source of information for fraud detection problems.
Enhancing User' s Income Estimation with Super-App Alternative Data
This paper presents the advantages of alternative data from Super-Apps to enhance user' s income estimation models.
Supporting Financial Inclusion with Graph Machine Learning and Super-App Alternative Data
The presence of Super-Apps have changed the way we think about the interactions between users and commerce.
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.
