Optimize credit portfolios and verify proofs of income by automating credit decisions in milliseconds, radically outperforming traditional scorecards in both consumer and business lending
Help underwriters evaluate creditworthiness using alternate data sources
Predict credit potential of those with no to little credit history data
Use datasets with meta-features in a networked context to generate accurate credit scores of individuals by training machine learning models to generate credit scoring models.
Capture Network Properties of the Customers
Predictive Modelling to give better credit scores.
H2O AI App, built with Wave: Credit Scoring with Graphs
To interact with the Credit Scoring with Graphs app, you must log in with your H2O Cloud Account. If you do not have an account, you can request a demo here. This app demonstrates the use of machine learning as part of an overall plan to minimize employee attrition. Users can view predictions of employee departure, forecast churn rates, and identify relevant factors contained in employee data.
Data Preparation (Customer Metadata, Telecom Data Calls made by the customers)
Nodes are the Customers, if they called each other in a given time span, there exists an edge between them.
Tabular Data converted into a Network/Graph Data.
Credit Score displayed on Wave
Unsupervised Machine Learning, Graph Neural Network, Predictive Modelling
Interactive Component: users can tune multiple parameters (Graph Complexity, Sampling Ratio, Vector Dimension, Node2Vec epochs, etc.
Dashboard to display insights