Predictive Analytics for Financial Services
GiniMachine’s predictive analytics platform is a powerful tool for building risk models. Due to a combo of friendly UI and AI/ML behind the curtain, the platform is a perfect fit for any type of business in the field of financial services.
No-Code Predictive Analytics for Finance
Financial institutions are embracing fintech to gain a competitive advantage. The platform provides great value for financial organizations and alternative lenders: it works with large datasets to make lending, P2P investment, and marketing decisions data-driven and beneficial.
The platform works with historical data, including big data and raw data – it requires no preliminary analysis or processing.
GiniMachine does the analysis for you: it implements a special decision tree ensemble method, a set of heuristics, and a few proprietary algorithms for the effortless development of prediction models.
You get access to valuable financial insights, graphs, and diagrams. They sort data types by importance, calculate Gini Index or K-S Score, and visualize thresholds crucial for decision-making.
The scoring models can be on-premise or cloud-deployed and require minimum user involvement to provide real-time calculations and predictions in minutes.
GiniMachine can be used for building decision-making models, credit scoring models, application scoring, churn prediction, and more.
Our clients agree that GiniMachine is a promising investment due to a reduction of labor costs, improved loan portfolios, and higher customer satisfaction.
Apply for a personal live demo and our team will guide you through the GiniMachine interface: from uploading the data to building the model and analyzing the graphic representation of model indicators.
Success stories of our customers: why they have chosen GiniMachine, what they expected from the platform and which goals they managed to attain.
Start Using GiniMachine Today
Get a quick product tour, ask your questions, and check out if it fits your business needs. We’ll get back to you as soon as we can.