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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.

Why GiniMachine?

Smart

The platform works with historical data, including big data and raw data – it requires no preliminary analysis or processing.

Automatic

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.

Visualizing

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.

Easy-to-use

The scoring models can be on-premise or cloud-deployed and require minimum user involvement to provide real-time calculations and predictions in minutes.

Tailored to your needs

GiniMachine can be used for building decision-making models, credit scoring models, application scoring, churn prediction, and more.

Economically Sound

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.

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Case Studies

Success stories of our customers: why they have chosen GiniMachine, what they expected from the platform and which goals they managed to attain.

More case studies

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