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GiniMachine – a Ready-to-Use AI/ML Tool for Decision Making

The GiniMachine team releases a ready-to-use version of the platform on the official website.

What is GiniMachine?

GiniMachine is an intuitive no-code platform for scoring, predictive analytics, and risk management powered by AI/ML on one side and users’ historical data on the other side.

The use cases include credit and collection scoring, customer churn prediction, the discovery of cross-sell and up-sell opportunities, employee turnover predictions, and a lot more.

GiniMachine automates and speeds up data-centric decision-making. It builds predictive models along with drawing and structuring the most impactful insights from the datasets in a few seconds.

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Social and economic impact

The system provides social and economic value as well: the implementation of GiniMachine positively impacts financial inclusion in countries with unbanked populations. Lack of credit history is no more a problem because GiniMachine can work with alternative borrowers’ data, maximize inclusion, and minimize risks.

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How it works

The system utilizes datasets with information about existing customers, employees, or processes and historical outcomes for each record. For example, borrowers appeared to be reliable or unreliable, employees – retained or left, purchases – completed or abandoned.

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Dataset analysis allows AI/ML to create predictive models with a high Gini index. Every model comes with a report, where users can check which record attributes were important for AI and how the decisions will be made.

The system includes the profit forecast functionality, a detailed attribute importance report for market insights, and model monitoring diagrams to ensure the high predictive power of the models in the long run.

GiniMachine can be either provided on a SaaS basis or can be deployed on the client’s servers. In both cases, the company demonstrates extra care of security issues, and businesses remain the owners of their data.

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[To share your insights with us, please write to sghosh@martechseries.com]

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