SENTIENT Case Study

Bank Improves Increases Customer Retention with Upgraded AI Model

Customer

Diversified Bank

Industry

Banking & Finance

Function

Customer Churn

Problem

A diversified banking company sought a way to improve its ability to accurately predict customer engagement and reduce churn rates by at least 10%.

Solution

With the help of a data science consulting firm, the company was able to develop and deploy a trained ML model using historical account transactions and trends. The model was able to provide insights on client behavior by analyzing a monthly account portfolio.

Result

The model successfully improved churn prediction accuracy by 20%, and allowed for analysis into the key features determining prediction and customer churn. This better enabled the company to increase the retention of profitable accounts and launch targeted retention incentives.

More Use Cases

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Fraud Detection

Application of ML in preventing credit card fraud detection

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Email : info@unitx.tech

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