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AI-driven resolution process

AI-driven resolution process

Companies that may be challenged by user have several resolution options, namely, a friendly negotiation, trade-off or withdrawal, or, by using court services. In the latter case, the company may let the decision defend itself, or hire attorneys to defend it. The process of choosing to hire attorneys or not is often subjective, fuzzy, unscientific, and case specific. In this use case, we illustrate how machine learning can help insurance companies assess the relevance of hiring an attorney to defend their decisions to minimize attorney services costs, make better decisions that are likely to be irrefutable by customers, and judges otherwise. 

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Churn prediction using supervised machine learning

In business, customer churn refers to customers abandoning a brand and stopping being a paying customer. It is commonly agreed that re-engaging an old customer is easier and more reliable that acquiring a new one. Therefore, it is advisable to anticipate client's churn and take corrective actions. This is where machine learning is best fit. In this use case, we will see how machine learning can help predicting the departure of customers.

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Churn prediction using supervised machine learning