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Analytics solutions for banking, financial services and insurance
Data sets
Data cleaning and preparation
Identifying useful data
Missing value replacement
Feature engineering
Customer analytics
Customer acquisition
Will this person become a customer?
Which offer will convince this person to become a customer?
Which advertising message will convince this person to become a customer?
Customer personalization
Which services will each customer be most likely to buy, if offered?
Is this customer open to a cross sell or upsell?
Churn
Will this customer leave?
Which offer or message will convince this customer not to leave?
Pricing
Price optimization
How should this service be priced to maximize profit?
Dynamic pricing
Fraud, risk, waste and abuse
Fraud analytics
Is this transaction or claim legitimate?
Is this transaction or claim unusual or unexpected?
Risk
Is this customer going to pay back the money they borrowed?