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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?