Banking

Banking

Modern financial institutions handle huge volumes of information about their customers, known as Big Data.

In contrast to traditional statistical modelling, which finds basically linear relationships between a limited number of variables, machine learning techniques allow patterns to be discovered in millions of data, even those with non-linear relationships.
Applications and usage

• Detect and prevent fraud
• Optimize credit risk calculation systems
• Early detection of payment default
• Prevent customer leakage
• Define expenditure/savings profiles
• Attract new customers with lead scoring
• Create customer segments based on hundreds of variables
• Define the optimal product portfolio for each segment
• Increase cross-selling and up-selling sales
• Create custom recommendations
• Increase lifetime value
• Prevent complaints and queries. Accelerate resolution

TOURISM	RETAIL	MASS CONSUMPTION	BANKING	INSURANCE
Prediction of cancellations	Cross-selling	Cross-selling	Risk management	Cross-selling
Nationality forecasting	Demand forecasting	Demand forecasting	Delinquency detection	Risk management
Occupancy prediction	Basket analysis	Product portfolio optimization	Fraud prevention	Risk management
Personalization of the stay	Custom recommendations	Lead Scoring	Lead Scoring	Lead Scoring
Customer segmentation	Customer segmentation	Customer segmentation	Customer segmentation	Customer segmentation
Optimization of predictions	Optimization of predictions	Optimization of predictions	Optimization of predictions	Optimization of predictions
Increase LTV	Increase LTV	Increase LTV	Increase LTV	Increase LTV
Prediction of abandonment	Prediction of abandonment	Prediction of abandonment	Prediction of abandonment	Prediction of abandonment