Banking
Solutions - Banking
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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.
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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 |
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Sectors
Campaign optimization - Customer segmentation - Customer retention - Custom recommendations - Cross-selling & Upselling - Demand forecasting
Overbooking management - Customer retention - Custom recommendations - Customer segmentation
Demand forecasting - Cross-selling & Upselling - Lead Scoring - Optimization of promotional campaigns
Prediction of non-payments - Fraud detection - Lead Scoring - Customer retention - Risk Scoring
Risk Scoring - Prediction of abandonment - Prediction of non-payments - Automatic dispatching of operations
Demand forecasting - Lead Scoring - Automatic dispatching of operations