Intelligent Risk Profile Assessment
Customization Based on Customer Profiles
Our micro-segmentation approach analyses each customer individually using sophisticated machine-learning algorithms. This in-depth analysis allows us to segment customers based on various criteria, such as:
Financial Behaviours:
- Transaction habits analysis:
- Evaluation of transaction types (deposits, withdrawals, transfers) performed by the customer.
- Frequency and volume of operations:
- Monitoring the frequency of transactions and the amounts processed to identify financial behaviour patterns.
Demographic Data:
- Personal information:
- Collection of data such as age, residence, profession, and marital status for a better understanding of the customer’s profile.
- Geographic context:
- Analysis of risks associated with the regions where the customer resides or operates, considering local risk levels.
Transaction History:
- Complete transaction history:
- Aggregation of transactional data over a given period to detect recurring patterns and anomalies.
- Detection of unusual behaviours
- Identify transactions that deviate from the customer’s usual behaviour, such as unusual fund transfers or suddenly high amounts.