Back

Analyze customer data to calculate risk scores and predict probability of default, optimizing portfolio management.

AI Solution Type: AI Agent that does not include a chatbot (it is possible to integrate a conversational interface or AI chatbot, if required)

Traditional Process: Financial risk evaluation is often based on basic credit rating models, which may not capture complex behavior patterns.

Application of Supervised Machine Learning (ML):

1. Data collection and analysis: Credit histories, income, payment patterns, market data.
2. Predictive model creation: The system trains with compliant vs. defaulting customers.
3. Risk score calculation: A probability of default is assigned to each customer.
4. Segmentation and personalized actions: Adjust credit terms for high-risk customers, offer incentives to reliable ones, etc.
5. Update and continuous improvement: The model is recalibrated with new data and changes in the economy.

Benefits:

• Reduction in financial losses: By anticipating default risk, preventive measures are taken.
• Improved portfolio management: Allows for more effective collection and credit strategies.
• Data-based decisions: Identifies complex correlations difficult to see manually.
• Greater competitiveness: Efficiently managing risk allows offering attractive conditions to reliable customers.

Conclusion: Evaluating financial risks with ML makes the process more precise and proactive. By protecting the portfolio and reducing defaults, companies strengthen themselves in a changing economic environment.