AI in production

AI supports business decisions while maintaining full control, auditability, and regulatory compliance.

• Credit decision engine. Credit scoring and risk assessment with explainable factors influencing the final decision.
• Fraud detection. Real-time transaction scoring and alerts for suspicious patterns.
• Claims triage. Automatic prioritization and routing of insurance claims based on risk level.
• Customer next best action. Personalized offer recommendations aligned with privacy policies and regulatory requirements.
• Risk assessment models. Models assessing operational, credit, and market risk with a complete audit trail.

Impact:

• Decision support with full auditability
• Consistency of decisions across all channels
• 30–50% reduction in fraud-related losses
  

Typical use cases:

• Retail, SME, and corporate credit scoring
• Real-time fraud detection
• Insurance underwriting

AI supports interactions with customers and employees, improving service quality, personalization, and access to knowledge.

• Banker co-pilot. An assistant for employees with access to knowledge bases, procedures, and policies, providing suggestions for next steps in customer interactions.
• Customer-facing agents. Chatbots and voice bots serving customers with access to product information and the ability to perform selected operations.
• Personalization engine. Real-time personalization of communications and offers based on customer behavior.
• Sentiment analysis. Analysis of customer sentiment and satisfaction based on conversations and interactions.

Impact:

• Real-time personalization of customer communication
• 20–40% increase in cross-sell conversion
• Improved customer retention

Typical use cases:

• AI assistant for call center agents
• Chatbots for banking or insurance products
• Personalized communication in mobile banking

AI enables organizations to act proactively — predicting customer behavior, identifying emerging risks, and recommending next business actions.

• Predictive analytics. Models predicting churn, customer lifetime value, and changes in credit risk levels.
• Recommendation systems. Product recommendations tailored to customer needs and life stages.
• AI in digital channels Intelligent journeys in mobile apps and web services that adapt to customer behavior.
• Advanced risk models. Models supporting stress testing, scenario analysis, and portfolio optimization.

Impact:

• Ability to respond before problems emerge
• Better strategic decisions based on predictive insights
• Increased customer lifetime value

Typical use cases:

• Customer churn prediction
• Product recommendations tailored to customer needs
• Credit or insurance portfolio risk prediction