AI-Driven Banking Process Automation and Customer Insights Platform

The client faces significant challenges including the need to leverage massive transaction and customer data for operational improvements and customer experience enhancement. Additionally, the bank faces a high churn rate of approximately 6% annually in its home loan portfolio and lacks insights into underlying customer dissatisfaction, hindering targeted retention strategies.

A large, multi-national bank operating across several countries and languages, aiming to enhance customer experience and operational efficiency through AI technology.

• Automate complex banking processes to reduce manual effort by approximately 35%, thereby increasing operational accuracy by 50%.
• Implement AI-powered chatbots within web and mobile platforms to handle over 50% of customer service requests, reducing manpower costs by around 20%.
• Develop predictive models that rank customers on a 1 to 10 scale concerning their likelihood to churn, enabling targeted retention interventions.
• Identify main reasons behind customer dissatisfaction to facilitate personalized engagement and improve retention rates by 20%.
• Use advanced machine learning techniques to analyze transactional data and optimize ATM cash levels, achieving an ATM service level improvement of around 92%.
• Integrate AI solutions into existing CRM systems via APIs for seamless data access and operational coherence.

• Multilingual AI assistant integrated into web and mobile banking apps to handle customer inquiries and complaints in real-time.
• Customer churn prediction model ranking customers from 1 to 10 with reason explanations to support proactive engagement.
• Analytics engine analyzing transaction data, holidays, pay days, and ATM locations to determine optimal cash levels and improve ATM service levels.
• APIs for seamless integration of AI insights with the bank’s existing CRM and operational systems.
• User interface dashboards for customer service teams to view customer risk scores, reasons, and operational analytics.

• CRM systems for data access and operational workflows
• Transactional data sources for analytics and machine learning model inputs
• Security systems for fraud detection and data privacy
• Notification and communication platforms for customer outreach

• System scalability to accommodate increasing transaction volumes and data points beyond 10 million records
• Real-time processing capabilities for customer interactions and analytics
• High security standards compliant with financial data regulations
• System availability of 99.9% uptime to ensure continuous customer service
• Latency targets under 2 seconds for customer interaction and data retrieval

Implementation of the AI-driven platform is expected to reduce manual processes by approximately 35%, increase operational accuracy by 50%, and improve ATM service levels by 92%. Additionally, the solution aims to decrease customer churn by 20% through targeted engagement and increase customer retention rates, while reducing manpower costs by 20% via automation. These improvements will position the bank as a technologically advanced leader in customer service and operational efficiency.