A leading global bank struggled with inefficient risk analytics, compliance challenges, and fragmented data systems. Their existing data infrastructure was unable to support real-time fraud detection, credit risk analysis, and regulatory reporting, leading to delays in decision-making and increased operational risks.

• Siloed & Inconsistent Data: Multiple legacy systems stored financial transactions, customer interactions, and credit data in separate data warehouses, making it difficult to consolidate insights.
• Regulatory Compliance & Reporting Delays: Compliance with Basel III, IFRS 9, and Anti-Money Laundering (AML) regulations required manual reconciliations, causing delays.
• Inefficient Risk Scoring Models: Traditional credit risk models lacked real-time processing capabilities, leading to delayed fraud detection and inaccurate loan risk assessments.
• High Cost of Legacy Infrastructure: Maintaining on-prem SQL-based data warehouses for massive financial datasets was costly and lacked scalability.

• Enterprise Data Lake Implementation: AiRO helped the bank transition from legacy SQL-based data warehouses to a modern Data Lake architecture (Azure Data Lake & Delta Lake) for real-time data processing and centralization.
• Advanced Risk Analytics with AI/ML: Implemented AI-powered risk models using Apache Spark & Databricks, enabling real-time fraud detection, credit risk analysis, and loan default predictions.
• Regulatory Compliance Automation: Developed automated reporting pipelines using Azure Synapse Analytics and Power BI, reducing compliance reporting time by 60%.
• Real-Time Data Processing: Integrated Apache Kafka for streaming financial transactions in real time, allowing instant fraud detection and improved customer profiling for risk assessments.
• Data Governance & Security: Implemented Role-Based Access Control (RBAC), PII data masking, and GDPR compliance frameworks, ensuring secure access and regulatory adherence.

• 75% Faster Risk Analysis: AI-powered fraud detection models flagged suspicious transactions in milliseconds, reducing fraud losses.
• 60% Reduction in Compliance Processing Time: Automated reporting pipelines streamlined regulatory reporting workflows.
• 30% Cost Reduction in Data Infrastructure: Migrating from on-prem SQL-based warehouses to cloud-based Data Lakes significantly reduced operational costs.
• Real-Time Customer Insights & Loan Approvals: AI-driven credit risk models enabled instant loan approvals, improving customer satisfaction.
• Secure & Scalable Data Infrastructure: Centralized data governance and encryption ensured data integrity, security, and regulatory compliance

By leveraging AI-driven data modernization and risk analytics, the bank transformed its risk management, compliance, and decision-making capabilities. With real-time fraud detection, automated regulatory reporting, and scalable cloud infrastructure, they achieved faster insights, reduced costs, and enhanced financial security.