How Enteros Leverages Performance-Driven AI and an Advanced AI Tech Stack to Transform Database Efficiency in the Banking Sector with AIOps and Cloud FinOps

1. Explosive Growth of Data

   • Read moreOracle AWR data storage and collection frequency optimization.

     Banks process millions of transactions per second, alongside large-scale risk and fraud detection computations.

   • Customer data, mobile banking usage, and regulatory records contribute to exponential data growth.

2. Complex Database Ecosystems

   • Read moreMongoDB profiler and database performance problem diagnosis and identification

     Banks use a mix of relational databases (Oracle, SQL Server, PostgreSQL) and NoSQL systems (MongoDB, Cassandra).

   • Legacy and cloud-native databases coexist, making optimization difficult.

3. Regulatory Compliance and Risk

   • Regulatory frameworks like Basel III, GDPR, and PCI-DSS demand accurate and timely data processing.

   • Poor performance or downtime can lead to compliance breaches and fines.

4. Cost Pressures

   • Cloud adoption in banking has shifted IT spend toward operational expenses.

   • Without cost attribution and optimization, cloud database costs can spiral out of control.

5. Need for Real-Time Insights

   • Fraud detection, credit scoring, and trading systems depend on real-time analytics.

   • Even slight database latency can cause financial and reputational damage.

Enteros’ AI-Driven Approach to Database Performance

Enteros brings a performance-first AI architecture designed to handle the scale and complexity of banking systems. Its solution combines AI-driven root cause analysis, statistical AI models, anomaly detection, and optimization algorithms with the power of AIOps and Cloud FinOps.

1. AI-Driven Root Cause Analysis

• Identifies bottlenecks in real-time across query performance, resource utilization, and workload spikes.

• Uses machine learning to detect anomalies before they escalate into outages.

2. Performance Optimization with SQL AI

• Optimizes query execution plans dynamically.

• Reduces CPU, memory, and I/O bottlenecks.

• Increases transaction throughput for critical applications like fraud monitoring and mobile banking.

3. AIOps for Proactive Operations

• Automated incident detection, diagnosis, and resolution.

• Predictive maintenance for databases to prevent downtime.

• AI-based alerts minimize noise, ensuring IT teams focus on critical issues.

4. Cloud FinOps for Cost Optimization

• Provides transparency into database-related cloud spend.

• Forecasts usage costs and aligns spend with business KPIs.

• Allocates costs across departments, enabling accountable financial governance.

5. Integration with Banking IT Ecosystem

• Works across hybrid and multi-cloud deployments.

• Integrates with compliance and monitoring systems.

• Adapts to SaaS, PaaS, and on-premise infrastructures.

Benefits of Enteros for the Banking Sector

1. Improved Transaction Speed and Reliability

• Faster query execution ensures smooth digital banking experiences.

• Critical operations like fund transfers and ATM transactions execute with minimal delays.

2. Proactive Risk and Fraud Detection

• Real-time data processing enhances fraud detection algorithms.

• AI-driven anomaly detection identifies suspicious activity at scale.

3. Regulatory Compliance and Reporting

• Ensures timely data processing for compliance audits.

• Automates data workflows to reduce human error.

4. Optimized Cloud Spending

• Tracks unblended and blended cloud rates.

• Recommends workload placement across cloud providers for cost efficiency.

• Forecasts future database spend using AI-based cost estimation models.

5. RevOps Efficiency

• Aligns IT, finance, and operations teams through shared observability metrics.

• Connects performance data with revenue impact, enhancing decision-making.

• Enables scalable operations without runaway costs.

Use Case: A Global Bank’s Transformation with Enteros

A leading multinational bank struggled with:

• High latency in risk modeling workloads.

• Rising cloud database costs.

• Frequent performance incidents during peak transaction periods.

Enteros Solution:

• Implemented AI-powered workload forecasting and SQL optimization.

• Deployed AIOps-driven root cause analysis for faster incident resolution.

• Applied Cloud FinOps practices to attribute costs across risk, retail, and corporate banking units.

Results Achieved:

• 35% improvement in query performance.

• 28% reduction in cloud database costs within six months.

• 40% faster incident resolution, reducing operational risks.

The Future of AI-Driven Database Performance in Banking

Banking IT will continue evolving with:

• Generative AI-powered financial modeling for fraud and risk scenarios.

• AI-driven capacity planning to predict future workload requirements.

• Advanced FinOps governance to ensure sustainable cloud adoption.

Enteros positions itself as a critical enabler of this transformation by combining AI, observability, and financial accountability into a single, unified platform.