Understanding AI-powered observability in financial services

Financial institutions face unique challenges in monitoring complex, transaction-critical systems where outages directly impact revenue and compliance. AI-powered observability is transforming how these organizations detect, diagnose, and remediate issues by moving from reactive, threshold-based monitoring to proactive, intelligent observation that connects technical performance to business outcomes. 

Let me take a step back and establish some fundamentals before diving into the more advanced concepts of AI-powered observability for financial institutions. 

What is observability in simple terms? 

Observability is fundamentally about understanding what’s happening inside your systems by examining their outputs. In financial services, this means: 

• Collecting data about how systems are performing (metrics) 
• Tracking interactions between systems (traces) 
• Recording important events (logs) 
• Understanding context of all this information 

Traditional monitoring tells you when something is broken. Observability helps you understand why it’s broken and how it impacts your business. 

The current reality for most financial institutions 

Most banks, insurance companies, and financial services firms today are at varying stages of observability maturity: 

1. Basic monitoring: Using tools like Splunk, Datadog, or New Relic to set static thresholds and alerts 
2. Manual correlation: When issues occur, teams manually piece together what happened across different systems 
3. Siloed visibility: Separate tools for infrastructure, applications, and business metrics 

For example, a typical mid-sized bank might: 

• Use AppDynamics to monitor their mobile banking application performance 

• Have Splunk for security and log analysis 

• Use CloudWatch for their AWS infrastructure 

• Have separate business dashboards for transaction volumes 

When an issue occurs, such as slower payment processing, teams often spend hours in war rooms trying to determine if the problem is in the database, application code, network, or elsewhere. 

How AI is beginning to transform this picture

AI is starting to bridge these gaps by: 

1. Automating pattern detection: Instead of humans setting thresholds, machine learning identifies what’s “normal” for your systems Real example: Capital One uses anomaly detection algorithms to automatically identify unusual patterns in their credit card processing systems without manual threshold configuration. 
2. Connecting the dots: Correlation engines that link seemingly unrelated events Real example: JPMorgan Chase implemented correlation analysis that automatically connected slow database queries with increased memory usage on specific application servers during month-end processing. 
3. Providing business context: Understanding the relationship between technical issues and business outcomes Real example: American Express built models that correlate API performance metrics with transaction approval rates, automatically estimating financial impact of technical issues. 

An example to illustrate the difference

Traditional approach: 

1. An insurance company’s claims processing system slows down 
2. Multiple alerts fire about high CPU, database connections, and response times 
3. Different teams check their respective areas 
4. After hours of investigation, they discover a database index issue 
5. Business impact is calculated after the fact 

AI-enhanced observability approach: 

1. ML-based anomaly detection notices subtle changes in database query patterns before performance degrades significantly 
2. The system automatically correlates these patterns with similar past incidents 
3. Root cause analysis suggests database index issues with 85% confidence 
4. The system estimates business impact: approximately 3,200 delayed claims processing worth $1.4M in total value 
5. Resolution teams receive specific recommendations based on past solutions 

This is not science fiction—banks like BBVA and insurers like Allianz are implementing these capabilities today, though most are still in early stages. 

How esynergy can help

esynergy helps financial institutions enhance observability with AI by combining technical excellence and deep financial domain expertise: 

• Maturity assessment: Benchmark your current observability against industry best practices and identify quick wins. 

• Platform strategy: Build a cohesive strategy that maximizes existing investments and closes capability gaps. 

• Implementation expertise: Deploy OpenTelemetry, configure AI-enhanced observability platforms, and build custom ML models for financial services. 

• Financial domain knowledge: Apply expertise in transaction flows, regulatory compliance, and business impact modelling. 

• Proof of value projects: Deliver targeted improvements with measurable outcomes in payments, claims, and trade execution monitoring. 

• Capability building: Upskill your teams through workshops, practice development, and AI/ML training for operations. 

Let’s connect to explore how we can accelerate your observability journey and deliver tangible business value. 

ABOUT THE AUTHOR

Prasad Prabhakaran is an AI expert with a proven track record of delivering AI, data, and digital solutions at organizations such as Microsoft, HSBC, Samsung, Howden Group, Janus Henderson, and ABN Amro. His key projects include LLM- and RAG-enabled intelligent document processing AI agents for Howden, AI agent design for HMRC, ONS, and Connected Mortgage, pricing models for Samsung, credit risk models for HSBC, anomaly detection for Mitsubishi, and code quality prediction for Microsoft. 

He specializes in Generative AI, machine learning, data science, and AI product development. Before joining esynergy as Head of AI, Prasad founded a startup that developed an NLP- and NLU-based AI tutor for personalized, adaptive learning.