Finance is a domain where millions can be lost or saved in the blink of an eye. With digital transactions doubling and cyber threats becoming increasingly sophisticated, the time has come to end reactive firefighting and episodic system checks.
The industry's dialogue is pivoting to observability: the power to not only watch systems, but to know them in real time, predict issues before they occur, and correlate technology performance to business results. It's a change taking place under the radar, often beneath the surface, but its effects resonate with everything from customer confidence to fiscal stability.
Few experts exemplify this transformation more fully than Geetha Krishna, an expert whose work has rewritten the way mission-critical financial systems remain tenacious under stress. His professional life has been founded on rendering complicated digital ecosystems transparent, predictable, and, most importantly, reliable.
From avoiding multimillion-dollar fraud attempts to achieving 100% service-level compliance for high-stakes transactions, Krishna's work has made observability an engineering niche term mainstream into a strategic business benefit for his company.
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When he began his journey, one of the biggest challenges was fragmented visibility. “We had separate tools for Salesforce, core banking, and payment systems, each showing only part of the picture,” he recalls. That fragmentation created dangerous blind spots during outages and slowed down diagnosis.
He addressed the challenge by deploying a unified observability platform built on open standards such as OpenTelemetry. This led to a 300% increase in API performance visibility and a 45% reduction in Mean Time to Resolution (MTTR).
“Now we can pinpoint a root cause in under 15 minutes. Before, it took over three hours,” he says. The change not only saved engineering hours but also kept services operational during high-volume transaction periods, protecting significant revenue.
In finance, speed isn’t just about customer experience; it’s about security. Fraud detection traditionally relied on batch-based processes, meaning suspicious activity could go unnoticed for hours.
Krishna changed that. By integrating real-time anomaly detection into observability pipelines and linking them to automated fraud-blocking workflows, he cut detection time from 24 hours to less than two minutes.
The payoff was immediate: $2.5 million in potential losses prevented in the first year alone. “The cost of being reactive is always higher than building proactive systems,” he says. “In finance, it’s not a nice-to-have, it’s survival.”
For many organizations, compliance with standards like SOX, PCI DSS, and GDPR is a separate, time-consuming process. His approach was to embed compliance checks directly into observability dashboards, automating the capture of audit-ready logs as part of everyday operations.
This reduced manual reporting time by 60% and eliminated audit findings for three consecutive years. “Compliance shouldn’t be a scramble before an audit,” he explains. “When it’s part of your operational DNA, you protect the business and free up teams for higher-value work.”
Observability is often thought of as an engineering function, but Krishna sees it differently. For him, it’s also about people. By linking observability alerts to Salesforce Omni-Channel, his teams could proactively notify customers within 30 minutes of detecting an incident.
The impact was tangible: a 6% jump in Customer Satisfaction (CSAT) scores and a 40% drop in complaints during outages. “Transparent, timely communication matters as much as fixing the problem itself,” he says. “It turns a potential trust crisis into an opportunity to show reliability.”
Though his work hasn’t been limited to a single environment. He has led multi-cloud observability projects spanning AWS, Azure, and on-prem systems, unifying dashboards for microservices, databases, and customer-facing applications. At one fintech client, this cut MTTR from seven hours to under 20 minutes and pushed uptime from 90.5% to 99.98%.
His enterprise-wide platform rollouts, predictive alerting frameworks, and API health monitoring solutions have all followed the same principle: context-rich, business-aligned observability beats raw data every time. “Terabytes of logs are useless without correlation,” he notes. “The real power is knowing exactly which service, which customers, and which business metrics are affected in real time.”
Krishna notes that observability is becoming increasingly connected to business outcomes. He points to developments such as AI-driven root cause analysis and automated remediation capable of resolving issues without human intervention, alongside “security-converged observability” that unifies operational and threat monitoring.
“Open standards like OpenTelemetry will dominate,” he says. “We’ll also see a move toward business-aware observability, where telemetry connects directly to KPIs such as revenue, conversion rates, and churn risk.”
His track record, from 100% SLA compliance for critical systems to $2.5 million in fraud prevention, is measurable, yet the deeper impact lies in the operational resilience embedded into his organization’s processes. For Krishna, observability is not just about visibility; it’s about enabling the right people to act on the right information at the right time.
“In the end, it is about trust, trust that systems will work, that problems will be caught before they spread, and that customers will be kept in the loop,” he says. “When you have that, you have the foundation for everything else.”
As financial systems grow more complex and the cost of downtime rises, observability is emerging as a critical discipline, not just for monitoring uptime, but for supporting reliability, security, and informed decision-making across the business.
