Observability must translate system signals into real-world, business-aligned outcomes.
At CreditAccess Grameen, our technology landscape supports one of the most complex and human-centric business models in financial services. We serve millions of rural and semi-urban women across India, and much of our operations happen far from the reach of core banking infrastructure—often at the edge, in field locations, and under challenging network conditions. In such a model, observability isn’t just about system uptime— it’s about service continuity, performance, scale, field agent productivity, and customer trust. ( We process around 1 lakh++ loan applications a day !!! and hence performance and scale is very imp.
Our digital stack is evolving rapidly, with mobile-first apps, cloud-native services, and integration with multiple fintech API`s. But even with the most advanced systems, problems can arise where visibility is weakest—at the last mile. If a mobile app fails to sync in a low-connectivity area, or if a digital payment stalls at an intermediary node, the customer doesn’t see a backend glitch—they just see a broken promise. That’s why we’ve started rethinking observability—not just as a backend function, but as a field-aware, outcome-driven capability.
In a field-intensive, high-touch business like ours, observability must connect digital signals to real-world
outcomes—and AI helps bridge that gap.
We’re working to stitch together telemetry from application, infrastructure, and user behavior—but the real challenge is interpretation. That’s where AI becomes essential. We’re using AI models to look for early indicators of friction—slower app response in a geography, increasing sync retries, or a spike in manual overrides by agents. These may not always raise traditional alerts, but they often point to a service degradation waiting to happen.
If a region is seeing slower transaction throughput, or if a cloud integration layer is under stress, we want the system to tell us—not just what’s wrong, but what might go wrong, and what to do about it.
Another area we’re focused on is business alignment. As a financial inclusion institution, our business users care about metrics like loan disbursement timelines, repayment syncs, and agent productivity. If observability doesn’t help answer those questions, it’s irrelevant. That’s why we’re building business-facing dashboards, where system health is presented not in CPU or API metrics, but in terms of field-level success rates and process compliance.
This also improves accountability. When observability is democratized—shared with product teams, field operations, and business heads—it becomes a collaborative tool rather than a diagnostic afterthought. People don’t just react to issues—they anticipate them, flag them, and learn from them.
Of course, cost is a factor too. We can’t afford to over-instrument in every environment, especially when dealing with remote regions. We’re using a tiered approach, where critical workflows receive deep observability, and non-critical ones are monitored using AI-based anomaly detection to keep telemetry lean.
–Authored by Sudesh Puthran, Chief Technology Officer, CreditAccess Grameen