8 Takeaways on AI-Powered Observability for BFSI

Key insights from Banking Frontiers’ webinar on AI-Led Observability

Banking Frontiers recently hosted an engaging webinar on AI-led observability with three practitioners who live these challenges every day:

• Shuchi Mahajan: SVP, Fraud Prevention, Analytics & Customer Awareness, HDFC Bank
• Prasanna N: Senior Technology Leader, Federal Bank
• Rao Haridasu: Chief of Staff & Business Strategy, VuNet Systems
  

Moderated by Manoj Agrawal, Group Editor at Banking Frontiers, the discussion went into the realities of outages, complexity, customer expectations, and how AI can turn observability from a dashboard into a strategic capability.

This blog distills the key takeaways and why AI-powered observability is now critical for BFSI.

1. Digital finance runs on fragile, hyper-connected webs

The panel started with a simple example of a single UPI Payment

Behind every ‘Tap and Pay” moment sits a web of:

• Issuing and acquiring banks
• Fraud and risk engines
• Core banking systems (CBS)
• Nodal Regulator and UPI switch
• Multiple internal and external APIs
• Telecom and OTP providers (for other journeys)
  

As Shuchi highlighted, one broken thread cascades across the entire chain. The customer just sees “failed transaction” or “OTP not received”. Social media sees it next, and the disruption is instantly amplified.

At the same time, many banks are still running:

• Legacy cores
• Partly modernized applications
• On-prem + cloud hybrids
• Multiple real-time channels
  

Outages of even 30 minutes can run into millions in potential losses and severe reputational damage.

2. From “what broke” to “what + why + impact.”

Historically, monitoring has been about the “what”: CPU high, memory saturated, latency breached, API failing.

The panel was unanimous: that’s no longer enough.

Shuchi framed it as a 360° approach:

• What is failing? (transactions, APIs, journeys, channels)
• Why is it failing? (config change, external API, fraud control, infra constraint)
• Who is impacted? (segment, geography, merchant, product, customer type)
• How do we prevent it from recurring? (design, process, guardrail changes)

Without tying the what and the why together, banks either:

• Fix the wrong thing, or waste time firefighting a “problem” that doesn’t really exist
  

She gave a powerful example:

• A spike in declined transactions might look like a “tech issue.”
• But a deep dive may reveal it’s actually fraud defence working as designed on a specific pattern
• In that case, the bank should not roll back changes under pressure. It should communicate better

3. Customer experience: when “everything is green” but the customer is still unhappy

One of the most interesting threads was around subjective customer experience.

Shuchi shared a story:

• Customers were going to “Update Contact Details.”
• 70% were dropping off without updating
• Everyone suspected a tech issue
• When they actually called customers, the answer was simple:

• • On mobile, the Logout and Update Contact Details buttons were too close
  • People were tapping “Update” by mistake when they just wanted to log out
    

A simple UI change (more spacing) fixed everything.

On paper, all systems were “green”: There were no errors, no latency breaches, and no infra bottlenecks

Yet there was a real user experience problem, and it only surfaced when observability was combined with human feedback and behavioral insights.

4. From reactive firefighting to proactive prevention

Today, many banks still learn about incidents through:

• Call centers escalating spikes in complaints
• Social media blow-ups
• Manual war rooms launched after the damage is done
  

Prasanna’s point was clear: this is not sustainable for the scale of digital we are now operating at.

AI-powered observability can change this in several ways:

1. Anomaly detection, not just threshold breaches
   

• Instead of alerting only when latency hits 90%, AI can flag unusual spikes from 20% to 60% long before failure.
  

2. Pattern-based alerts across channels and times, such as 
   

• salary days, rent days, bill payment cycles, and festival spikes

• Learning these seasonal and behavioral patterns lets AI distinguish “normal heavy load” from “worrying deviation”.
  

3. Proactive customer messaging
    Imagine this: as soon as a payment fails, the bank’s systems detect a wider issue and send:

   “We’ve noticed an issue on this channel. Your money is safe, and we’re processing a refund. No action needed from your side.”
   That transforms frustration into trust.

5. Business-centric observability: Looking beyond uptime metrics into critical business dimensions

Rao brought in a critical lens: observability must be business-centric.

Within a bank, “customers” are more than individuals:

• Retail customers using UPI, cards, loans, and internet/mobile banking
• Merchants, billers, aggregators, and partners whose business depends on the bank’s rails

Business teams want to know, in real time:

• Which merchants are growing or declining?
• Where are failures creeping up – a specific merchant, geography, channel, or biller?
• After onboarding 10 new partners, how many are actually generating volumes?

Traditionally, this visibility comes via weekly or monthly MIS reports.

With AI-powered observability on transaction data:

• Merchant and partner performance becomes real-time
• Relationship Managers can proactively engage where failures rise, or volumes dip
• Business, risk, operations, and IT all look at the same truth, from different lenses

6. AI + enriched data: where the real magic is

All three panelists agreed: AI is only as powerful as the data and context you feed it.

Rao put it sharply: if you run models only on technical signals (CPU, memory, raw error codes), the reasoning power is limited.

But if you enrich that same telemetry with:

• Business context (product, merchant, journey, segment, geography)
• Risk context (risk scores, AML flags, exposure)
• Fraud context (behavioral anomalies, device patterns, dormant-to-sudden-active shifts)
  

…then AI can:

• Spot suspicious transaction spikes from dormant merchants or unusual locations
• Correlate technical incidents with business impact (e.g., “UPI failures for high-value corporate payers in region X”)
• Trigger fraud/risk alerts based on real-time behavior instead of static rules
  

Similarly, in fraud prevention, Shuchi highlighted the move towards:

• Behavioral biometrics
• Customer typing/swiping patterns
• Historical transaction behavior
  

This allows banks to block only truly suspicious activity and reduce false positives.

7. Guardrails, governance, and the human in the loop

The panel did not sugar-coat the challenges:

• Data silos across legacy and modern systems
• Privacy and regulatory requirements for PI data
• Hallucination and bias risks in large language models
• Shortage of people who can design meaningful AI use cases, not just “play with prompts.”
  

Shuchi and Prasanna both stressed:

• Data quality & completeness are non-negotiable
• AI systems must be grounded in authoritative data (RAG, domain constraints, etc.)
• Banks need explainability – models must explain why they flagged something
• There must be strong guardrails and governance around usage, especially in fraud and customer-facing scenarios

In short: AI is powerful, but it is not a magic brick you plug in and forget. It’s a system you design, supervise, and continuously refine.

8. So, why is AI-powered observability critical for BFSI?

Across the conversation, the answer emerged clearly:

1. Scale & complexity have outgrown human monitoring.
    Billions of daily transactions, thousands of APIs, multiple clouds, and partners. Humans alone can’t see or reason about it in time.
   

2. Customer and merchant expectations are unforgiving.
    They want instant payments, instant answers, instant refunds. Any friction erodes trust, and they have alternatives.
   

3. Regulators and governments are pushing digital trust and resilience.
    Systemic disruptions in payments or banking now have national-level implications.
   

4. AI unlocks the move from visibility → insight → action.
   

• • • Visibility: see what’s happening, across systems and journeys

    • Insight: know why it’s happening and who it affects

    • Action: recommend and increasingly automate the right response
      

5. Business value goes far beyond IT metrics.
    AI-powered observability enables:
   

• • • Better customer experience

    • Stronger fraud and risk controls

    • Smarter growth through real-time merchant and product insights

    • Higher operational efficiency and lower firefighting
      

Summary

AI will not replace the people running BFSI technology, but people who know how to use AI-powered observability will outperform those who don’t.

As Rao framed it, when observability, recommendability, and resolution come together, you move towards a world where systems don’t just tell you what went wrong, they guide you on what to do next, and increasingly help do it for you.

That is the promise and the necessity of AI-powered observability in modern BFSI.