Real-time payments have become the beating heart of APAC’s digital economy. The region is setting the global pace for instant, 24/7 transactions.
For example, Indonesia’s BI-FAST is one of the world’s largest and most modernized real-time payment initiatives, incorporating 135 banks, multi-tenant aggregators, and non-bank participants.
Indonesia launched its first real-time payments system BI-FAST, owned and regulated by Bank Indonesia, in December 2021. As a later participant, it was able to learn from the real-time journeys of other countries, and was live in nine months.
Indonesia is now ranked as #8 in the world’s top 10 fastest-growing real-time payment markets, with almost 2 billion real-time payment transactions in 2023 and a CAGR of 44.6% (2023-2028).
While the push for global financial inclusion is revolutionizing the payments industry, rapid cross-border integration leaves payment systems vulnerable, and open to failure.
The limits of traditional observability tools
In the payments world, milliseconds define user experience. When a real-time payment fails or stalls, customers don’t wait - they abandon, complain, or move to competitors.
A single delay or system glitch can ripple across networks, costing money, partners, and reputations.
Traditional observability tools weren’t built for this environment - they were designed for static systems. They monitor, and provide visibility, but they don’t have the power to anticipate. They trigger alerts reactively, when thresholds are breached - after the fact. Challenges with this type of monitoring include:
-
Data silos
-
Alert fatigue
-
Lack of contextual correlation
Banking Ops teams can struggle to repair the damage, wading through dashboards, working against the clock to piece together logs and metrics from multiple systems.
In a high-velocity payments ecosystem, where transaction volumes and dependencies are growing, that’s too late, because in real time, customers only see a ‘payment failed’ message.
The next evolution: AI-native observability
With global payment systems evolving at breakneck speed, it’s no longer about reducing Mean Time to Repair (MTTR); it’s about creating a new benchmark, and a proactive approach - Mean Time to Prevent (MTT-Prevent).
MTT-Prevent enables banks and financial institutions to identify risks before they become outages.
AI-native observability is about baking intelligence into the core of observability. This is not just AI-assisted dashboards where traditional monitoring systems have been enhanced with a few algorithms - but systems where AI is at the center, to provide:
Autonomous anomaly detection at a transactional level
This means tracking and catching micro-patterns that humans or traditional/legacy systems would miss.
Context-aware correlation across channels, geographies and partners
Revealing the interconnectedness of network elements for a deep understanding of observability data. This includes:
-
Understanding how different elements or variables interact
-
Identifying patterns or trends
-
Predicting future outcomes
-
Conducting root cause analysis when issues arise
Natural language insights
Enabling teams to ask questions like, “What caused the decline spike in Singapore?” and get actionable answers.
Predictive prevention, not just retrospective reporting
Identifying and resolving potential incidents before they impact customers and systems. For example:
-
Analyzing transactions in real-time to flag high risk activity
-
Studying customer behavior and transaction history to detect potential fraud
-
Using automated network analysis to spot suspicious patterns in communications.
MTTR and MTT-Prevent: Flagging alerts vs built-in foresight
In payments, reliability and trust equates to reputation. The fewer failed transactions customers experience, the stronger the brand and loyalty become.
Millions of scenarios occur within global payments infrastructures every day. Imagine for example, spotting a sudden rise in payment timeouts. Traditional tools raise an alert, and engineers start digging for the cause, desperately scrambling to reduce MTTR.
With AI-native observability, the system has already identified that the issue isn’t within your infrastructure, but a third-party API under strain in a specific region.
With MTT-Prevent in action, load-balancing rules can be auto-adjusted to reroute traffic before transactions fail.
MTT-Prevent can turn downtime into uptime, and insight into value.
The banking challenge: Volume, complexity, and customer expectation
For banks across the APAC region, the shift to real-time payments and the continuing development of cross-border payment rails has introduced a new set of challenges.
-
Transaction volumes are volatile, spiking unpredictably with salary runs, retail campaigns, or cross-border events.
-
Multi-rail complexity - from card networks and instant payment schemes to open banking APIs - means hundreds of interconnected systems must operate flawlessly, 24/7.
-
At the same time, customer experience expectations have soared. Consumers expect instant payments, zero downtime, and seamless cross-platform performance.
Traditional monitoring can’t keep up with this kind of dynamic change. It’s no longer enough to know what failed; we need to know why - or what’s about to fail next.
This is where AI-native observability and the MTT-Prevent mindset become game-changers.
By analyzing patterns across rails, channels, and geographies in real time, AI-native observability can anticipate stress points before they turn into incidents.
By detecting subtle performance drifts, MTT-Prevent strategies can auto-correlate signals across complex ecosystems, and trigger preventative actions. This results in fewer outages, faster responses, and a payment experience that feels invisible – precisely the way customers want it to be.
Why APAC is leading the shift
The APAC region’s fast-moving and diverse payments ecosystem is uniquely positioned to lead the AI-observability revolution.
-
Regulators such as MAS and NPCI are pushing for transparency and resilience.
-
The market is hyper-competitive, driving adoption of technologies that give even a few milliseconds of advantage.
AI-native observability provides a competitive edge measured in both speed, and confidence.
From MTTR to MTT-Prevent: How to make the shift
Moving from reactive recovery to proactive prevention is more than a tooling upgrade – it must be seen as a mindset shift.
Many payment organizations still use MTTR as a benchmark metric.
The next generation of payments resilience will be measured by how rarely issues occur at all.
Here are some steps to begin the journey from MTTR to MTT-Prevent:
Unify your observability data
Gather telemetry from every rail, API, and infrastructure layer. Silos make it impossible to see the full payments journey or detect cross-system anomalies early.
Embed AI at the core, not at the edge
For true implementation of MTT-Prevent, AI is integral to the observability fabric, by continuously learning normal behaviour patterns and predicting deviations before they escalate.
Automate the insight-to-action loop
Once potential risks are detected, automation should kick in immediately, rerouting traffic, scaling resources, or alerting teams with contextual insights. The key to switching from MTTR to MTT-Prevent is reducing human lag time.
Evolve metrics and culture
Shift KPIs from “time to fix” to “incidents prevented.” Encourage teams to value foresight and system health over firefighting actions.
With these steps, banks, financial institutions and payment providers can move beyond chasing alerts and start building self-healing, self-learning systems that not only support real-time payments but protect them.
What AI-Native Observability Means for Banking Ops Teams
For operations teams, AI-native observability is about amplifying human expertise – not replacing it.
But instead of wasting valuable time chasing incidents across dashboards and tickets, teams gain a single, intelligent view of their entire payments ecosystem.
AI-native observability surfaces what matters, filters out the noise, and even recommends next steps. This means less firefighting, more foresight, and the ability for real-time payments to evolve faster across the globe.
Ready to discover deeper insight and drive business growth? Find out how with IR Transact.
Frequently Asked Questions
Q: What’s the difference between monitoring, observability, and AI-native observability?
A: Monitoring alerts you when something fails.
Observability helps you understand why it failed.
AI-native observability predicts when it might fail - and enables you to stop it before it happens.
Q: What does MTT-Prevent actually measure?
A: MTT-Prevent is a forward-looking resilience metric that tracks how effectively your systems can detect and mitigate potential incidents before they impact performance or customer experience.
Q: Why is this especially critical in payments?
A: Because payments are 24/7/365. With multi-rail transactions, fluctuating volumes, and always-on customer expectations, a single delay can escalate into failed transactions, financial loss, and reputational damage. MTT-Prevent isn’t just operational - it’s strategic.
Q: How can banks start implementing AI-native observability?
A: Begin by consolidating data sources, integrating AI-driven analytics into existing observability platforms, and automating responses to recurring patterns. Gradually evolve KPIs from recovery time to prevention success rates.
Q: Will AI-native observability replace human operators?
A: No, it augments human input. AI handles pattern recognition and prediction at scale, freeing operations teams to focus on higher-value tasks like optimization, innovation, and customer experience.
