From Uncertainty to Reliability: Transforming Banking Batch Jobs with Observability

It’s payday! Some employees haven’t received their salaries, triggering frantic calls to the payroll team. Scrambling to track down the root cause, the team discovers a data processing bug incorrectly marking some active employees as ‘inactive’. Salaries went unpaid, morale took a hit, and time was lost in manual fixes.        

On the other side of the world, corporate clients of a global bank notice delayed transaction confirmations. Exchange rates have now changed, generating penalties and wiping out potential gains. Furious clients escalate the matter, compelling the bank to compensate for the financial loss and urgently re-run the delayed batch jobs.

What went wrong?

These are not one-off mishaps. They are classic examples of unnoticed errors in critical batch jobs – errors that lead to spiralling financial losses, damaged customer trust, and operational chaos.

What led to these errors? Can batch jobs be made error-proof?

To address these questions, let’s first explore why banks rely on batch jobs.

Importance of Batch Jobs in Banking

Banks handle a vast number of business-critical repetitive processes, which, if done manually, would be labor-intensive, error-prone, and cause delays. To streamline these effort-intensive and time-consuming processes, batch jobs become essential.

Batch jobs process large volumes of data and execute repetitive tasks as a group rather than handling them individually in real-time. They run automatically in the background at scheduled intervals without requiring user intervention. Often, these jobs are scheduled during off-peak hours to optimize computing resources and improve efficiency.

The table below highlights examples of batch jobs commonly run by Banks.

 Fig 1. Examples of Batch Jobs in Banks

As evident, batch jobs are not just technical tasks but business-critical operations where a single failure can trigger a cascade of issues, disrupting downstream processes. Each job is interconnected with multiple upstream and downstream jobs, requiring precise sequencing and timely execution to meet strict SLAs. Any failure can lead to financial losses, compliance risks, and operational inefficiencies.

The image below provides the business criticality and dependencies that exist in batch jobs in Banking.

Fig 2. View of Dependencies and Workflow Journey in Banking Batch Jobs

Causes of batch job failures and their impact

Batch Jobs are mostly forgotten until something goes wrong. When a batch job fails, the consequences can be severe, including financial loss, compliance issues, and major business disruptions. In corporate banking, especially with high-volume, high-value transactions, downtime or failure can mean significant financial risk.

Here are some common causes of batch job failures.

Fig 3. Causes of Batch job failures

One of the major challenges we have observed is sequencing dependency, where “Batch B” starts executing before “Batch A” completes, leading to erroneous outputs that affect downstream systems and ultimately business outcomes.

When a batch job breaks down, the consequences extend far beyond technical issues.

Fig 4. Business Impact of Batch Job Failures

As mentioned earlier, the very nature of batch jobs means they go unnoticed until something goes wrong. But when an issue arises, forget fixing it; even identifying where and what caused the failure is extremely complex and time-consuming.

By the time the SREs and ITOps team sift through the numerous steps in batch processing and the vast volumes of data involved, the issue has impacted customers, escalating its severity and urgency.

To prevent such failures and, more importantly, their consequences, banks need an Observability solution that provides complete visibility into every step of the job, from initialization to completion, all in real-time, enabling banks to detect, diagnose, and resolve issues proactively.

Challenges in Current Observability Solutions

Despite deploying multiple observability tools, inefficiencies persist because traditional solutions fail to address key business needs:

• • Lack of Business Context: No visibility into regulatory deadlines, currency exchange constraints, and liquidity thresholds that banks navigate daily.
  • Limited Dependency Mapping: Poor visualization of job dependencies requires manual coordination.
  • No Business Impact Assessment: Focused solely on raw metrics without quantifying the business impact of failures
  • Silo-ed Monitoring: Limited integration across different banking systems

Without these capabilities, batch job failures remain hard to predict, diagnose, and resolve, leaving banks vulnerable to operational disruptions.

Business Observability: A Domain-centric approach for Batch Jobs

Traditional observability solutions focus primarily on technical metrics, often overlooking the business context that is crucial for batch job efficiency. A domain-centric approach to Business Observability bridges this gap by aligning batch job monitoring with real-world banking operations, ensuring smooth execution and minimizing disruptions.

 Key capabilities of a business observability solution include:

• • End-to-end Visibility: A single-pane view of batch workflows and their dependencies between batch jobs, with their status and health
  • Tracing: Track job dependencies and bottlenecks across multiple systems
  • Alerting: Set up automated alerts for job failures, delays, and SLA breaches
  • Predictive Analytics: Using AI/ML to detect anomalies and prevent failures proactively.
  • Root-Cause Analysis: Correlate data from various sources with a business context to quickly pinpoint the cause of failure, drastically reducing MTTR
  • Recovery and Self-Healing: Establish auto-retry mechanisms for failures and self-healing scripts to rerun failed jobs
  • Metrics: Monitor KPIs like job duration, success/failure rates, delays, resource usage, and data accuracy to provide insights into job performance.
  • Dashboards and Reporting: Provide real-time visualization of batch job performance and generate reports for compliance and audits

Fig 5. Benefits of Batch Job Observability

Best Practices in Batch Jobs Observability

To fully leverage the advantages of batch jobs observability and mitigate potential challenges, the following best practices are recommended.

• • Data Validation: Ensure correct input data before processing
  • Centralized Logging: Store logs in a unified system for easy access
  • Automated Job Retries: Reduce manual intervention for transient failures
  • AI-Based Anomaly Detection: Use machine learning to predict job failures proactively
  • Scalability Considerations: Ensure observability tools can handle high volumes
  • Compliance & Auditability: Maintain logs and traces for regulatory requirements
  • End-to-end Visibility: Create a complete view of the batch job workflow journey

VuNet’s Business-centric approach for Batch Jobs Observability

At VuNet, we understand that batch jobs in banking are not just technical processes—they are business-critical operations with significant financial and regulatory implications. Our platform vuSmartMaps is designed to provide domain-centric observability, offering deep insights into batch jobs, their dependencies, and their business impact. Here’s how we do it:

• • Domain Specific Adaptors: Our platform employs domain-specific adaptors that interface directly with Core Banking Systems, Middleware (CBX, or ABP, AML, etc.) to pull out key fields like batch IDs, job IDs, transaction statuses, and even “end-of-day” (EOD) flags. Instead of presenting raw logs, we can craft contextual dashboards showing how batches flow from one system to another, highlighting interdependencies and potential points of failure.
  • Dependency Mapping: By capturing and correlating batch IDs, job IDs, and stream IDs across systems, our platform maps the dependencies between batch jobs helping banks understand the sequence of jobs and how they impact each other
  • Proactive Alerts and SLA Monitoring: By ingesting historical performance data of batch jobs, the platform can baseline normal operating conditions—for example, a remittance batch typically completes in 2 hours, but at month-end it spikes to 4 hours. With these baselines, the system generates proactive alerts if a job is trending slower than expected, well before it breaches the SLA window.
  • Predictive Analytics: Our platform uses predictive analytics to forecast how long a batch job will take, especially during peak periods like month-end and recommend preemptive actions.. This helps banks proactively manage SLAs and avoid delays.
  • Regulatory Compliance: The platform ensures the Bank’s batch jobs are compliant with regulatory requirements. For example, our platform can track the generation of LCR reports and ensure that they are submitted to the RBI on time.
  • Real-time Dashboards: Our real-time dashboards provide a comprehensive view of batch workflows, including the status of each job, SLA compliance, and business impact. These dashboards are highly customizable, allowing banks to focus on the metrics that matter most to them.

Fig 6. Sample Dashboard displaying the health and performance of Batch Jobs

• • Hyper-Configurability: One of the major strengths of the platform is hyper-configurability.
    
    • Flexible Data Ingestion: Whether logs are coming from a legacy core banking platform, a modern microservices-based system, or an external payment gateway, the platform’s adapters handle a variety of data formats.
    • Custom Dashboards: You can quickly build dashboards that depict exactly how your batch workflows connect across systems.
    • Multi-Level Alerting: The platform supports different severity levels and routes alerts to the right teams—IT operations for infrastructure-level issues and business operations for SLA or functional issues.

Here are some examples of batch jobs, Observability enabled by VuNet.

• • Case Study 1: A major bank implemented vuSmartMaps for their Integrated Payment Systems application, which processes client bulk transactions through various payment channels (NEFT/RTGS/FT/IMPS). The existing tools couldn’t provide adequate visibility, Our solution provides unified dashboard views of job start/end times, job execution times, process queues with status, and alerts for stalled or overrunning processes. This has dramatically improved payment processing reliability, reduced manual intervention, and better customer service.
  • Case Study 2: Another bank uses the platform for monitoring batch jobs. A dashboard view provides all relevant details of the jobs, including execution times (Expected vs Actual start and end times), SLA compliances, and drill-down capabilities identifying the cause of failure. Additionally, drill-down capabilities in the dashboard view provide insights into the input and output jobs, providing the ability to minimise disruptions.

Conclusion

Batch Jobs are high-stakes processes of banking operations – crucial for daily settlements, corporate payments, regulatory compliance, and much more. Traditional observability solutions fall short in providing business insights, making failures difficult to predict and resolve. VuNet’s vuSmartMaps bridges this gap by offering business-centric observability, enabling proactive monitoring, automated root cause analysis, and SLA compliance tracking.

If your bank struggles with batch job failures, it’s time to move beyond traditional monitoring tools. Contact VuNet Systems today to transform your batch job observability and optimize business outcomes!