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How RPA in Banking is beneficial

Banking Industry has been transforming over the years. Technology has allowed the banks to manage their data much better making it centrally available. This data is also used now to get insights that were previously not possible.

Over the years the core banking function has involved a lot of people. With people, there is always a requirement for processes so that everyone in the organisation can take the same decisions. However, people following processes sometime maybe tired or be biased or bored or another people issue you have seen, which may lead to poor decisions. Research now has proven that computers are more reliable and unbiased while taking the decision based on processes and policies. This brings us to the concept of Robotic Process Automation.

RPA involves studying the existing processes and automating the most obvious and straightforward decisions in the process. This allows the companies to get better results on the execution of those processes. People are moved to doing more productive and creative things like identifying new revenue sources or creating new banking programs that can help the customer satisfaction with their bank.

RPA in banking - Mantra Labs

In this era of digitization, the existing banks face the extra challenges of digitization across the whole banking value chain as well as new banking models that are coming up with new age players. This combined with the regulatory frameworks that keep a tight noose and higher operating capital requirements make it a difficult business to be in.

RPA provides a side gate of relief for the banking industry as a whole with the benefits it entails. By using robotic algorithms the decisions can be made faster leading to faster processing cycles for every transaction. Computer programs can also perform self-audits to be compliant to regulations as soon as they change or apply. The existing infrastructure remains same which means not a great spend in opex. With data centrally managed and automated programs looking for insights it can bring huge benefits by finding deep operational insights to save time and cost.

RPA benefits in banking - Mantra Labs

The automation and computer software may make you feel that RPA is only applicable in IT systems however there are more areas that RPA can help. The key areas in banking that can be benefitted from RPA.
– Reporting
– Compliance
– Cyber Risk and Resilience
– sourcing and procurement
– Accounting and Administration
– Securities operations

Mantra Labs has been working with leading FinTech companies like EzeTap, Religare, I&M Bank and others in the fields of Payments, Insurance, Banking Solutions, Micro lending and newer initiatives of AI, Blockchain and Robotics Process Automation. For any query, contact us at hello@mantralabsglobal.com

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Silent Drains: How Poor Data Observability Costs Enterprises Millions

Let’s rewind the clock for a moment. Thousands of years ago, humans had a simple way of keeping tabs on things—literally. They carved marks into clay tablets to track grain harvests or seal trade agreements. These ancient scribes kickstarted what would later become one of humanity’s greatest pursuits: organizing and understanding data. The journey of data began to take shape.

Now, here’s the kicker—we’ve gone from storing the data on clay to storing the data on the cloud, but one age-old problem still nags at us: How healthy is that data? Can we trust it?

Think about it. Records from centuries ago survived and still make sense today because someone cared enough to store them and keep them in good shape. That’s essentially what data observability does for our modern world. It’s like having a health monitor for your data systems, ensuring they’re reliable, accurate, and ready for action. And here are the times when data observability actually had more than a few wins in the real world and this is how it works

How Data Observability Works

Data observability involves monitoring, analyzing, and ensuring the health of your data systems in real-time. Here’s how it functions:

  1. Data Monitoring: Continuously tracks metrics like data volume, freshness, and schema consistency to spot anomalies early.
  2. Automated data Alerts: Notify teams of irregularities, such as unexpected data spikes or pipeline failures, before they escalate.
  3. Root Cause Analysis: Pinpoints the source of issues using lineage tracking, making problem-solving faster and more efficient.
  4. Proactive Maintenance: Predicts potential failures by analyzing historical trends, helping enterprises stay ahead of disruptions.
  5. Collaboration Tools: Bridges gaps between data engineering, analytics, and operations teams with a shared understanding of system health.

Real-World Wins with Data Observability

1. Preventing Retail Chaos

A global retailer was struggling with the complexities of scaling data ope