In fintech, stale data is a dealbreaker.
When you’re tracking stock listings, company updates, and live market movements, every minute counts. And that’s the challenge Finnpick, a fintech platform that curates insights on listed companies, was facing. Their goal was simple but extremely important: to provide investors and analysts with up-to-date market data every single day.
But the process behind it wasn’t simple at all.
The Problem: Manual Updates Slowing Down Market Insights
Until recently, Finnpick’s data management process was completely manual.
Every time a company’s stock was listed, opened, or closed, the team had to log in, collect information, and update the records manually in their system.
At a smaller scale, this worked, but as the platform grew and the number of tracked companies increased, the process became tedious and error-prone.
- The data team spent hours every morning updating stock information.
- Delays meant that investors were sometimes viewing outdated data.
- The system couldn’t scale fast enough to handle more markets or instruments.
For a fintech platform that prided itself on precision and reliability, this slowdown had no place in the workflow.
Our Approach: Turning a Manual Routine Into a Dynamic System
Our mission was clear: what needed to be done was to make Finnpick’s data engine fully autonomous.
So we began by mapping how data flowed from external sources into their platform and identifying integration points with reliable financial APIs. Then, we designed a backend architecture that could run continuously without human intervention.
Here’s how we built it:
1. Automated Data Fetching:
Implemented cron-based schedulers to fetch stock listing and trading data at regular intervals. These jobs run silently in the background, pulling updates on open/close statuses, price movements, and new listings.
2. API-Driven Synchronization:
The system connects directly to trusted financial market APIs, ensuring that every piece of data originates from verified sources.
3. Data Validation and Normalization:
Before writing to the database, our backend checks the integrity and structure of every record, filtering duplicates and normalizing data formats.
4. Dynamic Database Updates:
Using Node.js and PostgreSQL, we built an optimized data pipeline that updates live tables instantly, ensuring that the web interface and dashboards always show real-time data.
5. Error Handling and Logging:
We introduced fail-safes to handle API downtime or data mismatches. Logs are stored and monitored through AWS CloudWatch, enabling automatic retries and quick visibility into system health.
The Results: Quantified
| Metric | Before | After |
|---|---|---|
| Daily Update Time | 2–3 hours (manual) | < 5 minutes (fully automated) |
| Data Freshness | Updated once per day | Updated every hour via cron |
| Error Rate | ~10–15% human entry errors | < 1% (auto-validation) |
| Scalability | Limited to 1 market | Multi-market ready |
| Operational Efficiency | Manual updates by 2 team members | Fully autonomous backend |
The Tech Stack That Made It Happen
- Backend: Node.js (Express)
- Database: PostgreSQL
- Scheduling: Cron Jobs (Node-Cron)
- Integration: Financial Market APIs
- Infrastructure: AWS (Lambda, RDS, CloudWatch)
- Security: API key rotation and restricted access controls
What Changed for Finnpick?
- 100% Elimination of Manual Work: The daily routine of updating hundreds of stock records is now fully automated.
- Real-Time Accuracy: Users always see the latest verified data, refreshed automatically at set intervals.
- Improved Efficiency: The operations team now focuses on analytics and product growth, not repetitive updates.
- Scalable Foundation: The new architecture can easily integrate additional markets, APIs, or data points in the future.
In just a few weeks, Finnpick evolved from a manual data operation to a modern fintech platform running on a self-sustaining backend, a transformation that not only improved reliability but also unlocked new opportunities to scale.
In Conclusion
In fintech, reliability is everything, and it comes from automated, error-free systems.
When your users depend on accurate, timely data to make investment decisions, you can’t afford delays or errors. By automating Finnpick’s data pipeline, we helped them deliver a better, more reliable experience to their users and gave them the infrastructure to scale without hitting another wall.
At Absolute App Labs, this is the kind of challenge we live for: taking operational headaches and turning them into elegant, scalable systems that let fintech companies move fast and stay sharp.