Enterprise ETL Solution for Financial Data Integration
5M+
Banking Records Ingested daily150+
Institutions Onboarded Simultaneously10+
Custom Plug-ins for Data Ingestion
Customer Overview
Our client is a fintech provider offering a SaaS-based risk monitoring platform that helps banks and financial institutions strengthen controls and meet regulatory obligations. Their platform provides dedicated, isolated environments for each financial institution to ensure data privacy and compliance. It continuously analyses customer, account, and transaction data to detect fraud, credit risk, operational anomalies, and compliance breaches in real-time. As the client was building the platform to support hundreds of institutions from the outset, they required a reliable and scalable approach to ingest and process large volumes of banking data.
Project Overview
The client partnered with TenUp to develop the risk monitoring system, with a core requirement of building a secure, accurate, and scalable data ingestion layer. Since financial institutions do not expose direct access to their core systems, TenUp was tasked with designing a solution to fetch customer, KYC, account, and transaction data from each institution’s infrastructure into the client’s dedicated instance, within a required timeframe and without compromising performance or security. The solution needed to support scheduled, event-driven, and near real-time processing, enforcing strict data-quality governance so the risk monitoring system received accurate, timely, and complete inputs.
Challenges
Building a secure, scalable, and flexible solution to handle diverse data formats, high volumes, and compliance timelines across hundreds of financial institutions.
- Meeting the risk monitoring system’s need for data from financial institutions, despite restrictions on direct core-system access, while supporting time-bound compliance requirements like reporting fraudulent transactions within 24 hours.
- Supporting high-volume ingestion for hundreds of financial institutions, each generating millions of transactions daily.
- Handling data arriving in inconsistent formats across financial institutions, from direct database extracts to files in CSV, Excel, XML, and custom structures.
- Ensuring secure, institution-specific data transfer channels to protect highly sensitive banking data in transit.
- Validating that data received from financial institutions exactly matches the data ingested into the risk monitoring system, ensuring a consistent and reliable state for transaction monitoring.
- Enabling visibility into data pipeline health and managing risks, such as missed triggers, partial or delayed file deliveries, and stalled processes to ensure no data or event is missed or incorrectly processed.
- Managing high-volume datasets without adding latency to risk-monitoring workflows, ensuring the financial institution never misses SAR/STR deadlines or deviates from daily or near real-time monitoring schedules.
Solution
TenUp developed an ETL Solution to ingest, validate, and transfer high-volume banking data across diverse environments, ensuring timely, accurate, and compliant data flow into the risk monitoring system.
- Provided flexible options for banks and financial institutions to share data: restricted direct database access to the core system or export to a dedicated folder in any format (CSV, Excel, XML, or custom structures).
- Developed a pluggable Agent ETL Client to extract data from databases or files in any format using customized plug-ins and prebuilt plug-ins for known core banking architectures.
- Built a proprietary, on-premise Agent ETL Receiver on the fintech company’s side, with dedicated servers and databases for each financial institution, to securely receive, validate, and process incoming data.
- Converted data from all formats into structured XML at the Agent ETL Client end, and enabled validation for XML files for structures and data types using XSDs at both client and receiver ends to ensure correctness and prevent data errors.
- Maintained detailed ledgers and activity logs at both client and receiver ends, capturing timestamps, record counts, and processing logs to provide a complete audit trail and reconcile sent versus received data.
- Implemented notifications and retry mechanisms at both client and receiver ends to detect and address missed triggers, partial or delayed file deliveries, or stalled processes, ensuring no data or event was missed or incorrectly processed.
- Designed distributed and parallel processing pipelines to efficiently handle high-volume datasets at scale, preventing workflow bottlenecks and maintaining timely processing.
- Established secure, institution-specific VPN data transfer tunnels with end-to-end encryption to protect sensitive banking data in transit across hundreds of banks and financial institutions.
- Enabled the Agent ETL Receiver to validate and categorize incoming data, ensuring the downstream risk monitoring system could reliably process all customer, account, and transaction records.
- Enabled visibility into ETL health through monitoring dashboards to proactively detect issues and maintain smooth operations across all data pipelines.
Benefits
The ETL solution TenUp built for a risk monitoring system serving numerous banks and financial institutions offered the following advantages:
- ~99.9% on-time data ingestion for ~5 million records daily, ensuring superior risk monitoring reliability.
- Simplified integration for 150+ banks and financial institutions, enabling simultaneous onboarding and data ingestion.
- Accurate and complete processing of transactions and records, supporting real-time compliance.
- Automated monitoring, audit trails, and ETL pipelines, reducing operational effort and accelerating issue resolution.
Technology
- Java
- OpenVPN
- XML
- XSD
- Microsoft SQL Server
Industry
- FinTech
Conclusion
TenUp’s pluggable and scalable ETL solution enables fintech platforms to ingest, validate, and process high-volume banking data securely and reliably across hundreds of financial institutions. By combining flexible data extraction, structured validation, parallel processing, and institution-specific secure channels, the system ensures accurate and timely delivery of customer, account, and transaction records into the risk monitoring platform. This improves operational visibility, maintains compliance with strict reporting timelines, and reduces manual intervention, allowing financial institutions to trust the platform for real-time risk detection and regulatory reporting.
Frequently asked questions
What is the difference between ETL and ELT in banking data pipelines, and which is better for real-time risk monitoring?
ETL transforms data before loading, enforcing validation and compliance upstream, well‑suited for banking platforms where only clean, structured records should reach risk monitoring systems. ELT loads raw data first, transforming it later, which suits high‑volume analytics but requires strong target‑side governance. For fraud detection with strict data governance and audit trails, ETL‑style upfront processing reduces false alerts and ensures regulatory‑grade accuracy.
How do banks securely transfer sensitive financial data to third-party SaaS platforms without exposing core banking systems?
Banks avoid direct core system access by using outbound-only agent connectors that push data externally, institution-specific VPN tunnels with end-to-end encryption, and scheduled exports in controlled formats like XML or CSV. This architecture keeps core systems isolated while maintaining a compliant, auditable data transfer channel for each institution.
What data quality checks should an ETL pipeline enforce before loading banking data into a risk monitoring system?
A banking ETL pipeline should enforce schema validation (e.g., XSD for XML feeds), null and completeness checks on mandatory fields, duplicate detection, referential integrity across customer‑account‑transaction relationships, and record‑count reconciliation between source and receiver. Running these checks at both extraction and ingestion ends creates a dual-layer validation framework, preventing corrupt data from reaching risk monitoring systems.
What are the most common causes of ETL pipeline failures in high-volume banking environments, and how can they be prevented?
Common causes include missed triggers, partial file deliveries, schema drift, silent zero-record completions, and memory exhaustion during peak loads. Prevention requires retry mechanisms with exponential backoff, record-count ledgers reconciling both sender and receiver ends, real-time anomaly-based alerting, and monitoring dashboards, shifting teams from reactive firefighting to proactive pipeline management.
What role do pluggable connectors or plug-ins play in scaling ETL solutions across diverse banking architectures?
Since banks run different core systems: Temenos, Finacle, Oracle FLEXCUBE, or custom-built infrastructure, pluggable connectors let ETL solutions adapt per institution without rebuilding pipelines. Each plug-in handles source-specific extraction logic, while the core ETL engine manages transformation and loading universally, dramatically reducing onboarding time and cost across hundreds of institutions.
How does parallel processing in ETL pipelines prevent bottlenecks when ingesting millions of banking transactions daily?
Parallel processing partitions transaction data by date range, account segment, or institution and processes each partition simultaneously across multiple nodes. This prevents a single large batch from blocking subsequent ingestion cycles. Without parallelism, peak-volume loads delay downstream risk monitoring workflows, causing missed SAR/STR compliance deadlines, making parallel architecture non-negotiable for high-volume banking ETL.
How do ETL monitoring dashboards improve operational visibility in large-scale banking data pipelines?
ETL dashboards provide real-time visibility into ingestion success rates, record volumes processed versus expected, retry frequencies, and latency per institution. For platforms serving 100+ banks, a single undetected stalled pipeline can cause missed compliance windows. Proactive anomaly-based alerting reduces mean time to resolution, converting reactive firefighting into preventive pipeline governance across all connected institutions.
How can ETL pipelines ensure 99.9% data accuracy in financial systems?
Achieving 99.9% accuracy requires dual-layer XSD schema validation at both extraction and ingestion points, record-count reconciliation comparing sent versus received data, referential integrity checks across customer-account-transaction relationships, and automated retry mechanisms for failed records. Maintaining detailed audit trails with timestamps ensures full traceability, catching data corruption before it reaches downstream financial reporting or risk monitoring systems.
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