Symend Boosts Performance and Fuels AI Innovation with Airbyte

About Symend

Symend is the leader in intelligent collections, designed to help enterprises maximize delinquency management and optimize key metrics like recovery rates, liquidation rates, roll rates, and time to cure. By leveraging applied behavioral science and AI, Symend’s digital debt recovery solution empowers businesses in financial services, telecommunications, and utilities to create hyper-personalized engagements that guide customers towards positive financial outcomes.

Founded in 2016 and globally recognized for innovation, Symend has served over 200 million customers worldwide, driving meaningful and sustainable improvements in collections strategies while fostering lasting customer relationships.

Challenges with legacy infrastructure necessitate a change

Symend transforms delinquency management by combining AI and behavioral science to address psychological barriers that prevent customers from resolving debt. This innovative approach surpasses traditional methods relying on credit data and call centers. By creating personalized digital experiences, Symend improves collection rates, lowers cost-to-collect, and increases customer lifetime value. 

But scaling its impact meant overcoming major data infrastructure challenges. With a growing customer base and ambitious AI goals, Symend's engineering team needed more than a legacy data pipeline tool could offer.

"Data strategy plays a huge role. It is a foundation for anything we do in the company. We collect a lot of data, and we want to make sure that the data is being leveraged in the right way," says Raman Singh, Tech Lead and Data Architect at Symend, who has been with the company for five years.

Symend originally built its EL layer using Microsoft Azure Data Factory (ADF). But as the company scaled, the team quickly ran into significant challenges with the Microsoft platform regarding performance, scalability, cost, usability, and support.

Modern data movement, powered by Airbyte

Raman led a thorough evaluation process, conducting extensive market research and reviewing technical articles. 

"We basically short-listed a few potential vendors in this space. After some discussions, we did a three month, deep-dive POC with Airbyte, just to test all the functionalities and understand how it works,” Raman says of his evaluation process.

Airbyte gave Symend:

• Distributed architecture that eliminates cascading failures across client pipelines
• Streamlined setup and scalability with fast deployment and flexible configuration
• Easy-to-manage connectors for Microsoft SQL, unstructured data in S3/Blob, and other enterprise sources
• Predictable, straightforward pricing model that simplified budgeting and significantly reduced overall spend
• Ability to create simpler, more consistent pipelines that scale with their business

Raman says, “And then we decided that this is the right tool for us."

Enabling AI and self-service analytics at scale

With Airbyte feeding data into Snowflake, Symend now supports high-impact use cases like:

• Scoring models in Databricks to predict customer payment behavior
• Sentiment analysis and feature engineering for ML applications
• Snowflake-powered chatbot utilizing Cortex AI for natural language data analysis
• Self Serve analytical dashboards to allow analysis of customer engagement performance

"The data that powers those models in Snowflake and what brings data to Snowflake is Airbyte," Raman explains. The chatbot feature in particular is a game-changer for Symend’s operational mobility. 

"This chatbot is a true self-service experience, removing bottlenecks from technical teams and enabling clients, business users, project managers, CEOs, CFOs, to just ask any questions to the data through natural language and gain insights."

Airbyte’s distributed architecture levels up performance

One of the most significant improvements came in system performance and reliability. Symend’s existing infrastructure did not feature sufficient parallelization, resulting in cascading issues — when one job failed, it often caused all the following jobs to fail. 

"With our legacy framework, if one of the pipelines fails for one client, it will stop everything for the rest of our clients. But with Airbyte, things are run in parallel because of the platform’s distributed nature, which means that we can process multiple clients at the same time without impacting performance."

This architectural advantage has allowed Symend to dramatically improve their data refresh rates. They have reduced latency from 2 hours to an hour, and in some cases as low as 30 minutes. This is a potential 75% increase in performance, all thanks to Airbyte’s superior data movement capabilities. As a result, Symend and its clients have access to more current, near real-time data for decision-making.

More insights, less maintenance

Today, Symend's engineering team is free to focus on strategic initiatives instead of managing fragile pipelines. With Airbyte, they are confident that data will flow reliably, refresh rates will meet strict SLAs, and downstream teams will always have the data they need to make timely decisions. Additionally, the platform optimizations have received strong support from Symend's leadership. 

"It's a huge win for our organization because it's basically a huge optimization opportunity for us in terms of cost and performance. That’s why there was huge support from our senior leadership and CFO to complete this project.”

Key Results

• Symend projects cost savings of approximately $500,000 annually by migrating from Azure Data Factory to Airbyte (pending validation after full implementation)
• The way Airbyte process connections in parallel can reduce data refresh latency from 2 hours to as little as 30 minutes, significantly improving SLAs and customer commitments
• Eliminated cascading pipeline failures with Airbyte's distributed architecture, enabled by parallel processing of multiple client data streams
• Enabled a self-service AI-powered chatbot via Snowflake Cortex that helps business users ask data questions in natural language without technical assistance
• Successfully completed a thorough 3-month proof of concept, with full production implementation scheduled to be complete by June 2025