Global Financial Firm
Modernization and seamless integration of data and AI operations in less than 3 months
Our client, a leading financial services corporation operating under strict confidentiality, processes millions of transactions daily across their credit risk assessment division. With operations spanning multiple continents and a workforce of over 10,000 professionals, they required a complete overhaul of their analytics infrastructure. Hiflylabs engineered a seamless migration to a unified data platform, establishing a foundation for their next-generation risk analytics capabilities.
50%
cost reduction in operational overhead
Integrated
AI, ML, and data platform for future-readiness
Challenge
The client's risk assessment operations relied on a complex web of systems. Their existing infrastructure, built over a decade, combined Amazon SageMaker for machine learning, Snowflake for data warehousing, and Neo4j for entity resolution. This fragmented approach created significant operational overhead, obstructed provisioning, and hindered their ability to implement modern AI-driven risk assessment models. With transaction volumes growing exponentially, they needed a solution that could scale efficiently while maintaining strict compliance with regulations. The main challenge was to migrate this intricate ecosystem without disrupting daily operations that process billions in transactions.
Solution
Hiflylabs orchestrated a comprehensive platform migration to Databricks’ ecosystem, employing our smart-migration methodology. Our solution unified all of the client’s analytics operations under a single platform while enhancing their data processing capabilities: - Migrated critical Snowflake data pipelines using our AI-assisted dbt translation tool, ensuring zero data loss and maintaining business continuity. - Transformed Streamlit applications into native Databricks dashboards, providing enhanced visualizations for risk metrics. - Replaced Neo4j's entity resolution system with an optimized NetworkX implementation, maintaining the core ML feature generation functionality. This approach surpassed the processing speed of the previous setup, while eliminating hefty licensing costs. - Established automated testing frameworks to validate data consistency and performance across systems. The new platform significantly improved their ability to handle complex risk assessments while reducing operational costs and complexity. By consolidating their tech stack, we created a foundation that supports their future AI and machine learning initiatives without requiring additional infrastructure investments.
Service
Data
Industries
Financial Services
Technologies
Python
MLFlow
NetworkX
Amazon Sagemaker
Snowflake
Apache Airflow
Databricks
OpenAI
SQL
Streamlit
dbt
Technologies
Python
MLFlow
NetworkX
Amazon Sagemaker
Snowflake
Apache Airflow
Databricks
OpenAI
SQL
Streamlit
dbt
-
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