How BlueCloud Transformed Agent Support with ML-Driven Self-Service for a Global Financial Organization

Overview

Industry: Financial Services

• Leveraged Snowflake to automate and streamline regulatory, payment service, and customer report generation.

• Migrated data to Snowpark boosting MLOps scalability, speed, and performance and streamlining deployment and monitoring for a future-ready ML infrastructure.

• Designed data models and pipelines to transform unstructured data and seamlessly load it into Snowflake tables, supporting key business needs.

A global financial organization recognized the need to automate the generation and distribution of critical Excel-based reports to enhance operational efficiency and enable self-service for their agents across the globe.  

The existing manual processes were time-consuming, prone to errors, and required significant effort, creating inefficiencies and potential compliance risks.  

The customer also faced challenges handling event data like payments, sends, attempts, refunds, and cancellations. The existing system relied on batch processing, updating every four hours with limited event coverage and attributes, making real-time insights and decision-making challenging.

They needed a partner who could help them migrate, modernize, and automate the generation and distribution of critical regulatory, payment service, and customer account statement reports to enhance operational efficiency and provide self-service capabilities to their agents globally.  

To enable seamless global data processing and self-service for the customer’s agents worldwide, we optimized ML data pipeline with advanced Snowflake features such as Snowpark and implemented a real-time data warehouse.

Automating Report Generation: We helped the client modernize and automate the generation and distribution of regulatory, payment service, and customer statement reports using Snowflake objects (tables, views, tasks, stored procedures).

Real-Time Data Integration: To eliminate manual file-based processes and improve overall operational efficiency, the BlueCloud team streamlined data flow by directly streaming transactional data into Snowflake via Kafka Connect, enabling real-time data processing and updates.

Scalable Data Tables with Snowflake and AWS: The BlueCloud team built scalable tables on Snowflake and AWS. This solution efficiently structured the data, providing a secure and reliable environment for storing and processing critical transaction data.

Data Modeling and Pipeline Development: To load data into Snowflake tables, the BlueCloud team designed and implemented logical and physical data models, as well as efficient data pipelines. We transformed unstructured data into a structured format, ensuring seamless population of physical data tables to support business needs.

MLOps Support and Snowpark Migration: By migrating the ML pipeline to Snowpark, BlueCloud significantly enhanced its MLOps environment—boosting scalability, speed, and performance. This transformation streamlined model deployment and monitoring, resulting in a high-performing, future-ready infrastructure for machine learning operations.

Real-Time Data Warehouse with Kafka: The BlueCloud team built a real-time data warehouse to efficiently process event data. By leveraging Kafka for data streaming, event data was seamlessly consumed and loaded into Snowflake staging, detail, standard, and event tables in real time.

Technologies Used

Through these efforts the client can now process their transactional data worldwide seamlessly with improved performance and provide self-service capabilities to their agents globally.

• Successfully migrated and automated Excel-based reports for timely and accurate distribution.

• Delivered an efficient, automated reporting solution, reducing manual effort and improving operational performance.

• Enabled real-time data processing with near-instantaneous tasks (within 15 minutes)

• Achieved a 60% reduction in time consumption and operational costs

• Enabled self-service capabilities for the customer’s agents, reducing dependency on IT support

• Successfully maintained a positive view of the current MLOps environment

• Established a more robust, future-ready environment for machine learning operations

• Achieved superior results and significantly boosted stakeholder satisfaction

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