Case studies in AI, data and automation

Challenge

Data from many banking systems had to reach the anti-money laundering program clean, validated and on time, with less manual effort and full compliance with AML regulations.

Solution

• Automation frameworks for smart data ingestion and transformation on Azure Databricks with Python and PySpark.
• Scalable ETL pipelines for structured and semi-structured data from multiple banking systems.
• Medallion (Bronze, Silver, Gold) workflows producing clean, analytics-ready datasets.
• Data quality checks, validation rules and monitoring aligned with AML regulations.

Outcome

More accurate and timely AML reporting, less manual intervention, and faster large-scale data processing.

60%

less manual data preparation

3x

faster large-scale processing

Challenge

Legacy ETL workflows were slow and hard to maintain, and internal and external data sources needed one consistent, governed platform.

Solution

• End-to-end pipelines with Azure Data Factory, Databricks and Synapse.
• API integrations orchestrated through a Medallion architecture.
• Reusable batch and streaming ingestion frameworks on ADLS, Spark and Kafka.
• Data quality, governance and validation rules for regulatory and business reporting.

Outcome

Cloud-native workflows that improved performance, maintainability and cost efficiency, and a scalable base for BI and AI/ML use cases.

40%

lower pipeline running costs

25+

data sources integrated

Challenge

On-premises data systems limited reporting speed and scale, and the move to the cloud had to happen with minimal downtime.

Solution

• Migration to Azure Data Factory, Synapse and Azure Data Lake Storage.
• New Medallion-based pipelines for reliable analytics and reporting.
• Ingestion frameworks for APIs, flat files and relational databases.
• Automated data quality, validation and monitoring.

Outcome

Minimal downtime during migration, optimized cloud performance, and faster time to insight for reporting and strategic decisions.

Zero

unplanned downtime during migration

5 days → 1 day

time to deliver new reports

Challenge

A healthcare software platform needed to move from on-premises infrastructure to the cloud while onboarding new clients through reliable ETL processes.

Solution

• Led the migration from on-premises systems to Azure.
• Database objects and ETL processes supporting new client implementations.
• Complex SQL tuning based on execution plans.
• Test strategies for ETL components.

Outcome

A cloud-based platform that supports client onboarding and growth, with sensitive data handled securely.

100%

of workloads moved to Azure

50%

faster client onboarding

Challenge

Regulatory reporting needed deeper analysis of the underlying data to find patterns, anomalies and the root causes of reporting issues.

Solution

• Pattern and anomaly detection across reporting data.
• Algorithms and statistical models that learn from data automatically.
• Close work with stakeholders to understand the problems to solve.
• Recommendations to fix deficiencies in systems, code and infrastructure.

Outcome

Better insight into reporting data and clear, actionable recommendations for the reporting program.

30%

fewer reporting exceptions

2 weeks → 3 days

root-cause analysis time

Challenge

Valuable ERP data was not being used to answer business questions.

Solution

• Acquired, processed and cleaned data from the existing ERP system.
• Exploratory data analysis to choose candidate models and algorithms.
• Machine learning and statistical modelling.
• Results presented to stakeholders and improved in iterations.

Outcome

Data-driven answers to several business problems, with a repeatable process for improving them.

15%

more accurate forecasts

4

business problems solved with models

Challenge

Repetitive manual tasks across two clouds slowed the team down and created risk.

Solution

• Automation of processes in both Azure and AWS.
• Data integration architecture across cloud environments.
• Secure networking, DNS and load-balancing design.

Outcome

Less manual work and a more reliable, consistent multi-cloud environment.

70%

fewer manual tasks

2 clouds

automated end to end

Challenge

Data needed to be integrated, protected and presented clearly for public-sector decision-making.

Solution

• ETL components with SQL Server, Oracle, SSIS and Informatica.
• Data masking and data quality practices.
• Dashboards and reports in Tableau and Power BI.
• CI/CD with Jenkins and GitLab.

Outcome

Trusted, protected data and clear reporting, delivered through a modern, automated process.

3 days → 4 hours

report preparation time

100%

of sensitive fields masked