Real-World Results from Data & AI

Challenge:

A leading digital bank needed a scalable platform to unify transaction data across products. They were struggling with siloed data systems, slow reporting cycles (2+ weeks), and difficulty meeting regulatory compliance requirements. The lack of real-time insights was hindering customer service and strategic decision-making.

Impact:

Reduced reporting time from 2 weeks to near real-time, enabling faster regulatory compliance and customer insights.

Challenge:

Singapore's top insurer wanted to speed up claims processing and detect fraud more effectively. Manual claim assessment was taking 5-7 days per claim, fraud detection was reactive rather than proactive, and customer satisfaction was suffering due to slow processing times.

Impact:

Cut processing times by 60% and improved fraud detection rates by 30%.

Challenge:

A government agency struggled with siloed legacy systems and lacked a secure way to share data across departments. Critical information was trapped in isolated systems, inter-department collaboration was difficult, and there was no unified view of citizen services. Security concerns prevented data sharing even when legally permitted.

Impact:

Enabled cross-agency collaboration and faster decision-making for public services.

Challenge:

A major retail chain with 200+ stores needed to understand customer behaviour across online and offline channels. They had fragmented customer data across e-commerce, point-of-sale systems and loyalty programs. Marketing campaigns were generic, inventory decisions were based on historical averages and they were losing market share to more data-driven competitors.

Impact:

Achieved 35% increase in customer lifetime value and 45% improvement in inventory turnover.

Challenge:

An industrial manufacturer was experiencing costly unplanned downtime and quality issues in their production line. Equipment failures were unpredictable, maintenance was reactive and expensive, quality defects were detected too late in the process, and overall equipment effectiveness (OEE) was below industry benchmarks.

Impact:

Reduced unplanned downtime by 70% and improved product quality by 40%.

Challenge:

A healthcare provider network needed to improve patient outcomes while managing costs. Patient data was fragmented across multiple systems, care coordination between facilities was difficult, operational inefficiencies were driving up costs, and they lacked predictive insights for patient risk management.

Impact:

Improved patient outcomes with 28% reduction in readmissions and 35% improvement in care coordination.