What challenges did Finture face before AI implementation?

Finture needed to improve data management and security while supporting scalable, high-performance analytics and log processing for its credit-card and QRIS payment services. The company also wanted to maintain competitiveness as transaction activity and retention increased.

What was the impact of the AI solution?

The implementation lowered infrastructure and storage costs by more than 30%. It improved system uptime for Yup services through a high-availability design. It enabled faster real-time data analytics for operational decision-making and improved banking experiences.

Rank#1,502of 3,449

Source

AlibabaProductionEvidence: Medium65/100

Finture (Indonesia) data management and security modernization with Alibaba Cloud

Use case typeData platform modernizationUpdated 5 days ago

Finture, a leading neobank in Southeast Asia, modernized its data management and security environment to support rapid growth in Indonesia. The company serves credit card and QRIS payment products and needed scalable, high-performance analytics and log processing for operational decision-making.

Organization: Finture
Industry: Finance
Location: Indonesia
Published: July 2026

Reported outcomes

−30%

infrastructure and storage costsCost savings

Strategic outcomes

Cost efficiencyImproved system uptimeBetter decisions & insightEnabled faster real-time analytics for operations

Catalog median for cost savings deployments: −40% across 177 reported metrics. Compare benchmarks →

Why do we believe this?Outcome claims, sources, and evidence checks

Normalized claim

Infrastructure and storage costs: 30% decrease

Alibaba Cloud CustomersJul 23, 2026Customer storyExplicit claimMedium evidence strength

Lowered infrastructure and storage costs by >30%

Why do we believe this deployment?Customer identity, provider attribution, maturity, and source checks

Customer: Finture
Provider: Alibaba
Maturity: Production
Linked source: Alibaba Cloud Customers

The company serves credit card and QRIS payment products and needed scalable, high-performance analytics and log processing for operational decision-making

Customer identity supportedSource describes one deploymentMaturity supported

Primary read

Use case focus

Showing 3 of 3

1Data platform modernization
2Real-time analytics
3Operational analytics

Finture needed to improve data management and security while supporting scalable, high-performance analytics and log processing for its credit-card and QRIS payment services.
The company also wanted to maintain competitiveness as transaction activity and retention increased.

Finture deployed a real-time data warehousing architecture covering batch and streaming scenarios.
It used Alibaba Cloud DataWorks to streamline ETL and automate data pipelines, and used MaxCompute and Hologres for scalable batch and near-real-time warehousing and analytics.
Alibaba Cloud Elasticsearch was used for efficient log processing, real-time analytics, troubleshooting, and search.
Alibaba Cloud collaborated with Finture's DevOps team on architecture and security improvements.

Technologies

Alibaba Cloud DataWorks MaxCompute Hologres Alibaba Cloud Elasticsearch

The implementation lowered infrastructure and storage costs by more than 30%.
It improved system uptime for Yup services through a high-availability design.
It enabled faster real-time data analytics for operational decision-making and improved banking experiences.

Architecture

Real-time data warehousing and analytics architecture spanning batch and streaming scenarios. DataWorks was used for ETL orchestration and data integration, MaxCompute and Hologres for scalable batch and near-real-time warehousing/analytics, and Alibaba Cloud Elasticsearch for log processing and real-time analytics. Alibaba Cloud and Finture DevOps collaborated on architecture and security improvements.

Sources & evidence1

Evidence: Medium65/100Evidence strength