What challenges did Snowdrop Solutions Ltd. face before AI implementation?

Merchant names in bank and transaction data often differ from the names customers recognize, making reconciliation and enrichment difficult at scale. Snowdrop needed to expand into new regions and onboard high-volume clients without long manual research and ingestion cycles. The company also needed a platform that could scale reliably across geographies while improving match accuracy.

What was the impact of the AI solution?

Snowdrop reported a 40% improvement in data accuracy. Manual regional onboarding dropped from 90 days to a couple of weeks. Merchant-to-transaction match rate increased by up to 15%. Transaction processing scaled from 198 million per month to more than 2.1 billion per month. The company says it now has 100,500x more merchants in its system than before migration.

Source

GCPScaled productionEvidence: Medium65/100

Snowdrop: AI-assisted merchant reconciliation using Vertex AI + BigQuery

Updated Jun 13, 2026

Snowdrop Solutions Ltd. is a financial services transaction enrichment company that helps banks and payment providers clarify merchant information in transaction data where trade names vary by region and business. The company migrated its merchant reconciliation platform to Google Cloud to improve scalability, deploy faster in new regions, and support more accurate end-customer merchant information at larger transaction volumes.

Organization: Snowdrop Solutions Ltd.
Industry: Finance
Location: United Kingdom
Published: May 2026

Reported outcomes

+40%

accuracyQuality & accuracy

90 daysquantified impact+15%quantified impact100.5xquantified impact

Strategic outcomes

Customer experience & trustImproved merchant match accuracyMarket & geographic expansionExpanded into new regions fasterSpeed & agilityAccelerated regional onboardingScale & capacityScaled transaction processing capacity

Catalog median for quality & accuracy deployments: +41% across 63 reported metrics. Compare benchmarks →

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

Normalized claim

Accuracy: 40% increase

Google Cloud Customer StoriesMay 13, 2026Customer storyInferred claimMedium evidence strength

Snowdrop reported a 40% improvement in data accuracy.

Normalized claim

Quantified impact: 90 days decrease

Google Cloud Customer StoriesMay 13, 2026Customer storyInferred claimMedium evidence strength

Manual regional onboarding dropped from 90 days to a couple of weeks.

Normalized claim

Quantified impact: 15% increase

Google Cloud Customer StoriesMay 13, 2026Customer storyInferred claimMedium evidence strength

Merchant-to-transaction match rate increased by up to 15%.

Normalized claim

Quantified impact: 100.5 x increase

Google Cloud Customer StoriesMay 13, 2026Customer storyInferred claimMedium evidence strength

The company says it now has 100,500x more merchants in its system than before migration.

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

Customer: Snowdrop Solutions Ltd.
Provider: GCP
Maturity: Scaled Production
Linked source: Google Cloud Customer Stories

Merchant names in bank and transaction data often differ from the names customers recognize, making reconciliation and enrichment difficult at scale

Customer identity supportedSource describes one deploymentMaturity supported

Primary read

Use case focus

Showing 3 of 4

1Merchant reconciliation
2Data enrichment
3Transaction processing automation

Merchant names in bank and transaction data often differ from the names customers recognize, making reconciliation and enrichment difficult at scale.
Snowdrop needed to expand into new regions and onboard high-volume clients without long manual research and ingestion cycles.
The company also needed a platform that could scale reliably across geographies while improving match accuracy.

Snowdrop uses Google Places API and Google Maps Platform to gather localized merchant information and combines it with manually scraped website data for enrichment.
It stores unstructured data in Cloud Storage and uses BigQuery as its data warehouse for scalable processing across regions.
Vertex AI is integrated with Snowdrop's Merchant Reconciliation System (MRS) API to automate background research, add validation checkpoints, and improve merchant match accuracy.
Google Kubernetes Engine and Cloud Service Mesh support scalable, lower-burden deployment and operations across regions.
The article says Snowdrop is also planning to use Gemini for Google Cloud and AlloyDB for additional language handling, embeddings, and data processing capabilities.

Technologies

Vertex AI BigQuery Google Places API Cloud Storage Cloud Service Mesh Google Kubernetes Engine Gemini for Google Cloud Google Maps Platform

Snowdrop reported a 40% improvement in data accuracy.
Manual regional onboarding dropped from 90 days to a couple of weeks.
Merchant-to-transaction match rate increased by up to 15%.
Transaction processing scaled from 198 million per month to more than 2.1 billion per month.
The company says it now has 100,500x more merchants in its system than before migration.

Architecture

Snowdrop's MRS API runs on Google Cloud with Cloud Storage for unstructured data, BigQuery for regional warehouse scaling, Vertex AI for AI-assisted merchant validation and background research, Google Places API/Google Maps Platform for localized merchant enrichment, and Google Kubernetes Engine plus Cloud Service Mesh for deployment and service management.

Sources & evidence1

Evidence: Medium65/100Evidence strength