Pay-by-Face Gains Ground in South Korea

Hey Payments Fanatic!
What started as a small pilot in a company cafeteria in 2019 is now spreading across South Korea. Facial recognition is moving beyond phones and airports, showing up in stores, gas stations, and on university campuses.
Naver Pay and Toss have added new momentum to facial payments. With in-app face registration and their own terminals, theyโve made pay-by-face smoother for both users and merchants.
Still, even with 160,000 merchants on board, facial recognition is touchy stuff. In Korea, better watch where you look... blink the wrong way and your lunch might end up on someone elseโs tab ๐
But the story doesnโt end here. Scroll down for more details ๐
See you tomorrow!
Cheers,
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INSIGHTS
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NEWS
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GOLDEN NUGGET
๐จ ๐๐จ๐ฐ ๐ญ๐จ ๐๐ง๐ฅ๐จ๐๐ค ๐๐๐ฅ๐ฎ๐ ๐๐ซ๐จ๐ฆ ๐๐๐ฒ๐ฆ๐๐ง๐ญ๐ฌ ๐๐๐ญ๐ โ ๐๐ฒ DEUNA ๐ Created by Arthur Bedel ๐ณ โป๏ธ

As commerce evolves into an intelligent, agent-driven ecosystem, payments data is emerging as a critical strategic asset โ but one that remains vastly underutilized.
This post outlines the key limitations holding teams back โ and how leading merchants are transforming data into a competitive advantage.
๐๐ง๐๐๐ซ๐ฌ๐ญ๐๐ง๐๐ข๐ง๐ ๐๐๐ฒ๐ฆ๐๐ง๐ญ๐ฌ ๐๐๐ญ๐
Todayโs commerce stack generates rich and interconnected data from multiple systems:
โ Payments Data: transaction metadata, approval rates, methods, issuer response
โ Orders Data: cart value, SKUs, timestamps, discounts
โ Identity Data: device fingerprinting, customer behavior, fraud signals
โ Other Sources: CRM, logistics, loyalty, geography, and more
๐ ๐๐ฒ๐ฌ๐ญ๐๐ฆ๐ข๐ ๐๐ฅ๐จ๐๐ค๐๐ซ๐ฌ ๐๐จ ๐๐๐ฅ๐ฎ๐ ๐๐๐๐ฅ๐ข๐ณ๐๐ญ๐ข๐จ๐ง
1๏ธโฃ Fragmented & Inconsistent Data
โ 80% of data teamsโ time is spent cleaning instead of analyzing
โ Siloed PSPs, fraud tools, and BI platforms prevent unified decision-making
2๏ธโฃ Context-less AI doesn't deliver
โ 90% of AI projects fail to operationalize
โ Without business context, generic AI outputs remain disconnected from strategic goals
3๏ธโฃ Constant Firefighting, Minimal Strategy
โ Operational teams are stuck responding to incidents instead of driving outcomes
๐๐จ๐ฐ ๐๐๐๐๐ข๐ง๐ ๐๐๐๐ฆ๐ฌ ๐๐ง๐ฅ๐จ๐๐ค ๐๐๐ฅ๐ฎ๐
The process to transform raw payments data into actionable intelligence:
1๏ธโฃ Data Ingestion
โ Consolidating data streams from PSPs, checkout systems, CRMs, fraud tools, and commerce platforms into a central layer
2๏ธโฃ Variable Definition & Structuring
โ Translating raw fields into business metrics (e.g., GMV, approval rate, ARPU)
3๏ธโฃ Real-Time Monitoring & Alerts
โ Establishing live observability for key indicators (e.g., spikes in declines, fraud signals, latency issues)
4๏ธโฃ Cleaning, Deduplication & Standardization
โ Removing duplicates, normalizing formats (e.g., date, currency, identifiers)
5๏ธโฃ Industry-Specific Contextualization
โ Applying domain knowledge (e.g., travel refunds, retail loyalty...) to reclassify and enrich data
6๏ธโฃ Addition of Exogenous Variables
โ Integrating external factors such as FX volatility, holidays, weather, or marketing campaigns to reveal hidden correlations and refine models.
7๏ธโฃ AI Implementation
โ Using ML and intelligent agents to detect anomalies, identify high-impact opportunities, and automate routing, retries... in real time.
๐๐จ๐ง๐๐ฅ๐ฎ๐ฌ๐ข๐จ๐ง: ๐ ๐๐ซ๐ข๐ง๐๐ข๐ฉ๐ฅ๐๐ฌ ๐๐๐๐ข๐ง๐ข๐ง๐ ๐๐ก๐ ๐๐๐ฐ ๐๐ญ๐๐ง๐๐๐ซ๐
1๏ธโฃ Unified Data Foundation
2๏ธโฃ Context-Aware Opportunity Detection
3๏ธโฃ Real-Time Execution on Intelligence
โ Automate retry logic, fraud triggers, routing adjustments, and more
Source: DEUNA
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