Pay-by-Face Gains Ground in South Korea

Pay-by-Face Gains Ground in South Korea
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, theyโ€™ve 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 elseโ€™s tab ๐Ÿ˜…

But the story doesnโ€™t end here. Scroll down for more details ๐Ÿ‘‡

See you tomorrow!

Cheers,

Marcel


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INSIGHTS

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NEWS

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๐Ÿ‡ฐ๐Ÿ‡ท Pay-by-face expands in Korea. Payments made solely through facial recognition, or "pay-by-face," are being adopted rapidly as financial companies compete to gain an early foothold in next-generation financial services. While the initiative was bold, the requirement to register one's face in person at banks proved inconvenient.

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GOLDEN NUGGET

๐Ÿšจ ๐‡๐จ๐ฐ ๐ญ๐จ ๐”๐ง๐ฅ๐จ๐œ๐ค ๐•๐š๐ฅ๐ฎ๐ž ๐Ÿ๐ซ๐จ๐ฆ ๐๐š๐ฒ๐ฆ๐ž๐ง๐ญ๐ฌ ๐ƒ๐š๐ญ๐š โ€” ๐›๐ฒ DEUNA ๐Ÿ‘‡ Created by Arthur Bedel ๐Ÿ’ณ โ™ป๏ธ

How to Unlock Value From Payments Data

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.

๐”๐ง๐๐ž๐ซ๐ฌ๐ญ๐š๐ง๐๐ข๐ง๐  ๐๐š๐ฒ๐ฆ๐ž๐ง๐ญ๐ฌ ๐ƒ๐š๐ญ๐š

Todayโ€™s 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Ÿ‘ ๐’๐ฒ๐ฌ๐ญ๐ž๐ฆ๐ข๐œ ๐๐ฅ๐จ๐œ๐ค๐ž๐ซ๐ฌ ๐“๐จ ๐•๐š๐ฅ๐ฎ๐ž ๐‘๐ž๐š๐ฅ๐ข๐ณ๐š๐ญ๐ข๐จ๐ง

1๏ธโƒฃ 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๏ธโƒฃ 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๏ธโƒฃ Constant Firefighting, Minimal Strategy

โ†’ Operational teams are stuck responding to incidents instead of driving outcomes

๐‡๐จ๐ฐ ๐‹๐ž๐š๐๐ข๐ง๐  ๐“๐ž๐š๐ฆ๐ฌ ๐”๐ง๐ฅ๐จ๐œ๐ค ๐•๐š๐ฅ๐ฎ๐ž

The process to transform raw payments data into actionable intelligence:

1๏ธโƒฃ Data Ingestion

โ†’ Consolidating data streams from PSPs, checkout systems, CRMs, fraud tools, and commerce platforms into a central layer

2๏ธโƒฃ Variable Definition & Structuring

โ†’ Translating raw fields into business metrics (e.g., GMV, approval rate, ARPU)

3๏ธโƒฃ Real-Time Monitoring & Alerts

โ†’ Establishing live observability for key indicators (e.g., spikes in declines, fraud signals, latency issues)

4๏ธโƒฃ Cleaning, Deduplication & Standardization

โ†’ Removing duplicates, normalizing formats (e.g., date, currency, identifiers)

5๏ธโƒฃ Industry-Specific Contextualization

โ†’ Applying domain knowledge (e.g., travel refunds, retail loyalty...) to reclassify and enrich data

6๏ธโƒฃ 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๏ธโƒฃ AI Implementation

โ†’ Using ML and intelligent agents to detect anomalies, identify high-impact opportunities, and automate routing, retries... in real time.

๐‚๐จ๐ง๐œ๐ฅ๐ฎ๐ฌ๐ข๐จ๐ง: ๐Ÿ‘ ๐๐ซ๐ข๐ง๐œ๐ข๐ฉ๐ฅ๐ž๐ฌ ๐ƒ๐ž๐Ÿ๐ข๐ง๐ข๐ง๐  ๐“๐ก๐ž ๐๐ž๐ฐ ๐’๐ญ๐š๐ง๐๐š๐ซ๐

1๏ธโƒฃ Unified Data Foundation

2๏ธโƒฃ Context-Aware Opportunity Detection

3๏ธโƒฃ Real-Time Execution on Intelligence

โ†’ Automate retry logic, fraud triggers, routing adjustments, and more

Source: DEUNA


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