GLOBAL COMMERCE. HUMAN JUDGEMENT.
See the patterns.
Make the right
decisions.
I’m Yong Shang. I turn payment data into practical fraud controls, clear decisions, and better customer experiences.
Open to leadership roles & consulting conversations
PEOPLE-FIRST APPROACH.
01 — ABOUT
Behind every payment,
there’s a person.
Good risk decisions protect both the business and the people it serves.
Across 15+ years in customer operations and fraud risk, I’ve worked across merchant, acquirer, issuer, and customer environments. My focus is finding the patterns behind payment fraud, strengthening detection, and protecting legitimate transactions.
I care just as much about making those findings useful to clients, colleagues, and senior leaders. I’m at my best where analytical investigation, risk judgement, and collaboration meet.
HOW I WORK
From signal to action.
01 Investigate +
Use transaction analysis to understand patterns, segment risk, and identify what needs closer attention.
02 Design & test +
Translate findings into targeted detection rules. Test control behaviour and consider the effect on legitimate customers.
03 Monitor & refine +
Review fraud, chargeback and approval trends after deployment. Tune controls as behaviour changes.
04 Align & lead +
Turn analysis into clear recommendations, partner across teams, and coach analysts to exercise sound judgement.
02 — EXPERIENCE
Deep in the detail.
Broad in perspective.
From customer conversations to APAC risk leadership.
Read my resumeNOV 2023–AUG 2026 / VisaSenior Manager, Managed Risk Services
+
Led APAC managed-risk delivery across Southeast Asia, Greater China, and India and South Asia, overseeing strategic acquirer, merchant and issuer portfolios and four analysts across Asia Pacific.
Analysed transaction data to identify emerging fraud patterns, designed targeted monitoring controls, and translated fraud, chargeback and approval-rate performance into recommendations for senior stakeholders.
Partnered with Product, Engineering, Account Management and client teams to improve risk strategies, platform workflows, implementation outcomes and delivery quality.
JUL 2021–NOV 2023 / VisaSenior Managed Risk Analyst (Manager)
+
Managed 8–12 APAC acquirer and merchant clients, using SQL, Tableau and DBVisualizer to identify BIN attacks, card testing, account takeover and mule-network activity.
Owned the fraud-strategy lifecycle, from rule design, testing and deployment to post-implementation monitoring, monthly performance reviews and quarterly business reviews.
Translated analytical findings into practical control recommendations and partnered with cross-functional teams on service, workflow and product improvements.
JUL 2017–JUL 2021 / VisaManaged Risk Analyst
+
Advised merchants on online payment risk by analysing transaction data, identifying fraud patterns, and designing monitoring rules using velocity, geolocation, device and behavioural signals.
Supported onboarding, go-live configuration, system workshops and ongoing strategy optimisation. Produced performance reports covering rule effectiveness, return on investment and improvement opportunities.
Presented fraud insights and control recommendations to audiences ranging from operational analysts to C-suite stakeholders.
AUG 2014–JUN 2017 / APPLE SOUTH ASIAiTunes Fraud Prevention Team Lead
+
Led a fraud-review team investigating card-not-present transactions and customer accounts across JPAC, EMEIA and AMR.
Oversaw investigation quality, conducted regional accuracy reviews, coached new hires, and communicated fraud trends through regular reports and updates to global stakeholders.
Partnered with business teams on emerging threats, mitigation actions, training, performance targets and team planning.
MAY 2012–AUG 2014 / APPLE SOUTH ASIAProduct Specialist, Greater China
+
Supported Greater China customers through end-to-end issue resolution and product recommendations across chat and telephone channels.
Contributed to internal-tool testing, weekly analysis, statistical reporting, duty-agent coverage and knowledge management to strengthen operational effectiveness and team readiness.
NOV 2010–MAY 2012 / APPLE SOUTH ASIACustomer Service Representative
+
Handled customer enquiries across Singapore, Southeast Asia, Taiwan and Hong Kong, with a focus on clear communication and effective one-call resolution.
Coordinated Taiwan Stop Invoice requests across four Asia teams, supporting consistent execution across markets.
03 — SELECTED WORK
The thinking behind
the outcome.
I build frameworks and repeatable processes that keep improving after the immediate problem is solved.
04 — THE ANALYTICS LAB
See the whole picture.
Explore the detail.
Explore payment decisions, risk signals and transaction patterns across markets. Every view responds to your filters.
Payment decisions
Fraud outcomes are separate from rule hits. All amounts are illustrative USD equivalents.
Transaction distribution
Select a highlighted country or a table row to filter the dashboard.
Rule performance
Compare matched attempts, decisions and observed fraud. Select a rule for interpretation.
Card BIN country
Issuer-country distribution for the filtered transactions. Select a country to explore it.
Confirmed fraud by country
Compare confirmed fraud counts and amounts across the selected cohort.
Data definitions, methodology & monthly values
This interactive demonstration uses generated data, not client data or personal performance results. Transaction country represents the merchant-reported customer country; card BIN country represents the card issuer’s country. They may differ without indicating fraud.
Approval rate = approved attempts ÷ total attempts. Reject rate = rejected attempts ÷ total attempts; this simplified dataset contains only these two final decisions, so they sum to 100%. All approved attempts are assumed settled. Chargeback rate = chargeback count ÷ settled count for the same transaction cohort, using assumed mature outcomes. This is not a card-scheme monitoring ratio. Volume counts attempts, including potential retries, rather than unique customers or orders.
Fraud count and amount describe confirmed outcomes on approved transactions. Rejected attempts are not labelled as prevented fraud. Each matched attempt is assigned one primary rule to avoid double counting; production rule hits can overlap. Aggregated rates are calculated from summed counts, not averaged percentages. The map is illustrative and uses Natural Earth boundaries.
A sample of how I connect transaction analysis, control performance and stakeholder conversations.
05 — FRAUD TRENDS & PERSPECTIVES
A wider lens.
A sharper perspective.
Research and resources across card networks, banking and digital assets. Curated on 23 September 2026.
Scams and social engineering
How payment threats are evolving beyond stolen card credentials.
Card testing as an early warning signal
Connecting payment patterns with broader fraud and cyber risk.
Authorisation, authentication and fraud prevention
Merchant resources on payment protection and customer experience.
The changing shape of crypto scams
Research on impersonation, AI-enabled fraud and on-chain scam activity.
A bank’s view of emerging scams
Current scam alerts, warning signs and customer protection resources.
External perspectives are linked to their original publishers. This is a curated reading shelf, not a live news feed.
LET’S CONNECT
Better payments start
with a conversation.
Building a stronger risk team? Rethinking fraud controls?
I’m open to senior roles and consulting conversations.