āļ›āļĢāļ°āļāļēāļĻāļ‡āļēāļ™āļ™āļĩāđ‰āļŦāļĄāļ”āļ­āļēāļĒāļļāđāļĨāđ‰āļ§

We are seeking a detail-oriented and analytical Fraud Management Specialist to join our growing team. In this critical role, you will play a vital role in protecting our customers and ensuring the security of our digital payment solutions by identifying, investigating, and preventing fraudulent activity. You will be responsible for analyzing transactions, investigating suspicious activity, and taking appropriate action to mitigate risk. This is a fast-paced and dynamic environment, requiring a proactive and results-oriented individual with a keen eye for detail and a passion for security.

Responsibilities:

  • Analyze transactions in real-time using fraud detection tools and rules.
  • Identify suspicious activity based on pre-defined risk profiles and behavioral patterns.
  • Investigate flagged transactions and determine their legitimacy.
  • Escalate high-risk cases to the Fraud Management team for further investigation.
  • Gather and analyze evidence related to suspected fraudulent activity.
  • Conduct research to identify potential fraud schemes and perpetrators.
  • Document findings and recommend appropriate actions, such as blocking accounts, recovering funds, or reporting to law enforcement.
  • Collaborate with internal teams (customer support, risk management) to resolve cases effectively and efficiently.
  • Analyze fraud trends and patterns to identify emerging threats and adjust detection rules accordingly.
  • Generate reports on fraud activity, providing insights to the Fraud Management team and senior management.
  • Track and measure the effectiveness of fraud prevention and detection measures.
  • Stay up-to-date on the latest fraud threats, trends, and best practices.
  • Participate in ongoing training and development opportunities to enhance your skills and knowledge.

 

Qualifications:

  • Minimum of 2-3 years of experience in fraud analysis or a related field.
  • Strong analytical and problem-solving skills.
  • Excellent attention to detail and ability to identify anomalies in data.
  • Proficient in Microsoft Office Suite ,SQL language and data analysis tools.
  • Understanding of fraud detection and prevention techniques preferred.
  • Effective communication and interpersonal skills.
  • Ability to work independently and as part of a team.
  • Bachelor's degree in business administration, finance, IT, engineering, or a related field preferred.
āļ›āļĢāļ°āļŠāļšāļāļēāļĢāļ“āđŒāļ—āļĩāđˆāļˆāļģāđ€āļ›āđ‡āļ™
  • 2 āļ›āļĩ
āļĢāļ°āļ”āļąāļšāļ•āļģāđāļŦāļ™āđˆāļ‡āļ‡āļēāļ™
  • āļĢāļ°āļ”āļąāļšāļŦāļąāļ§āļŦāļ™āđ‰āļēāļ‡āļēāļ™
āđ€āļ‡āļīāļ™āđ€āļ”āļ·āļ­āļ™
  • āļŠāļēāļĄāļēāļĢāļ–āļ•āđˆāļ­āļĢāļ­āļ‡āđ„āļ”āđ‰
āļŠāļēāļĒāļ‡āļēāļ™
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āļ›āļĢāļ°āđ€āļ āļ—āļ‡āļēāļ™
  • āļ‡āļēāļ™āļ›āļĢāļ°āļˆāļģ

āđ€āļāļĩāđˆāļĒāļ§āļāļąāļšāļšāļĢāļīāļĐāļąāļ—

āļˆāļģāļ™āļ§āļ™āļžāļ™āļąāļāļ‡āļēāļ™:2000-5000 āļ„āļ™
āļ›āļĢāļ°āđ€āļ āļ—āļšāļĢāļīāļĐāļąāļ—:āļāļēāļĢāļŠāļ·āđˆāļ­āļŠāļēāļĢ / āđ‚āļ—āļĢāļ„āļĄāļ™āļēāļ„āļĄ
āļ—āļĩāđˆāļ•āļąāđ‰āļ‡āļšāļĢāļīāļĐāļąāļ—:āļāļĢāļļāļ‡āđ€āļ—āļž
āđ€āļ§āđ‡āļšāđ„āļ‹āļ•āđŒ:www.ais.co.th
āļāđˆāļ­āļ•āļąāđ‰āļ‡āđ€āļĄāļ·āđˆāļ­āļ›āļĩ:1986
āļ„āļ°āđāļ™āļ™:4.5/5

āļšāļĢāļīāļĐāļąāļ— āđāļ­āļ”āļ§āļēāļ™āļ‹āđŒ āļ­āļīāļ™āđ‚āļŸāļĢāđŒ āđ€āļ‹āļ­āļĢāđŒāļ§āļīāļŠ āļˆāļģāļāļąāļ” (āļĄāļŦāļēāļŠāļ™) āļŦāļĢāļ·āļ­ āđ€āļ­āđ„āļ­āđ€āļ­āļŠ (AIS) āđ€āļ›āđ‡āļ™āļœāļđāđ‰āļ™āļģāđƒāļ™āļ­āļļāļ•āļŠāļēāļŦāļāļĢāļĢāļĄāđ‚āļ—āļĢāļ„āļĄāļ™āļēāļ„āļĄāđ€āļ„āļĨāļ·āđˆāļ­āļ™āļ—āļĩāđˆ āđ‚āļ”āļĒāļ›āļĢāļ°āļāļ­āļšāļ˜āļļāļĢāļāļīāļˆāļŦāļĨāļąāļ āđ„āļ”āđ‰āđāļāđˆ āļšāļĢāļīāļāļēāļĢāđ‚āļ—āļĢāļĻāļąāļžāļ—āđŒāđ€āļ„āļĨāļ·āđˆāļ­āļ™āļ—āļĩāđˆāđƒāļ™āļ›āļĢāļ°āđ€āļ—āļĻ āļšāļĢāļīāļāļēāļĢāđ‚āļ—āļĢāļ­āļ­āļāļĢāļ°āļŦāļ§āđˆāļēāļ‡āļ›āļĢāļ°āđ€āļ—āļĻ āđāļĨāļ° āļšāļĢāļīāļāļēāļĢāđ‚āļĢāļĄāļĄāļīāđˆāļ‡āļ•āđˆāļēāļ‡āļ›āļĢāļ°āđ€āļ—āļĻ āđ‚āļ”āļĒāđƒāļ™āļ›āļĩ 2556 AIS āđ„āļ”āđ‰āđƒāļŦāđ‰āļšāļĢāļīāļāļēāļĢāļĨāļđāļāļ„āđ‰āļēāļāļ§āđˆāļē 41 āļĨāđ‰āļēāļ™āđ€āļĨāļ‚āļŦāļĄāļēāļĒāđāļĨāļ°āļĄāļĩ ...

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āļ—āļĩāđˆ WorkVenture āđ€āļĢāļēāđƒāļŦāđ‰āļĄāļđāļĨāđ€āļŠāļīāļ‡āđ€āļāļĩāđˆāļĒāļ§āļāļąāļšāļšāļĢāļīāļĐāļąāļ— āļšāļĢāļīāļĐāļąāļ— āđāļ­āļ”āļ§āļēāļ™āļ‹āđŒ āļ­āļīāļ™āđ‚āļŸāļĢāđŒ āđ€āļ‹āļ­āļĢāđŒāļ§āļīāļŠ āļˆāļģāļāļąāļ” (āļĄāļŦāļēāļŠāļ™) āđ‚āļ”āļĒāļĄāļĩāļ‚āđ‰āļ­āļĄāļđāļĨāļ—āļĩāđˆāđ€āļāļĩāđˆāļĒāļ§āļ‚āđ‰āļ­āļ‡ āļ•āļąāđ‰āļ‡āđāļ•āđˆāļ āļēāļžāļšāļĢāļĢāļĒāļēāļāļēāļĻāļāļēāļĢāļ—āļģāļ‡āļēāļ™ āļĢāļđāļ›āļ–āđˆāļēāļĒāļ‚āļ­āļ‡āļ—āļĩāļĄāļ‡āļēāļ™ āđ„āļ›āļˆāļ™āļ–āļķāļ‡āļĢāļĩāļ§āļīāļ§āđ€āļŠāļīāļ‡āļĨāļķāļāļ‚āļ­āļ‡āļāļēāļĢāļ—āļģāļ‡āļēāļ™āļ—āļĩāđˆāļ™āļąāđˆāļ™ āļ‹āļķāđˆāļ‡āļ‚āđ‰āļ­āļĄāļđāļĨāļ—āļļāļāļ­āļĒāđˆāļēāļ‡āļšāļ™āļŦāļ™āđ‰āļēāļ‚āļ­āļ‡āļšāļĢāļīāļĐāļąāļ— āļšāļĢāļīāļĐāļąāļ— āđāļ­āļ”āļ§āļēāļ™āļ‹āđŒ āļ­āļīāļ™āđ‚āļŸāļĢāđŒ āđ€āļ‹āļ­āļĢāđŒāļ§āļīāļŠ āļˆāļģāļāļąāļ” (āļĄāļŦāļēāļŠāļ™) āļĄāļĩāļžāļ™āļąāļāļ‡āļēāļ™āļ—āļĩāđˆāļāļģāļĨāļąāļ‡āļ—āļģāļ‡āļēāļ™āļ—āļĩāđˆāļšāļĢāļīāļĐāļąāļ— āļšāļĢāļīāļĐāļąāļ— āđāļ­āļ”āļ§āļēāļ™āļ‹āđŒ āļ­āļīāļ™āđ‚āļŸāļĢāđŒ āđ€āļ‹āļ­āļĢāđŒāļ§āļīāļŠ āļˆāļģāļāļąāļ” (āļĄāļŦāļēāļŠāļ™) āļŦāļĢāļ·āļ­āđ€āļ„āļĒāļ—āļģāļ‡āļēāļ™āļ—āļĩāđˆāļ™āļąāđˆāļ™āļˆāļĢāļīāļ‡āđ† āđ€āļ›āđ‡āļ™āļ„āļ™āđƒāļŦāđ‰āļ‚āđ‰āļ­āļĄāļđāļĨāļˆāļĢāļīāļ‡āļŠāļĄāļąāļ„āļĢāļ‡āļēāļ™ āļžāļąāļ—āļĒāļēāļŸāļđāđ‰āļ”āļ­āļīāļ™āļ”āļąāļŠāļ•āļĢāļĩāļŠāļĄāļąāļ„āļĢāļ‡āļēāļ™ āļ—āļĩāļ”āļąāļšāļšāļīāļ§āļŠāļĄāļąāļ„āļĢāļ‡āļēāļ™ āļ­āļīāļ™āļˆāļĩāđ€āļ™āļĩāļĒāļŠ āļ‹āļīāļĄāļĄāļđāđ€āļĨāļŠāļąāđˆāļ™āļŠāļĄāļąāļ„āļĢāļ‡āļēāļ™ WV