Thursday, 6 June 2019

Fraud Analyst at KCB

Reporting to the Senior Manager, Prevention & Detection the position will be reviewing transactions alerts and collate data from the real-time Fraud Management System and other reporting sources with a view to identify fraudulent transactions, institute prompt mitigation actions and create awareness on fraud trends across the Bank network.

Responsibilities

  • Constantly monitor real time queues and identify high-risk transactions, unauthorized transactions and fraudulent accounts.
  • Identify fraudulent transactions and institute prompt mitigating actions.
  • Review and provide resolution to queued transactions within defined SLAs to reduce potential revenue losses.
  • Review daily reports & compromise alerts to track and prevent fraud.
  • Recommend for appropriate holds on accounts identified as having potentially fraudulent issues.
  • Undertake research on fraud patterns by tracking confirmed fraudulent transactions to develop rules to prevent and detect fraud.
  • Contact Customers & Banks to verify transactions and resolve fraud-related account issues, if fraud confirmed, initiate investigations and dispute process through the Case Management system.
  • Facilitate understanding of fraud prevention and detection across the Bank network.
  • Track status of resolved items to provide a record of activities and support identification of trends or persistent issues.
  • Recommend changes to Fraud detection system parameters optimizing fraud detection while minimizing negative customer impact.

Qualifications

For the above position, the successful applicant should have the following:

  • University Degree in Computer Science / Finance / Business/ Banking / Law / Criminal Justice studies or any relevant field from Institution recognised by Commission of Higher Education
  • At least 4 years’ experience in Banking Operations; 3 years’ experience in Fraud Prevention and Detection
  • At least 3 years’ Analysis experience using Analytics techniques in Linear and Logistic Regression, Clustering Techniques, Neural Network, Decision Trees, etc.
  • Professional qualifications in Database Administration and Programming knowledge is added advantage
  • Familiarity with wide array of Fraud Analysis tools, applications and solutions

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