Fraud Data Analyst

1 week ago

Sydney, New South Wales, Australia Commonwealth Bank Full time

You are a curious and enthusiastic Analyst with a true passion for data and analytics

We are a close knit team who use data to develop and enhance the Group's fraud detection systems, algorithms, and reporting engines

Together we will develop detection strategies and analytic solutions to help protect our customers from falling victim to fraud and scam events.

Do work that matters:


The Fraud Data Analytics team, part of the Chief Data and Analytics Office, maintains the fraud detection logic for the Group.

Our team supports the operations of Group Fraud Management Services who are devoted to protecting the monetary assets from fraud and financial loss of CBA and Bankwest customers.


See yourself in our team:

Fraud Analytics is responsible with defining and execution of robust fraud analytics procedures for the Group.

Our team's diverse backgrounds means we can draw on one another's experience to deliver to best work outcomes. We are committed to safe-guarding our customers from financial fraud and scams. Our analytics run on Australia's largest real-time transaction monitoring system, handling millions of transactions per day.

On a day-to-day basis, you will:

  • Make continual improvements to detection system rules to ensure maximum detection occurs whilst false positive and point of detection remains low.
  • Ensure detection system rules do not adversely affect customer experience.
  • Provide insights into trends and anomalies including root cause analysis.
  • Build effective working relationships within stakeholders.
  • Manage potentially difficult interactions ensuring the appropriate priority or severity level is assigned.

We are interested in hearing from people who have:

  • An analytical mindset with the ability to solve problems in a creative way.
  • Proven experience working with SQL to extract and manipulate data from complex datasets.
  • Experience working with Tableau, SAS, R or Python would be desirable.
  • Ability to understand fraudster behaviour and psychology to improve rules.
  • Understanding of Machine Learning and how it can be used to detect Fraud/Scams.
  • Ability to suggest and carry out practical actions to address upcoming fraud trends with tangible outcomes.
  • Tertiary qualified in relevant disciplines: Actuarial science, applied mathematics, Statistics, Computing Science, Data Science.
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Advertising End Date: 28/08/2023

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