Senior Manager, Financial Crime Compliance Data, Insights & AI Enablement

Australian Compliance Institute; Sydney; Hybrid; AUD 180,000 - 240,000; Full time

Job summary

Commonwealth Bank of Australia is seeking a Senior Manager to lead the Financial Crime Compliance Machine Learning & AI function. You will partner with domain, data science and delivery teams to shape model design and governance, ensuring regulatory and operational outcomes are reflected throughout delivery.

You will coordinate testing, review results and support governance, risk assessments and stakeholder engagement to drive compliant, effective model deployments across AML/CTF, sanctions and

Qualifications

• Strong experience in Financial Crime Compliance, including AML/CTF, transaction monitoring, customer risk, sanctions or related detection environments.
• Deep understanding of financial crime typologies, detection frameworks and regulatory expectations.
• Experience working with Data Science, analytics or technology teams in data-driven delivery environments.
• Strong understanding of model delivery lifecycles, governance processes and testing practices.
• Experience translating financial crime risks and typologies into practical detection requirements and operational outcomes.

Responsibilities

• Partner with FCC domain, Data Science and delivery teams on FCC-owned model initiatives.
• Provide FCC input into model requirements, typologies and detection logic.
• Ensure financial crime risks, regulatory expectations and operational considerations are reflected within model design outcomes.
• Review and challenge model approaches, analytical outputs and delivery decisions from an FCC risk and governance perspective.
• Coordinate and oversee testing activities to ensure models are appropriately assessed prior to deployment.
• Review testing outcomes and support assessment of whether models are operating in line with FCC expectations and objectives.
• Support governance and approval processes, including model risk assessments, documentation and stakeholder engagement.
• Translate complex financial crime risks and emerging typologies into clear business and detection requirements.
• Work across multiple stakeholders to resolve ambiguity, manage dependencies and support timely delivery outcomes.
• Provide clear updates, insights and recommendations to FCC leadership and governance forums.
• Support internal audit, Line 3 assurance and regulatory engagement activities where required.

Skills

Financial Crime Compliance

AML/CTF

Sanctions

Model governance

Testing practices

Cross-functional collaboration

Regulatory knowledge

Job description

See yourself in our team

Financial Crime Compliance (FCC) sits within CBA’s Group Risk function and is responsible for setting the strategy, frameworks and oversight that protect the Group from financial crime risks.

As part of this, FCC plays a critical role in shaping and governing the Group’s advanced analytics and model-driven detection capability across AML/CTF, Sanctions, Anti-Bribery & Corruption (ABC) and Anti-Tax Evasion (ATE).

The Machine Learning & AI function sits at the centre of this transformation — partnering closely with Financial Crime domain, Data Science and delivery teams to ensure FCC-owned models are aligned to financial crime risks, regulatory expectations and operational outcomes.

This team plays a critical role in ensuring that models are built on clear financial crime requirements, supported by robust governance and appropriately tested prior to deployment.

What your role will involve

As a Senior Manager, you will report to the Executive Manager, Financial Crime Compliance Machine Learning & AI, and act as a key FCC representative across the delivery lifecycle for FCC-owned financial crime models.

You will work closely with Financial Crime domain, Data Science and delivery teams to ensure models are designed, tested and implemented in alignment with FCC expectations, financial crime typologies and regulatory obligations.

While model development will be undertaken by specialist Data Science teams, you will play a critical role in providing FCC input into model design, detection logic and governance outcomes — ensuring that financial crime risks and operational considerations are appropriately reflected throughout delivery.

On any given day, you will

• Partner closely with Financial Crime domain, Data Science and delivery teams across FCC-owned model initiatives
• Provide FCC subject matter expertise into model requirements, typologies and detection logic
• Ensure financial crime risks, regulatory expectations and operational considerations are appropriately reflected within model design outcomes
• Review and challenge model approaches, analytical outputs and delivery decisions from an FCC risk and governance perspective
• Coordinate and oversee testing activities to ensure models are appropriately assessed prior to deployment
• Review testing outcomes and support assessment of whether models are operating in line with FCC expectations and objectives
• Support governance and approval processes, including model risk assessments, documentation and stakeholder engagement
• Translate complex financial crime risks and emerging typologies into clear business and detection requirements
• Work across multiple stakeholders to resolve ambiguity, manage dependencies and support timely delivery outcomes
• Provide clear updates, insights and recommendations to FCC leadership and governance forums
• Support internal audit, Line 3 assurance and regulatory engagement activities where required

We’re interested in hearing from people who have

• Strong experience in Financial Crime Compliance, including AML/CTF, transaction monitoring, customer risk, sanctions or related detection environments
• Deep understanding of financial crime typologies, detection frameworks and regulatory expectations
• Experience working closely with Data Science, analytics or technology teams in model or data-driven delivery environments
• Strong understanding of model delivery lifecycles, governance processes and testing practices
• Experience translating financial crime risks and typologies into practical detection requirements and operational outcome

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