Transforming Workforce Strategy for a $14B Australian Consumer Banking Giant

Discover How Draup Enabled AI-Driven Workforce Transformation for a National Banking Leader

35,000+

Employee Count

$14.0 Bn

Revenue

Banking and FinServ

Industry

Sydney, Australia

Headquarters

About the Company

A leading financial services enterprise and one of Australia’s largest consumer banks, the organization serves millions of customers nationwide through a broad branch network, metropolitan centers, and regional operations. With 20,000+ employees and $14B in annual revenue, it is known for innovation, stability, and customer trust.

Before Draup

1

Limited Visibility into AI’s Workforce Impact

Unclear how AI and automation would reshape banking roles, workflows, and customer interactions.

2

Unclear Role Evolution & Reskilling Pathways

Rapidly changing skill needs made workforce transitions, reskilling priorities, and future structures difficult to define.

3

Insufficient Talent, Location & Regulatory Insight

Leadership lacked clarity on talent availability, hiring difficulty, compensation, and rising compliance and cybersecurity demands.

4

Uncertain Branch & AI Governance Strategy

Branch network shifts and disparate AI initiatives created ambiguity around future service models and role redesign.

The Core Challenges

Understand AI’s impact on banking roles, workflows, and customer-facing operations.

Define evolving skill needs and build clear reskilling pathways across key job families.

Evaluate talent availability, hiring difficulty, and cost across major and regional Australian hubs.

Clarify branch evolution, academic partnerships, and governance requirements for AI-enabled roles.

The Solution

Customer Success Manager

PROFILE

LOCATION ANALYSIS

TALENT MARKET INSIGHTS

CAREER PATHS

DATA INTEGRATION

TECH STACK

AI & Digital Workforce Impact Assessment

Analyzed how AI and automation reshape banking workflows, customer interactions, and role structures, supported by impact ratings and competitive benchmarking.

Talent Intelligence & Role Evolution Pathways

Mapped national talent supply, hiring difficulty, and compensation, while defining role adjacencies, reskilling needs, and pathways into AI-augmented roles.

Branch & Operational Transformation Strategy

Outlined future branch models—Smart, Advisory, Cashless—enabled by automation, hybrid servicing, and AI-driven customer experiences.

Regulatory, Security & Global Benchmarking Insights

Assessed emerging compliance and cybersecurity requirements, and benchmarked global banks to guide workforce distribution and capability-center strategies.

Outcome

AI Ready Workforce Strategy

Delivered a unified view of how AI transforms nine banking job families, supported by role evolution pathways and structured reskilling roadmaps.

Operational & CX Productivity Gains

AI-driven efficiencies delivered significant productivity gains, including over 55% time savings, 4.5× output improvement, faster CX analysis, reduced compliance monitoring time, and accelerated reporting cycles.

Future-Focused Skill Architecture

Created a taxonomy of traditional, emerging, and AI-driven skills across Risk, IT, Customer Service, Legal, and Operations.

Strategic Talent & Location Intelligence

Provided city-level insights on talent availability, hiring difficulty, and compensation to optimize national workforce distribution.

Powered by unparalleled global labor & market data

19K+

Skills

850M+

Professionals

5700+

Locations

1.6M+

Peer Group Companies

1B+

Job Descriptions

4M+

Career Paths

Data Trust & Methodology at Draup

Draup uses a structured methodology to reduce inaccuracies and prevent misleading information, with a focus on transparency and responsible data practices.

1

Data Transparency and Coverage

Draup sources data from diverse, vetted global inputs including public datasets, labor information, professional ecosystems, and industry research. Coverage varies by region and role, and we supplement limited areas with alternative sources and analyst-led modeling.

2

Profiles Behind the Numbers

All aggregate insights related to buyers, roles, skills, and locations are grounded in structured, anonymized human and organizational profiles rather than pure extrapolation. This ensures context, reliability, and protection of personal identities.

3

Data Hygiene and Integrity

We maintain a clean, accurate data environment by removing duplicates, outdated entities, and invalid records. Automated checks powered by ML models and analyst reviews work together to ensure clarity and accuracy for every entity, such as buyer, role, skill, technology, or company. Learn more

4

AI Governance and Bias Mitigation

Our governance framework combines automated evaluations with Human-in-the-Loop reviews. We use statistical checks, audits, and cross-source comparisons to reduce demographic and structural bias before insights reach the platform.

5

Compensation Guardrails

Compensation insights use blended, directional inputs such as aggregated ranges, benchmarks, modeled distributions, and market signals. We distinguish clearly between modeled and reported data and apply guardrails to prevent over-interpretation.

6

Hourly and Frontline Workforce Coverage

For hourly, frontline, and blue-collar roles where digital visibility is limited, we incorporate government datasets, localized labor information, and specialized partners to build a more complete and balanced view of workforce supply and demand.