Accelerating workforce intelligence for a $7.4B stock market exchange leader

Discover How Draup Helps Restructure Roles, Boost Mobility, and Scale a Future-Ready Workforce

~9000

Employees

$7.4B

Revenue

Financial Services

Industry

New York, U.S.

Headquarters

About the organization

A global financial services giant, valued at $7.4 billion, the company operates leading exchanges across the U.S. and Europe while expanding into fintech solutions. With over 9,000 employees, the company is building an agile, tech-driven workforce to power digital transformation and emerging technology adoption.

The Core Challenges

Role Architecture Redesign
To restructure roles and families, aligning new tech adoption with future-ready workforce capabilities.
Internal Mobility Clarity
To enhance internal mobility by creating transparent reskilling routes and reducing costly external hires.
Peer Skill Benchmarking
To benchmark against peers and track evolving standards for competing in scarce digital skills.

The Solution

Financial Risk Manager
PROFILE
LOCATION ANALYSIS
TALENT MARKET INSIGHTS
CAREER PATHS
DATA INTEGRATION
TECH STACK
The image is for representational purpose only.
01
Workforce Intelligence
Broke down the company-specific workloads into AI-automatable, human-led, and hybrid tasks.
02
Skills Taxonomy
Built a regularized skills taxonomy to standardize job families and career paths.
03
Seamless Integration
Created 500+ non-linear mobility pathways with linked Udemy learning journeys.
04
Peer Intelligence Benchmarking
Delivered peer benchmarking reports to track evolving skill standards.

Outcome

Structured Career Transitions

Custom career paths helped employees reskill into future-ready roles.

Smarter Mobility

Transparent pathways boosted retention and reduced external hiring costs.

Stronger ROI on Learning

AI-driven skill mapping enabled rapid reskilling aligned with digital goals.

Future-Ready Workforce

A scalable framework to sustain long-term fintech innovation.
6,100+​
Locations
4M+​
Career Paths​
1.7M+​
Peer Group Companies​
28K+
Skills
3,500+​
Roles
1B+​
Professionals​
55K+​
Universities​
1B+​
Job Descriptions​
200K+​
Courses​
180K+​
Technology Solutions​
1.6M+​
Account Priorities​
Labor Market Data Contextualized with Business Intelligence for Every Decision

Analyzing 1B+ data points
daily from 100K+ sources

6,100+​
Locations
4M+​
Career Paths​
1.7M+​
Peer Group Companies​
28K+
Skills
3,500+​
Roles
1B+​
Professionals​
55K+​
Universities​
1B+​
Job Descriptions​
200K+​
Courses​
180K+​
Technology Solutions​
1.6M+​
Account Priorities​
AICPA SOC2 TYPE 2
SOC 2
GDPR Logo
GDPR
ISO 27001 Logo
ISO
EAIGG Logo
EAIGG

Ethical AI, Backed by Global Standards

Draup, an AI-first company, upholds global standards like SOC 2, GDPR, and ISO 27001. As an EAIGG member, we audit for bias and build trustworthy AI.

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.

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.

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.

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.

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.

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.

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.

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