Global Talent Intelligence partnership with $70B+ New York based Insurance & Financial Services Leader

Discover How Draup Enabled AI-Driven Workforce Transformation for an Insurance & Financial Services giant

10,000+

Employee Count

$70 Bn

Revenue

BFSI

Industry

New York, US

Headquarters

About the Company

A global leader in insurance and financial services, the organization operates across 40+ markets and serves tens of millions of customers. It is strengthening operational resilience while accelerating digital transformation, with a sharp focus on securing leadership and analytics talent and building long-term workforce capabilities across mature and emerging markets.

Before Draup

1

Fragmented View of Critical Talent & Locations

No unified visibility into global availability and competitiveness of Real Estate, Executive Leadership, and Data & Analytics talent across key hubs.

2

Limited Market & Competitor Benchmarking

Incomplete insight into peer hiring, exits, compensation, and benefits made it difficult to position competitive offers in priority regions.

3

No Standardized Demand–Supply & Skills Forecasting

Emerging investment, data, and AI skill needs were evolving without a consistent model to track gaps or anticipate regional shortages.

4

Disconnected Data & Workforce Planning Frameworks

Siloed datasets and inconsistent job frameworks hindered integrated workforce planning and location decisions across functions and geographies.

The Core Challenges

Unify fragmented talent, role, and location data for enterprise-wide workforce planning.

Attract and retain executive and analytics talent in highly competitive U.S., LATAM, and EMEA markets.

Define and track emerging skills in investment, data, and AI to future-proof critical roles.

Use data on pay, diversity, and talent movement to benchmark peers and prioritize the right hubs.

The Solution

Data Scientist

PROFILE

LOCATION ANALYSIS

TALENT MARKET INSIGHTS

CAREER PATHS

DATA INTEGRATION

TECH STACK

Unified talent taxonomy

Built a global framework for Real Estate, Executive Leadership, and Data & Analytics roles by mapping 850M+ profiles and 550M+ job descriptions.

Executive intelligence in LATAM & EMEA

Quantified leadership supply–demand gaps, benchmarked compensation and benefits, and analyzed hiring/exit trends across major competitors.

Deep dives into U.S. Real Estate & Data markets

Assessed talent availability, hotspots, hiring difficulty, evolving skills, and pay trends for key U.S. hubs.

Comprehensive rewards benchmarking

Used Draup’s Cost Simulation and JD Benefits Analysis to compare compensation and benefits across critical roles and sharpen market competitiveness.

Outcome

Unified global workforce strategy

Integrated leadership, investment, and data talent insights into a single decision-making framework for long-term planning.

Data-led hub prioritization

Enabled clear selection of global and U.S. hiring hubs based on talent depth, cost efficiency, and hiring feasibility.

Sharper competitive positioning

Comprehensive intelligence on compensation, benefits, and executive movement across major insurers and financial services peers accelerated proactive talent acquisition strategies.

Future-ready capability building

Clearer visibility into emerging skills and more accurate workforce planning across markets, supporting diversification, internal mobility, and targeted upskilling.

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.