Global Activity Based Costing Big-Data Model for a Money transfer corporation

revealing the true profitability of different solutions and client segments.

COST INTELLIGENCE ENGINE

Client Context

A leading global money transfer and cross-border payments corporation operated across 100+ countries with fragmented financial systems, complex compliance structures, and high transaction volumes.

Despite robust top-line growth, the company struggled to understand true profitability by corridor, product, and customer segment. Traditional cost allocation relied on outdated assumptions and manual spreadsheets, limiting visibility into cost drivers and masking inefficiencies in operations and service delivery.

Key Challenges

Opaque Cost Structure: Overhead and shared service costs were spread using broad averages, obscuring actual drivers such as compliance checks, fraud monitoring, or payout partner fees.
Data Fragmentation: Operational, financial, and customer data resided across regional systems and currencies, making consolidated analysis time-consuming.
Static Models: Legacy costing models were not scalable for the company’s digital expansion or evolving transaction mix (retail, online, corporate transfers).
Profitability Blind Spots: Inability to link activity costs to customer behavior, product usage, and regional compliance demands.

Solution Design

Big-Data–driven Activity-Based Costing Engine was deployed and integrated with the client’s data lake and ERP environment to create an automated, transparent, and scalable profitability model.

Key Components:

Global Cost Model Architecture
- Designed a unified activity framework covering retail, digital, and B2B money transfer flows.
- Standardized cost pools (e.g., compliance, partner management, customer servicing, IT, treasury) across 12 regions.
- Linked operational drivers such as number of transactions, KYC reviews, refund requests, and API calls to corresponding resources.
Data Integration Layer
- Connected 20+ disparate data sources, including finance (GL/CO), transaction logs, digital channels, workforce management, and partner payments.
- Established automated ETL pipelines in the cloud (Azure Data Factory / Databricks) for near-real-time data refresh.
Machine Learning-Enhanced Driver Discovery
- Applied random forest and SHAP analyses to detect non-linear cost drivers (e.g., fraud case volume, customer onboarding effort, transaction corridor risk).
- Improved driver causality accuracy by 25% vs. manual mapping.
Big-Data ABC Engine
- Implemented a distributed computation model capable of processing 100M+ transaction records monthly.
- Cost allocation performed at transaction-level granularity, producing true cost-per-transaction, cost-per-customer, and cost-per-corridor metrics.
Dynamic Profitability Dashboard
- Built Power BI dashboards for real-time profitability by region, channel, and customer segment.
- Included drill-downs into activity and resource cost layers with automated variance explanations.

Key Deliverables

Global Activity & Cost Driver Catalog – standardized 180+ activities and 60+ measurable drivers.
Automated Allocation Engine – ML-assisted ABC model integrated with ERP data pipelines.
Profitability Cube – multidimensional profitability dataset for ad-hoc slicing and what-if analysis.
Executive Dashboards – consolidated regional cost & profit views with predictive margin forecasts.
Governance Playbook – methodology for ongoing model updates and business ownership.

Business Impact

True Cost Transparency: Achieved 98% coverage of total operating costs via driver-based allocation.
Granular Profitability Insight: Identified unprofitable corridors and customers contributing to 18% of volume but generating negative margins.
Strategic Resource Reallocation: Enabled data-driven rationalization of payout networks and compliance resourcing, saving USD 12 M annually.
Faster Reporting: Reduced profitability reporting cycle from 4 weeks to 2 days.
Predictive Planning Enablement: ML models forecasted cost behavior under transaction growth and regulatory-change scenarios.

Conclusion

The Global ABC Big-Data Model transformed the client’s cost management capability from static reporting to predictive, transaction-level profitability intelligence.

It empowered leadership to make informed pricing, partnership, and investment decisions while sustaining compliance and service quality across global operations.