Objective

Assess the client’s AI/ML platform needs and gaps to recommend solutioning approaches that would enable their strategic goals of data and AI/ML models monetization.

Industry: Financial Services – Payments

Services: Strategic Advisory

Technology: AI/ML Strategy

Democratization of data to the data science and data analyst community within the enterprise.

Provided a comprehensive Solution Roadmap and recommendations which formed the cornerstone for capital funding for gap closure initiatives.

Justification for the adoption journey to cloud for the data workload.

Our Client

Our client is the second-largest payment-technology corporation globally. The company offers payment transaction processing and related services worldwide, facilitating transactions between merchants’ banks and card-issuing banks or credit unions using its debit, credit, and prepaid cards.

Challenge

The Global Payments Services client was looking to scale their AI/ML capabilities beyond serving the enterprise. Motivating factors included the potential to monetize their data & AI/ML models across their customers and deliver ethical (not-for-profit) AI/ML for the society.

Approach

Deployed surveys, conducted user workshops and researched vendor /provider landscape to provide a:

Needs and Gap Assessment
Solution Recommendations

that would support both current AI/ML capabilities and future needs.

Outcomes

AI/ML Reference Architecture developed to reflect the client’s nuanced requirements that also served as the framework of choice for socializing gaps and building consensus on prioritization of the next steps.
Vendor/Provider Evaluation Rubric that provided a robust and reusable mechanism to evaluate and rank AI/ML solutions from third-party solution providers.

Impact

Solution Roadmap that provided WorldLink’s comprehensive recommendations which formed the cornerstone for capital funding for gap closure initiatives
Justification for the adoption journey to cloud for the data workload
Democratization of data to the data science and data analyst community within the enterprise