Mastercard - Improves Visibility and Reporting with AI



Mastercard harnessed the power of Artificial Intelligence (AI) and Machine Learning (ML) to improve the quality of its contract metadata and analytical capabilities. This innovative approach has ushered in significant enhancements in information accessibility, collaboration, and service delivery, both internally and for customers.

Business Challenge

Facing the challenge of improving contract information management and reducing risks associated with high-risk vendors, Mastercard joined forces with Mainspring. Their mission was to cleanse the existing Contract Lifecycle Management (CLM) repository, validate key contract provisions, and optimize spending adherence to contractual agreements. Over the years, Mastercard's CLM system had suffered from data integrity issues due to poor user adoption. The organization sought to recalibrate its data and bolster its analytics capabilities while revamping the CLM platform.

Our Solution:
In collaboration with Mastercard, Mainspring devised a comprehensive data entry model that not only captured essential transactional data but also introduced additional fields to facilitate advanced analytics. A structured data cleansing schedule was implemented to meet internal deadlines and support the redesign of a more robust CLM program, ensuring that future contracts would capture vital details during their creation. Furthermore, Mainspring incorporated Kira Systems, a Natural Language Processing (NLP) machine learning system, to extract contract text from standard and non-standard agreements.

Solution Delivered

Mainspring's efforts for Mastercard unfolded in several stages:

  • Machine Learning-Powered OCR and Extraction: Initial deployment of machine learning technology to perform Optical Character Recognition (OCR) and extract contract provisions tailored to Mastercard's requirements
  • Manual Verification by Contract Analysts: Mainspring's contract analysts meticulously reviewed the extractions, verifying and making corrections as necessary. This process included transcribing handwritten text and ensuring the integrity of parent-child-sibling contract relationships.
  • Data Transformation for CLM System Integration: The verified data, with an accuracy rate exceeding 95%, was then transformed to be seamlessly integrated into Mastercard's CLM system.
  • Reusable Process: The use of saved models allowed the process to be repeated as needed for additional batches of contracts.

Key Results

Mastercard's CLM repository is now being extensively utilized, particularly for evaluating the quality of contracts with existing vendors and establishing contracts with new ones. The organization also enjoys ongoing access to the extraction technology for on-demand data retrieval.

This AI-powered application has also yielded valuable insights into:

- Identifying causes and factors contributing to missing contracts.
- Monitoring the timeliness and frequency of upcoming renewals and expiration dates.
- Categorizing and tracking the amount of spend not covered by existing contracts.

Crucially, executive support played a pivotal role in ensuring the success of this initiative. Mastercard's sponsorship of the project drove its ambitious timeline while minimizing disruptions to the business.

The infusion of trustworthy data via AI has paved the way for transforming information into actionable insights. This shift towards data-driven decision-making not only saves time and resources but also enhances user adoption and overall effectiveness, resulting in a more efficient and competitive Mastercard.