Mexico Competition

The Comisión Nacional Bancaria y de Valores (CNBV) needs data infrastructure for AML supervision (the Project) to:

  1. Allow financial institutions to submit information for anti-money laundering (AML) compliance digitally and automatically to the CNBV;
  2. Increase the volume, granularity, and frequency – and improve the quality – of AML-related data submitted to the CNBV;
  3. Enable CNBV staff to import historical records into the central data storage platform; and
  4. Enable CNBV staff to improve AML-related data validation and analysis, and generate customized reports for supervisory and policy development purposes.

Reducing inefficiencies in current processes would give CNBV staff greater bandwidth to improve supervisory quality, and enable the CNBV to provide sharper guidance to supervised institutions in improving their AML compliance systems.

The CNBV is currently supporting the development of a new regulatory framework for the FinTech sector and the prototype RegTech solution is expected to strengthen its supervisory capacity.

The CNBV has been a pioneer in implementing a proportional, risk-based approach to AML with the aim of protecting the integrity and stability of the financial sector while creating a regulatory environment that is conducive to innovation and financial access.

By improving data quality and access, and developing new tools for data visualization and analysis, this Project will support the CNBV’s efforts to effectively implement an AML risk-based supervisory approach that reduces compliance costs and promotes financial inclusion while ensuring financial integrity.

The aim of this project is to develop a prototype for this solution by April 2018.


  • CNBV evaluates the Financial Institution’s suspicious transaction model (“C”) to ensure it identifies out of a given set of transactions (“B”) the suspicious transactions it was designed to flag (“D”).
  • Each supervised institution has implemented its own set of AML processes and has its own suspicious transaction model.
  • CNBV receives information in stacks of CDs or even printed paper files.
  • Because each institution’s suspicious transaction model differs, the CNBV’s onsite team needs to spend up to four weeks understanding the model and its parameters and verifying that flagging is happening as designed.
  • When there is an inquiry regarding a given supervised entity, the data is manually loaded onto the analyst’s computer, analyzed (typically using MS Excel), and then removed from the computer.


  • No way to get: (i) a complete historical view of a given supervised entity or (ii) a pan-entity view across a group.  
  • There is a technical limitation in that Excel has a maximum number of rows that can be allowed per file, which is often less space than what is required for analysis.
  • Entities do not completely understand the format requirements from the CNBV and often send information using varying formats. CNBV staff responsible for AML supervision spend a lot of time fixing these formatting inconsistencies.


  • A new data architecture using modern, secure, cloud-based technologies such as data lakes, EL[T], and big data tools.
  • Individual databases for each historic and future visit would be consolidated into the central data storage platform.
  • Dashboard solution and analytical tools would be introduced to generate fast, clear insights from the data.

Qualifications for Innovators

Each project requires an organization with the capacity, relevant experience, and resources required to develop a prototype of a data request/storage platform and tools for data-driven metrics and insights.

Key qualifications include the following:

  1. Demonstrated ability to build and/or integrate with large public-facing APIs, to intelligently design user interfaces for non-technical and technical customers, to properly write documentation, and to maintain an enterprise ecosystem.
  2. Experience with data analytics and building user-interactive applications.
  3. Institutional understanding of tech industry best practices and familiarity with industry-standard server-side and client-facing technologies.
  4. Ability to dedicate sufficient staff resources (e.g., developers, a designer and a project manager) throughout the prototype development and testing stage.

Project award

The successful applicant will:

  • Be awarded US$100,000 to develop and test the required solution. This is a fixed-sum grant award, which must cover all of the applicant’s expenses related to the development and testing work, including staff time, hardware, software, travel, and all other project-related expenses.
  • Participate in R2A working groups to be introduced to the global community of regulators and supervisors, investors, and academics that are partnering with R2A.
  • Be invited to participate in other events hosted by R2A partners to (a) understand the needs of emerging market financial authorities and (b) explore innovative solutions.
  • Be featured in the R2A website and publications as a pioneer within the emerging RegTech for Regulators community.
  • Benefit from association with prominent organizations such as the Bill & Melinda Gates Foundation, the Omidyar Network, and the US Agency for International Development, the three funders of R2A.

Information & Applications:

Please direct all questions about this initiative and your application to:


R2A competition partners (2).png