Reengineering data infrastructure to strengthen AML supervisory capacity.
About
Applicant name: Government of Mexico
Innovation Name: Suptech AML CNBV
Innovation Type: Solution
Website: http://www.gob.mx/cnbv
Mission: Reengineer its data infrastructure in order to strengthen its AML supervisory capacity.
Innovation Description: An API-based AML data architecture and AI-driven analytics tool, which included: Centralized platform to generate standardized, automated requests to supervised entities with raw data received through push or pull submission stored in a data lake. An API to establish secure, direct line of machine-to-machine data transmission feeding the data into a processing engine instantly running validations tests verifying quality, content and structure of reports and funnelling processed data into the data lake creating a consolidated, single and access-controlled data architecture. AI-driven analytics that detects suspicious transactions using predictive analysis and ML techniques (clustering, neural networks, logistic regression, random forests) and recommend AML alerts using ML based on FIs underlying risks exposures. Dashboards and watchlist tracker provide a view of the AML risk landscape. We are currently developing the tool and it is scheduled that by the end of 2020 there will be 48 dashboards in production.
Innovation Inspiration: Inefficiencies in AML data architecture combined with many financial institutions categorized as high-medium risk results in inadequacies in drawing deep insights from data informing onsite visits or otherwise as well as delayed and unproductive auditing. These challenges arise from pain points identified across the AML supervision life cycle. In terms of data extraction this includes the submission of incomplete, erroneous, or improperly formatted reports. In terms of data transmission, it includes the data formats, file size limitations and security. In terms of data storage, it includes diverse data formats and a lack of a data warehouse system. And in terms of data analysis it includes reliance on manual loading of “cold storage” data loading and Excel analytics which prevents complex data mining and application of statistical models and data visualization.
Who benefits from the innovation most? The most important part of this technological development is the benefits for all participants in the supervision process. In the case of the Supervisory Authorities they will be able to establish standards to obtain the homologated information of all those Entities that they will supervise.
These information standards range from the construction of layouts, in which the supervisory authorities will indicate what information is being requested, as well as the characteristics that it must meet.
As part of the characteristics that the information provided must have, is the establishment of rules, within which we can find the standardization of certain information with previously established catalogs of obligations.
As a result, the Supervisory Authorities will be able to carry out a comprehensive analysis of all the supervised entities, in order to identify unusual behaviors throughout the financial system.
What are you most proud of about your innovation? Currently, the review of the information provided by the Supervised Entities is two to three weeks, depending on the volume of the information that is provided, taking into account that the visits are carried out in a period of four to five weeks, which represents up to 50% of the time of the visit, only in the review of the information, leaving approximately 50% of the time for the analysis of the information.
Considering that the volume of information is from 3 million records in a small entity to more than 50 million records in a big entity.
With the development of this tool, it will be possible for the analysis of large amounts of information to be carried out in a matter of minutes, reducing the review time of the information provided by up to 99%.
Status of Innovation: Pilot
Applicant Type: Government Agency
Headquarter City: Mexico city
Headquarter Country: Mexico
Year Founded: 1924
Main Category
Main Category Choice: Compliance Solution
Why do you think you should win this category?:
As part of the functionality of this tool, there are several boards that will allow us to identify what are some of the unusual behaviors that are already found in some of the typologies published by FATF.
In this way, the supervisory team will be able to analyze in more detail all the transactions carried out by this type of client, identifying in a timely manner the unusual behaviors that are being carried out, as well as the people related to them. Derived from this analysis, new unusual behaviors carried out by the various clients of the various Supervised Entities may be identified, identifying the types of products that are used, the amounts, the main characteristics of the clients, the geographical areas in which they are carried carry out operations, among many others. By improving data quality and access, and developing new tools for data visualization and analysis, CNBV’s efforts to effectively implement an AML risk-based supervisory approach that reduces compliance costs and ensuring financial integrity.
Secondary Category
Secondary Category Choice: Technology Innovation
Why do you think you should win this category?
As part of the functionality of this tool, there are several boards that will allow us to identify what are some of the unusual behaviors that are already found in some of the typologies published by FATF.
In this way, the supervisory team will be able to analyze in more detail all the transactions carried out by this type of client, identifying in a timely manner the unusual behaviors that are being carried out, as well as the people related to them. Derived from this analysis, new unusual behaviors carried out by the various clients of the various Supervised Entities may be identified, identifying the types of products that are used, the amounts, the main characteristics of the clients, the geographical areas in which they are carried carry out operations, among many others. By improving data quality and access, and developing new tools for data visualization and analysis, CNBV’s efforts to effectively implement an AML risk-based supervisory approach that reduces compliance costs and ensuring financial integrity.
Team Members
Full Name: Sandro Garcia Rojas Castillo
Role: Leader
Linkedin: https://mx.linkedin.com/in/
Full Name: José Luis Ortiz Guzman
Role: Sub leader
Link: https://mx.linkedin.com/in/
Full Name: Cesar Cid Contreras
Role: Sub leader
Full Name: Maribel Flores Santiago
Role: Sub leader
Supporting Links
First steps in the proyect: https://static1.squarespace.
Video: https://www.youtube.com/watch?
Entry Representative
Full Name: Sandro Garcia Rojas Castillo
Role: Leader
