Data Services

The management and monitoring of vast quantities of data are one of the central challenges for banks in their efforts to battle cyber-crime. Banks feed data from a diverse set of sources into centralized monitoring systems. Global banks obtain customer and transaction data from various systems and maintaining the quality of data in terms of accuracy, timeliness and other factors can be quite difficult.

ComplyGenics offers high-quality business solutions by first assessing the needs of end-users and the current architecture of the system. Then, our business intelligence experts apply appropriate methods for design, planning, information management, performance optimization, implementation, and integration. Our goal is to enhance each client with smart and friendly user interfaces, effective training, and efficient documentation of their compliance data management.

Enhancing the Quality of Data

The quality of insights derived from any analysis will be highly dependent on the quality of data provided. Financial services firms use a variety of internal and external data sources to fight crime, but many firms – particularly universal banks operating in different regions and across different lines of business, using multiple systems and data sources – face data quality issues.

Analytics to Transform Data Into Information & Information Into Insight

For most organizations, a lack of data is not the problem. The real problem is a lack of the right data. Banks typically have greater access to centralized data than they have ever had, but most banks use less than five percent of the available data in making decisions related to financial crime prevention; much of the rest of the data is considered too expensive to deal with.

Applying Data Visualization Techniques

As the volume and complexity of data increase, financial institutions are adopting data visualization techniques allowing complex data to be viewed by business experts through a visual interface. This helps the business process experts look for visual patterns and identify inconsistencies.

Reference & Master Data Management

Conduct current state assessment of Master Data Management practices / tools and provide recommendations for desired improvements. Determine the sourcing and consumption requirements and design to create and manage Master Data. Reconcile Reference Data & standardize for upstream and downstream consumption and management.

Data Quality

Establish the data standards for definition and usage, including business rules. Profile the existing data against the standards. Analyze the profiling results and develop a Data Cleansing Strategy. Monitor data over time through data quality dashboards and create alerts when data violates set business rules.

ETL Design & Development

Extract Transform Load (ETL) is a complex exercise that simply cannot be blindly copied from one implementation to the next. Having developed ETL solutions across a variety of risk and compliance scenarios, APN enables financial institutions to incorporate best practices, recognize exception protocols, and establish a rigorous and robust quality assurance process when setting up ETL to optimize results from any detection system being used.

Data Warehousing

Architect solution including ETL subsystems, data validation controls and presentation layer and identify and define downstream data integrations. Define the scope of data sources and required history for inclusion. Measure and define data quality issues and coordinate with Data Governance to address

Big Data Design & Development

As financial intuitions seek more effective means to manage data stores related to financial crime and compliance, they can rely on Matrix-IFS’ track record to architect big data solutions in this domain. Our experts can deliver the components needed to implement the program successfully within the organization and develop custom tools, applications, and enhancements to meet the organization’s IT needs.

Data Modeling

Develop the Conceptual and Logical Data Models and organize into subject areas. Transform the Logical Data Model into the Physical, identify and define downstream data integrations, and plan, execute and institutionalize

Data Conversion

ComplyGenics assists establishing conversion strategy based on business needs and builds a conversion architecture. ComplyGenics acts as a managed partner in developing source-target mapping as well as developing data validation framework.

Data Integration

Define the scope of the data integration and construct the architecture plan and all relevant data sources, mappings, cleansing, and data transformations

Metadata Management

Conduct current state assessment of the Metadata Management practices / tools and provide recommendations and roadmap for tagging and cataloging metadata

Cloud Services

We have extensive experience in helping financial institutions operate their financial crime and compliance technology solutions on the cloud. We can leverage private cloud solutions or partner with industry cloud providers such as AWS or Google Cloud, for a public or hybrid cloud solution. We strongly believe that a robust cloud strategy, executed by a reliable and experienced partner can “future proof” the organization’s risk and reduce the cost of financial crime and compliance starting from day one.

Our Services:

ETL Architecture

Determine Data Warehouse ETL Architecture to be used to effectively migrate data from ultimate source to target

ETL Framework

Define overall Extract, Transform and Load (ETL) Strategy, relevant data sources, mappings and data transformations

ETL Development

Design and develop ETL packages to carry out data transformation and loads

Data Mapping

Map Source to Target data mappings including required transformations


Conduct appropriate functional and performance testing to identify bottlenecks and data quality issues

Data Modeling

Develop the Conceptual and Logical Data Models Transform the Logical Data Model into the Physical Model and DDLs

Data Governance

Organization, process and tools to effectively manage data as a business asset

Data Quality Assessment

Provide an overarching data quality assessment to prioritize and direct investments in enhancing data quality

Data Cleansing

Executing on data transformations and standardization to grow Data Quality

Data Retention Planning

Evaluating data retention requirements around analytic needs, availability of historic data, eDiscovery risk, etc.

Our Activities: