The impact from the financial crisis has revealed that despite significant investments in sophisticated data management architecture, banks were not able to take preventive measures or reduce risk. Furthermore, recent increases in banking regulation with parallel
increasing complexity has created a challenging struggle for banks and institutions to manage through their information. Data approaches have to be re-worked, which has created an opportunity for innovation. New approaches, methodologies and tools related
to data management, governance and compliance can be adopted.
Collecting data typically is extremely difficult since most reporting is done manually with complex tools, making it almost impossible for IT and Business to be in harmony.
However, with increasing pressures and more regulatory requirements this has become overwhelming and nearly impossible to accomplish in the right time limits with limited resources. Even though the aggregation and reporting regulations are clear, implementing
them in practice is complex and costly (IT, Risk, Compliance Audit...) As the number of reports required increases with increasing global business transactions banks are finding they need to comply to legislation at all levels (internally, locally, nationally,
Moreover, banks are completing reports, for regulatory requirements, manually because they do not have the resources or tools to automate their processes. Even if they do, most of the time, the technical software is separated by either business function or
activity, created a very disconnected structure to data management. If firms had one place to handle and view their information, most challenges would be solved.
It is now more critical than ever to adopt a fresh approach with a new generation of tools that provide simpler data management coherent with business processes. Innovation in technology and tools must advance the industry in the right direction. Banks and
institutions must be able to create reporting in an efficient way that does not require so much time and resources spent doing a task that can be reduced significantly and instead greatly improve the quality of data presented to supervisors.
Innovation will be the key to create new, efficient processes, ensure compliance and reduce risk. Innovated solutions will automate manual processes and most importantly aggregate all information so that it is organized. In one single layer top management can
view data in a way that is directly linked to company strategy in order to make sound business decisions, while being compliant at all levels of the organization (internally and externally).
A RegTech EDM solution entails implicit data management that is compliant and extracts data's maximum value in terms of prevention (e.g. reduce systemic risk, solvency and adequate capitalization in times of crisis, transparency) and as a means of proposition
(e.g Multi-channel, BigData, Customer Analytics, etc.).
As a RegTech optimized platform it has to:
• Data integration from legacy to hadoop: Function as a hub for different data sources facing aggregation with a federation logic, as opposed to copying the control logic across the data or vice versa
• Declarative ELT approach: Reduce technical complexity (technical controls, backup repositories, execution plans) to let users focus on business aspects such as defining rules and controls, data reconciliation, and aggregation, etc.
• Rules based user interface for non-developers: Ensure parameters by reducing development times and use an agile approach according to more effective rapid prototyping
• Role base interaction: Enable profiles for data management on a user role basis from IT Architect to Data Steward and Business User attributing the right competence and ownership for each one.
• Automatic documentation in natural language: Simplifies the rules and controls definition as well as theire life cycle management using automatic documentation that is readable and understandable for business user
• Accountability: Ensure the appropriate level of data accountability and provide correct audit levels and data transformation processes used