The EBA (European Banking Authority) estimates that 70% of financial institutions are satisfied with the RegTech solutions implemented, however, in the regulatory reporting and Anti Money Laundering (AML) areas these percentages fall to 33% and 22% respectively.

What causes such a significant discrepancy in the perception of these areas of RegTech?

Regulatory reporting, as well as AML procedures, are clearly cost centres – they consume significant resources without directly influencing the business and undergo constant legislative evolution. Regulators in order to supervise and to ensure the security and stability of the financial sector, require in-depth analysis of reported information. And to identifying money laundering or terrorist financing, AML procedures generate a high number of false positive alerts that have to be analysed and evaluated.

However, it is possible to control these variables and diminish the cost involved. Technologies such as Machine Learning and Artificial Intelligence allow to significantly limit the number of false positives and focus on the analysis of those cases that actually constitute a high risk of money laundering. Additionally, it is possible to calibrate the AML procedures by adjusting alert limits (within allowed by regulation) to the typology and risk of customers or products.

In the case of regulatory reporting, the biggest challenge during implementation is the approach to data that is used as a source for reporting and considering the project only as a technological component, forgetting that it has a very significant functional part. A unique approach and flexible ETL (Extract, Transform, Load) according to the procedures defined in BCBS239 allow to substantially decrease future solution maintenance costs and to accelerate the response time to the regulator.

The configuration of regulatory reporting as well as money laundering prevention processes are influenced by the institution’s internal procedures and data format available for analysis. And those are directly interlinked to the products and instruments offered by the institution as well as the customer populations. This diversity of products and populations as well as internal differences in the organisation of institutions, requires the regulatory reporting or AML solution to be flexible enough to fit the reality of the particular institution.

Rigid IT products, designed for all institutions in the financial sector (and it is a very large sector), cannot meet the specific needs of a particular institution. In addition, providing coverage to all possible cases increases the complexity of the system and influence directly user experience.
To overcome this situation, implementations should be approached as a mix of two realities: a mature product (with several proven features) and a specific implementation and configuration. This way it will be possible not to overload the end-users with unnecessary functionalities, simplifying and reducing the number of steps needed for regulatory compliance.

A flexible approach to the data ETL process allows not only to increase the quality of the processed data, but also to make it easier for users to perform tasks. It allows for a focus on the control and monitoring of the reported information rather than on the execution of the reporting itself.