Artificial Intelligence solutions

RegTech for risk and compliance management

RegTech is the application of Artificial Intelligence techniques for compliance monitoring, and it is a strong competitive advantage for banks, which operate in a complex regulatory scenario where financial crimes monitoring and compliance management are expensive and inefficient.
Big Data Analyitics tools allow greater transparency in financial institutions operations, increasing customer satisfaction.

RegTech allows banks and financial institutions to improve regulatory compliance, risk monitoring and the detection of financial crimes while reducing management costs

What is RegTech

RegTech is the application of Big Data technologies in the banking and financial sector  for monitoring, multidimensional analysis and decision support. This approach allows organizations to deeply know and manage compliance processes, and in particular anti-money laundering analysis (AML) and risk management


By using machine learning and deep learning is possible to analyze huge volumes of transactions, identifying money laundering or fraudulent scenarios


Risk monitoring can become more efficient through multidimensional analysis and machine learning algorithms for customer profiling


Classification and text intelligence techniques could be used to quickly analyse the large amount of regulations, to verify that the compliance of control processes used by the bank

RegTech advantages

RegTech allows the automation of human activities like  compliance controls, and at the same time enables the discovering of correlations to make the financial crimes fighting more robust and effective. In this way it is possible to:
maximize productivity, reducing subjectivity in assessing potential financial crimes
– enable real-time monitoring and multidimensional analysis of transactions, in order to prevent fraudulent activities
increase customer satisfaction and minimize risks

RegTech and
Artificial Intelligence

RegTech uses Artificial Intelligence techniques based on inductive (machine learning and deep learning) and deductive (reasoning) approaches to cross-reference transaction and customer data, thereby profiling behavior. In particular:

profiling techniques and association rules are used for a greater and deeper knowledge of customers 

classification and correlation algorithms enable more in-depth control over transactions and the identification of anomalous scenarios that must be manually verified

natural language analysis and process mining techniques allow to verify the compliance of the implemented processes with respect to the regulations

Manage AML monitoring with Moneying, the Revelis solution for RegTech

Moneying is a suite based on Rialto™ platform that provides advanced analytics functionalities to identify potential financial crimes or risk behavior.


Financial crimes are identified through the verification of circular transfers of money or anomalous behavior


Risk is monitored through dynamic indicators. Moneying provides flexible and customizable risk reduction mechanisms


Moneying uses historical data to predict potential fraudolent situations and to manage alarms

Do you want more information on Revelis solutions for Big Data Analytics?

Contact Us