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
FINANCIAL CRIMES
By using machine learning and deep learning is possible to analyze huge volumes of transactions, identifying money laundering or fraudulent scenarios
RISK MANAGEMENT
Risk monitoring can become more efficient through multidimensional analysis and machine learning algorithms for customer profiling
COMPLIANCE
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.
ANTI MONEY-LAUNDERING
Financial crimes are identified through the verification of circular transfers of money or anomalous behavior
RISK MANAGEMENT
Risk is monitored through dynamic indicators. Moneying provides flexible and customizable risk reduction mechanisms
PREDICTIVE ANALYSIS
Moneying uses historical data to predict potential fraudolent situations and to manage alarms
