nir-vana innovation network

Financial companies and banks are today subject to having to deal with the compliance of their operations with complex regulations such as AML (Anti Money Laundering), KYC (Know Your Customer), Basel III (Basel Framework International regulatory standards for banks), the MiFID II (Markets in Financial Instruments Directive 2014) and many others. Banks must evaluate and manage a variety of risks, including credit risk, market risk, operational risk and liquidity risk. Furthermore, financial transactions can be vulnerable to fraud and prevention is a priority for banks. RegTech tools using advanced techniques, such as behavioral pattern analysis, risk analytics and monitoring, and machine learning can support financial institutions to detect suspicious behavior and fraudulent activities and then effectively identify and mitigate such risks. RegTech tools in banking aim to streamline and control regulatory compliance processes, improve risk management, prevent fraud, protect cybersecurity and improve operational efficiency, enabling financial institutions to adapt more agilely to an ever-changing regulatory environment. The solutions offered by the company for the banking and financial sector use Big Data technologies for monitoring, multidimensional analysis and decision support. This approach allows organizations to understand in detail and efficiently govern compliance processes, and in particular anti-money laundering (AML) analysis 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 analyses the large amount of regulations, to verify that the compliance of control processes used by the bank. The technology offered 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