In every aspect of our day-to-day lives, machine learning techniques interact with each other and have a growing impact on our behaviors. Machine learning algorithms are used in the financial sector to detect fraud, facilitate high frequency trading, and provide stakeholders with financial consulting services. Hence becoming extensively designed, ML is able to rapidly review millions of large datasets in order to enhance the outcomes. ML tends to be more effective when massive amounts of information are included into the program, when deriving findings and predicting the future. Implementations for financial risk management can benefit from machine learning, but the approach should be appropriate for the issue at hand and the data at hand. Furthermore, the application of machine learning methods in the field of financial services depends heavily on the environment. In the finance industry, machine learning is frequently viewed as a method that could provide that analytical power. In addition, ML can be employed for many activities that are thought to be above human capacity.

Machine learning, financial systemic risk, Risk analysis, Algorithmic accountability, Personal data, Risk-based inspection, Risk assessment, Fraud.