LO 75.2: Describe the application of machine learning approaches within the

LO 75.2: Describe the application of machine learning approaches within the financial services sector and the types of problems to which they can be applied.
Financial institutions deal with an increasingly large volume of data they need to analyze, which requires complex analytical tools. In response to new regulations and compliance measures, following the 20072009 financial crisis, financial institutions have been required to report more comprehensive details on balance sheet metrics and business models. These include stress tests, and reporting on liquidity measures, capital, and collateral.
As a result, financial institutions need to be able to adequately structure, analyze, and interpret the data they collect. Various regulatory standards were introduced on data delivery with the aim to improve the quality of supervisory data, including the Basel Committees Principles for Risk Data Aggregation (Basel 239) and IFRS 9.
Financial institutions are also faced with an exceptionally large amount of low-quality, unstructured data, called big data, from the output of consumer apps, social media feeds, and various systems metadata. It has become increasingly more important that institutions are able to effectively analyze this high volume of data, including using conventional machine learning techniques as well as more complex deep learning techniques.
Financial institutions should use conventional machine learning techniques for mining high-quality, structured supervisory data. Deep learning and neural networks should be used for low-quality, high-frequency, big data type sources.