Data Mining in Finance: Current Advances and Future Challenges

Abstract

Data mining has been successfully applied in many businesses, thus aiding managers to make informed decisions that are based on facts, rather than having to rely on guesswork and incorrect extrapolations. Data mining algorithms equip institutions to predict the movements of financial indicators, enable companies to move towards more energy-efficient buildings, as well as allow businesses to conduct targeted marketing campaigns and forecast sales. Specific data mining success stories include customer loyalty prediction, economic forecasting, and fraud detection. The strength of data mining lies in the fact that it allows for not only predicting trends and behaviors, but also for the discovery of previously unknown patterns. However, a number of challenges remain, especially in this era of big data. These challenges are brought forward due to the sheer Volume of today’s databases, as well as the Velocity (in terms of speed of arrival) and the Variety, in terms of the various types of data collected. This chapter focuses on techniques that address these issues. Specifically, we turn our attention to the financial sector, which has become paramount to business. Our discussion centers on issues such as considering data distributions with high fluctuations, incorporating late arriving data, and handling the unknown. We review the current state-of-the-art, mainly focusing on model-based approaches. We conclude the chapter by providing our perspective as to what the future holds, in terms of building accurate models against today’s business, and specifically financial, data.