This chapter will introduce financial analytics very holistically and then dive
into how machine learning transformed the finance industry. The discussion shall start
with the underlying principles of financial analytics; it involves rigorous analytical
expositions of financial data to deduce insights that promote decision-making, enhance
performance, and avoid risks. Areas such as performance analysis, risk management,
forecasting, fraud detection, and optimization will be featured within this light theme of
data-driven decision-making in contemporary finance. The role of machine learning is
then discussed within the chapter. The author states that the impact machine learning
has on predictive analytics, algorithmic trading, fraud detection, portfolio optimization,
and scoring credit has increased lately. Much more accurate and almost instantaneous
decision-making with financial applications is enabled by machine learning when
processing large, complex datasets. A challenge to financial data, the chapter goes on
to discuss issues it poses in terms of quality, non-stationarity, imbalanced datasets, and
interpretability of model outcomes. While such challenges are plentiful, there are many
opportunities as well inside this landscape, from alternative sources of data to real-time
analytics, automation, and even RegTech solutions. The chapter concludes by stating
that it is only when the following challenges are addressed that machine learning will
truly be leveraged in finance for both scalable insight and cooperative intelligence.
Keywords: Financial analytics, Machine learning, Principal component analysis.
