Big Data Analytics in Finance: Enhancing Risk Management Through Predictive Modeling
DOI:
https://doi.org/10.70179/aeweb616Keywords:
AI, wealth management, machine learning, personalized investment, big data, finance, risk management, predictive modeling, cloud infrastructure, scalable services, agile finance, credit scoring, portfolio optimization, AI integration, cloud computing, wealth advisory, data-driven strategiesAbstract
Recent technological innovations allow the collection of data at unprecedented scales and speed. Big Data Analytics tools improve decision making in numerous business areas such as marketing, human resources, operations management, etc. Predictive modeling, one of the techniques of Big Data Analytics, provides crucial support to decision making, helping organizations to foresee the implications of their actions. Banks and Financial Services Companies have been early adopters of data analytics and closely related machine learning techniques to optimize transactions and operations in multiple tasks such as credit scoring, anti-money laundering, fraud detection, trading, etc. However, despite the ample amount of transactions and client data available, Risk Management is one of the areas that can take advantage of the recent technological trends to incorporate advanced predictive models. In this essay, we review Risk Management and its relation with Predictive Modeling. Then, we identify how Predictive Modeling can enhance the Decision Making process in the Risk Appetite, Risk Limiting and Risk Pricing components of Risk Management. Finally, we reflect on the implications of the incorporation of predictive modeling and Big Data for Risk Management.
Banks and Financial Services Companies may be one of the earliest adaptors of Big Data and Machine Learning. Many of their main tasks have been facilitated by predictive equations for decades. Executive decisions have been supported by logistic regressions and demand forecasts for a longer history than in other business areas. Their interactions with clients at a large variety of products and the transactions history provide a fertile generator of data. Credit scoring, fraud detection, anti-money laundering, trading interaction with clients and trading modeling are some of the subjects in which Banks and Financial Companies have used predictive equations for a longer time. Despite the presence of Behavioral Risk Models for a long time, the use of advanced and cutting-edge Predictive Modeling and Big Data Analysis techniques for Risk Management is much more limited. They have been primarily concentrated in a few number of areas, spanning Credit Risk Modelling.
