Artificial Intelligence in Predictive Analysis of Insurance and Banking

Abstract

Investments require a study of the market and comparative analysis for clear directions. Portfolio management plays a very important role in the investment sector. Correlations and predictive analysis have been prominent in the field backed by data science. The concept has an extension to anomaly detection in the insurance and banking sectors as well. Predictive analysis provides a platform for developing relational models in proposing data collection, analysis, investment customization, and practices for investors. These techniques are now being apprehended largely by artificial intelligence (AI) to optimize their characteristic properties and confirm the correlational patterns in the investment sector. Recently, effective methods like genetic algorithms have been used to understand the characteristic patterns of data that have contributed much to the popularity in this field of AI. Thus, studies have exhibited a commendable integration between AI and predictive analysis, thus enabling algorithmic models for establishing a profound role for portfolio optimization and anomaly detection. In this chapter, we explore several modalities of data science in the field of predictive analysis; optimization techniques; and anomaly detection in insurance, banking, and investment sectors.