Purpose The objective of this study is to provide a systematic review of the literature on
artificial intelligence (AI) in customer-facing financial services, providing an overview of …

Quantum computers are expected to surpass the computational capabilities of classical
computers during this decade and have transformative impact on numerous industry sectors …

In recent years, research has found that in many credit risk evaluation domains, deep
learning is superior to traditional machine learning methods and classifier ensembles …

Credit risk assessment is at the core of modern economies. Traditionally, it is measured by
statistical methods and manual auditing. Recent advances in financial artificial intelligence …

The credit risk prediction technique is an indispensable financial tool for measuring the
default probability of credit applicants. With the rapid development of machine learning and …

The banking, financial services, and insurance (BFSI) sector is one of the earliest and most
prominent adopters of artificial intelligence (AI). However, academic research substantially …

Can satisfactory explanations for complex machine learning models be achieved in high-risk
automated decision-making? How can such explanations be integrated into a data …

Most credit scoring algorithms are designed with the assumption to be executed in an
environment characterized by an automatic processing of credit applications, without …

We have analyzed 127 publications for this review paper, which discuss applications of
Reinforcement Learning (RL) in marketing, robotics, gaming, automated cars, natural …

Explainable AI (XAI) is widely viewed as a sine qua non for ever-expanding AI research. A
better understanding of the needs of XAI users, as well as human-centered evaluations of …