AI-based Personalization and Trust in Digital Finance

TL;DR

This paper reviews AI's role in digital finance personalization and trust, identifies research gaps, and develops an AI-based credit risk detection model with high accuracy to enhance personalized financial services.

Contribution

It systematically reviews existing research, identifies key gaps, and proposes a novel AI-based credit risk detection model using multiple classifiers with superior performance.

Findings

Random Forest classifier achieved ~89% accuracy

The model outperforms existing credit risk detection methods

AI enhances personalization and trust in digital finance

Abstract

Personalized services bridge the gap between a financial institution and its customers and are built on trust. The more we trust the product, the keener we are to disclose our personal information in order to receive a highly personalized service that maximizes consumer value. Artificial Intelligence (AI) can help financial institutions tailor relevant products and services to their customers as well as improve their credit risk management, compliance, and fraud detection capabilities by incorporating chatbots and face recognition systems. The present study has analyzed sixteen research papers using the PRISMA model to perform a Systematic Literature Review (SLR). It has identified five research gaps and corresponding questions to analyze the present scenario. One of the gaps is credit risk detection for improved personalization and trust. Finally, an AI-based credit risk detection model has been built using four supervised machine learning classifiers viz., Support Vector Machine, Random Forest, Decision Tree, and Logistic Regression. Performance comparison shows an optimal performance of the model giving accuracy of ~89%, precision of ~88%, recall of ~89%, specificity of ~89%, F1_score of ~88%, and AUC of 0.77 for the Random Forest classifier. This model is foreseen to be most suitable for envisaging customer characteristics for which personalized credit risk mitigation strategies are particularly effective as compared to other existing works presented in this study.