# AI-based Personalization and Trust in Digital Finance

**Authors:** Vijaya Kanaparthi  
**arXiv:** [2401.15700](https://arxiv.org/abs/2401.15700)  
**Date:** 2024-01-28  
**Summary page:** https://www.summarizepaper.com/en/paper/2401.15700v1/

## Key points

- Trust is crucial in personalized financial services to bridge the gap between customers and financial institutions.
- More trust leads to customers being more willing to share personal information for tailored services that enhance consumer value.
- Financial institutions can leverage Artificial Intelligence (AI) to customize products and services, improve credit risk management, compliance, and fraud detection using tools like chatbots and face recognition systems.
- A Systematic Literature Review (SLR) identified five key research gaps related to personalized financial services.
- One research gap focused on credit risk detection as a means to improve personalization and trust within financial services.
- An AI-based credit risk detection model was developed using four supervised machine learning classifiers: Support Vector Machine, Random Forest, Decision Tree, and Logistic Regression.
- The Random Forest classifier exhibited optimal results with an accuracy of approximately 89%, precision of around 88%, recall of about 89%, specificity of 89%, F1_score of 88%, and an AUC of 0.77.
- This model is effective in predicting customer characteristics for implementing personalized credit risk mitigation strategies compared to existing works in this field.
- Incorporating advanced AI technologies into financial services can enhance customer experiences while strengthening trust and security measures in operations.

## Summary

In the realm of personalized financial services, trust plays a crucial role in bridging the gap between customers and financial institutions. The more trust we have in a product, the more willing we are to divulge personal information for tailored services that enhance consumer value. Leveraging Artificial Intelligence (AI), financial institutions can customize products and services for customers while also bolstering credit risk management, compliance, and fraud detection through tools like chatbots and face recognition systems. To delve deeper into this subject, a Systematic Literature Review (SLR) was conducted using the PRISMA model to analyze sixteen research papers. This review identified five key research gaps and posed corresponding questions to better understand the current landscape. One such gap focused on credit risk detection as a means to improve personalization and trust within financial services. To address this gap, an AI-based credit risk detection model was developed utilizing four supervised machine learning classifiers: Support Vector Machine, Random Forest, Decision Tree, and Logistic Regression. Performance comparison revealed that the Random Forest classifier exhibited optimal results with an accuracy of approximately 89%, precision of around 88%, recall of about 89%, specificity of 89%, F1_score of 88%, and an AUC of 0.77. This model is envisioned as particularly effective in predicting customer characteristics for implementing personalized credit risk mitigation strategies compared to existing works in this field. By incorporating advanced AI technologies into financial services, institutions can not only enhance customer experiences but also strengthen trust and security measures in their operations.