An Comparative Analysis about KYC on a Recommendation System Toward Agentic Recommendation System

TL;DR

This paper evaluates a novel agentic AI-based recommendation system designed for KYC in finance, comparing its performance across various content types and benchmarking against industry standards using nDCG metrics.

Contribution

It introduces a large-scale agentic recommendation system for KYC, integrating experimental data with theoretical and industry benchmarks across multiple content verticals.

Findings

Enhanced recommendation accuracy with KYC integration

Performance improvements over baseline models

Benchmarking results aligned with industry standards

Abstract

This research presents a cutting-edge recommendation system utilizing agentic AI for KYC (Know Your Customer in the financial domain), and its evaluation across five distinct content verticals: Advertising (Ad), News, Gossip, Sharing (User-Generated Content), and Technology (Tech). The study compares the performance of four experimental groups, grouping by the intense usage of KYC, benchmarking them against the Normalized Discounted Cumulative Gain (nDCG) metric at truncation levels of $k=1$, $k=3$, and $k=5$. By synthesizing experimental data with theoretical frameworks and industry benchmarks from platforms such as Baidu and Xiaohongshu, this research provides insight by showing experimental results for engineering a large-scale agentic recommendation system.