AI Adoption to Combat Financial Crime: Study on Natural Language Processing in Adverse Media Screening of Financial Services in English and Bangla multilingual interpretation

TL;DR

This study evaluates the use of AI-powered NLP for adverse media screening in Bangladeshi financial services, demonstrating high accuracy and highlighting challenges in AI adoption due to technical and regulatory barriers.

Contribution

It introduces NLP-based adverse media screening tailored for multilingual contexts in Bangladesh, emphasizing its effectiveness and the challenges faced in AI integration.

Findings

NLP achieves approximately 94% accuracy in adverse media detection.

AI technology is underutilized in Bangladesh compared to global adoption.

Main barriers include lack of expertise, high costs, and regulatory uncertainties.

Abstract

This document explores the potential of employing Artificial Intelligence (AI), specifically Natural Language Processing (NLP), to strengthen the detection and prevention of financial crimes within the Mobile Financial Services(MFS) of Bangladesh with multilingual scenario. The analysis focuses on the utilization of NLP for adverse media screening, a vital aspect of compliance with anti-money laundering (AML) and combating financial terrorism (CFT) regulations. Additionally, it investigates the overall reception and obstacles related to the integration of AI in Bangladeshi banks. This report measures the effectiveness of NLP is promising with an accuracy around 94\%. NLP algorithms display substantial promise in accurately identifying adverse media content linked to financial crimes. The lack of progress in this aspect is visible in Bangladesh, whereas globally the technology is already being used to increase effectiveness and efficiency. Hence, it is clear there is an issue with the acceptance of AI in Bangladesh. Some AML \& CFT concerns are already being addressed by AI technology. For example, Image Recognition OCR technology are being used in KYC procedures. Primary hindrances to AI integration involve a lack of technical expertise, high expenses, and uncertainties surrounding regulations. This investigation underscores the potential of AI-driven NLP solutions in fortifying efforts to prevent financial crimes in Bangladesh.