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Automation of Trade Finance Processing & Trade-Based Money Laundering (TBML) Pricing Model

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Automation of Trade Finance Processing & Trade-Based Money Laundering (TBML) Pricing Model

Posted by: admin
Category: Services

Financing global trade sits at the heart of the relationship between banks and corporates. Trade Finance is one of the huge volumes: by 2026, trade flows are predicted to reach US$24 trillion.1
Given this growth trajectory, banks have identified trade finance as a critical pillar of their future business strategy. However, market growth alone is not the only ingredient for success. To succeed, banks must position themselves as leaders in the digitization of trade finance.

Because only through maximized digitization, automated processing and comprehensive audit capabilities in a multi-step, multi-part processing ecosystem like trade finance can financial institutions be competitive enough to pick the fruits of market growth.

State Bank of Pakistan in order to stop Money Laundering has issued guidelines for Banks. The objective of State Bank of Pakistan is to strengthen the trade-related Anti Money Laundering / Combating Financing of Terrorism regime and conserve foreign exchange.

How can financial institutions prepare for this digital journey? What are the challenges of digitizing the front end of trade finance? How will the “digital worker” mimic, sense, comprehend and act like a human? To answer these questions Al Intisar Solutions has explored how combining proven Market Technology with Artificial Intelligence and Machine learning can help Banks master key challenges in Trade Finance.


With global trade projected to grow, trade finance continues to be a significant pillar of banks’ business model. According to a 2018 ICC Global Survey, revenues from trade finance are expected to grow at an annual rate of up to 6%. Trade finance revenues could be as high as US$48 billion. The outlook is promising, but organizations need first to overcome several challenges to optimizing their trade finance business to maximize profitability. It is especially significant since trade finance is mainly paper-based and labor-intensive and suffers from fragmented information. The lack of standardization is costly, time-consuming and error-prone and further squeezes already tight margins.

With technology as a key enabler, the trade finance industry is facing a major technology overhaul. Banks are in a race to go digital. While many are trying to digitize their front end,

The use of Robot Process Automation (RPA) technology seems to be the most straightforward answer, especially since the technology has become a universal enabler in banks these days. RPA is best described as a business process tool to automate manual, intensive, repetitive and rule-based tasks. Those who have successfully implemented RPA elsewhere in their banking operations will be best placed to apply it to trade finance

Author: admin

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