An AI-Driven Approach to Streamline Transaction Banking Operations

Various challenges exist across the entire banking industry, including transaction banking. Among these, the enhancement of cross-border remittances and payments has been identified as a key theme by the G20. In November 2020, the G20 endorsed a roadmap aimed at achieving cross-border payments that are "faster, cheaper and more accessible and transparent," while maintaining the safety and security of such transactions. Targets have been set from the perspectives of "cost," "speed," "transparency," and "access," with deadlines placed between 2027 and the end of 2030 (Figure 1).

(Reference) Financial Stability Board “Targets for Addressing the Four Challenges of Cross-Border Payments”
https://www.fsb.org/uploads/P131021-2.pdf

According to a 2025 survey by Swift (Society for Worldwide Interbank Financial Telecommunication)^*1, the time it takes for a payment instruction to reach the beneficiary bank from the ordering bank generally falls within the target of one hour. On the other hand, it has been pointed out that it takes time for the beneficiary bank to process the transfer and credit the beneficiary's account after receiving such instructions. While factors such as time differences contribute to delays in payment processing, the bottleneck is often manual processing and verification resulting from regulations related to remittances (Figure 2).

The most time-consuming task, in particular, is reporting to the authorities. Processing remittances is becoming increasingly complex due to the need for various verifications and supporting documents depending on the purpose of the transfer, as well as differing rules across sending and receiving countries. While the adoption of remittance systems is progressing, most are limited to functions centered on deposit and withdrawal processing, and in many cases, judgments and verifications based on transaction details are handled manually. This is because, in addition to the difficulty of implementing a system that covers every possible pattern, it is also challenging to continuously keep up with regulatory revisions.

In addition, compliance checks used to be a factor that prolonged payment processing times. However, in recent years, many AI-driven solutions have emerged; in particular, major banks in Europe, the US, and Asia are utilizing machine learning in the fields of AML (Anti-Money Laundering) and fraud detection to reduce false positives and achieve higher detection accuracy. HSBC has successfully reduced false positives by approximately 60% while increasing detection rates by leveraging AI. This not only significantly reduces the time spent on detailed analysis and customer verification for false positives, but also contributes to the sophistication of financial crime prevention itself.
Furthermore, in trade finance, efforts are underway to shorten processing times and reduce errors by using AI to extract data from shipping documents and letters of guarantee, and to perform automatic matching and classification. There is also a shift from paper to digital in trade finance, and with legal frameworks being established and platforms being introduced in major Asian countries, further expansion of AI utilization is expected in the future.

AI excels at pattern recognition, and for routine checks, AML compliance checks can sometimes be completed in a matter of seconds to minutes. In trade finance as well, a 30% to 70% reduction^*2 in man-hours and processing time per transaction  can be expected. An increasing number of banks are adopting a method where AI is utilized for initial work followed by human verification, aiming to reduce time and improve operational efficiency through AI while ensuring the accuracy required in the financial industry.

*1 (Reference) Spotlight on Speed 2025
https://www.swift.com/ja/node/310345

*2 (Reference) Impactsure implements an AIenabled trade finance solution for an Indian private sector bank
https://www.temenos.com/wp-content/uploads/2022/10/Trade-Finance-case-study-by-IBS-Intelligence_IMPACTSURE-TECHNOLOGIES_casestudy.pdf
（Reference）Transform Trade Finance from Paper to Digital Excellence
https://acesw.com/solutions/trade-finance

Advances in AI technology and the standardization of remittance data are opening up new possibilities for utilization in transaction banking. This chapter focuses on regulatory compliance, which is one of the factors affecting processing time in remittance operations, and introduces AI utilization examples.

Remittance operations require verification of various documents and supporting evidence, depending on the purpose of the transfer and regulations of the countries involved. These documents exist in a wide variety of formats, ranging from standardized electronic data to unstructured paper media and complex documents requiring careful scrutiny; many of these involve tasks that are well-suited for AI.

After receiving a remittance instruction from a customer, the AI analyzes the transfer details and attached documents to automatically determine whether supporting evidence needs to be verified, based on each country’s regulations and the bank’s own rules. If it is determined that such verification is necessary, a decision will be made as to whether it should be handled by AI or requires manual processing. For example, in cases where analysis from multiple perspectives is required, manual processing is considered appropriate. On the other hand, for electronic data and standardized documents where the format is clear and the content is easy to verify, AI can automate verification. For transactions determined to be processable automatically by AI, necessary information is extracted from documents such as contracts, invoices, and tax certificates to verify consistency with the purpose of the remittance (Figure 3).

Manually determining the required supporting documents based on the sending and receiving countries and the purpose of the remittance is not only time-consuming but also prone to errors in judgment. Also, in document verification, tasks such as reviewing massive amounts of text such as contracts are far faster when performed by AI than by humans.
Furthermore, even when certain details of a transaction are unclear during each judgment or check, AI can independently investigate it and notify a human as necessary. This can be described as a distinctive feature of AI that greatly differs from conventional systems. For example, if the purpose of the remittance is not specified in a remittance instruction, AI can refer to past transaction data to predict the purpose of the remittance. In checking whether the required documents are complete, it is also possible to scan the folder of received documents, quickly review its contents, and determine in a short time whether all the necessary documents are in place.
Standardized documents are processed using rule-based systems, while for unstandardized documents, judgment accuracy is improved through AI learning that utilizes past data. In cases where judgment is difficult, the AI automatically flags them as "requiring human review," ensuring both efficiency and accuracy. Furthermore, even in cases where human verification is required, AI can classify documents and extract content in advance, which is expected to reduce verification workload.

AI is no longer just a tool for automating specific processes; it is becoming capable of autonomously executing entire workflows by determining the actions required to meet goals and objectives.
However, simply introducing AI will not allow us to fully utilize its advanced capabilities. When introducing AI into remittance transaction processes, greater effectiveness can be expected by establishing mechanisms to digitize and standardize related data such as supporting documents, and by utilizing data accumulated during transaction processing in combination with functions in other areas such as automated report generation, marketing, and improvement of customer satisfaction. To maximize the benefits of AI, it is essential to construct a system scheme for AI to function, develop a data environment that is easy to utilize, and design appropriate governance.
As a partner with strengths in both business expertise and AI technology, ABeam Consulting provides hands-on support to accelerate value creation, from formulating future-oriented business concepts to implementation and operations.

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1. Formulation of AI Utilization Strategy / Data Architecture
2. Data Preparation (including ISO 20022 and existing data)
3. PoC Design and Model Accuracy Verification
4. Business Process Redesign
5. Comprehensive Support Including AI Governance and Operational Rules Design

ABeam Consulting remains committed to contributing to the advancement of operations and value creation for the entire banking sector, including the transaction banking domain, through AI-driven business transformation.