In 2024, significant governmental reports have underscored the pivotal role of technological innovation in driving economic growthThe "AI Plus" strategy stands out as a guiding beacon for various industriesAmong the critical players in this transformation is the Bank of Communications, a prominent state-owned bank in ChinaThis institution is aligning itself with the current trends by emphasizing the creation of an "AI new business card," integrating artificial intelligence (AI) applications across its diverse business linesThe bank is strategically focusing on areas such as cost reduction, risk management, customer experience enhancement, and value creation through AI.

The personal finance segment is leveraging AI technologies like precise marketing and online risk management to address customer needs effectively while maintaining robust risk controls that facilitate profit generation

Meanwhile, the corporate banking side enhances its operations through innovative technologies such as supply chain marketing and media sentiment monitoringThis comprehensive overhaul has significantly reduced reliance on manual processes, thus driving down costsInterestingly, despite these advancements, the interbank business, one of the three pillars of banking operations, is lagging in its AI journey.

Recognizing the pressing need for adaptation in this digital era, the Bank of Communications is set to bolster its technological investments in the interbank segment, learning from other successful divisionsThis initiative aims to harness AI technologies to improve service quality and efficiency, contributing to the financial sector's stability and prosperous development.

As the capital market evolves and regulatory frameworks mature, the interbank business is increasingly focusing not just on service provision but also on enhancing internal operational efficiency

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One of the primary challenges is the vast amounts of market data that have yet to be fully realized as potential resourcesThe sheer volume of manual operations in numerous business processes inevitably raises costs and risks.

With the rise of general-purpose model technologies, the landscape is shifting, offering new opportunities for the interbank business to catch upThe advent of ready-to-use AI capabilities could enhance various aspects of banking operations, from trading to market research, effectively unburdening many labor-intensive tasks and laying the groundwork for sustained value creationIn light of these trends, it is crucial to explore the existing AI applications within the context of the Bank of Communications, particularly in interbank operations.

One notable application is in the currency trading processes between banks, specifically in credit borrowing and bond repurchase, which serve as vital funding mechanisms for liquidity management and play a key role in implementing central bank monetary policy

However, traditional trading processes have faced specific challenges such as a reliance on manual labor for determining trading parameters and the need for traders to continually check counterparties' credit lines and collateral availability, leading to extensive redundant work.

The Bank of Communications has addressed these issues by employing natural language processing (NLP) and multi-turn dialogue technologies to automate trading workflowsThe introduction of the "Interbank Funds Trading Intelligent Robot" exemplifies this innovation, as it assists traders in identifying counterparties with greater accuracy and speedBy automating the collection of trading parameters, the system enhances trade execution efficiency and helps alleviate current bottlenecks in trading volumes.

The intelligent robot also enables a centralized trading console, allowing for real-time tracking and querying of all trades and credit utilization

This setup facilitates early detection and resolution of issues before formal transactions are completed, relying on core technologies that enhance contextual understanding for improved decision-making.

The deployment of this intelligent trading robot has significantly improved traditional processes by minimizing repetitive tasks and increasing efficiencyThe robot can quickly discern trading particulars and return pertinent information to the central console, which in turn provides real-time insights into position changes, ultimately promoting better risk management and agile decision-making.

In the area of commercial paper trading, another critical short-term financing tool, challenges have also surfacedTraditional methods of quoting and negotiating have proven inefficient and costly, involving extensive manual efforts, while rapid market fluctuations demand real-time updates on pricing information.

To tackle these issues, the Bank of Communications developed an intelligent pricing system for commercial paper discounts, rooted in element extraction algorithms

This employs NLP techniques to streamline the pricing process by identifying crucial elements such as type, maturity, and transaction direction from the vast pool of messages regarding discount tradesTraining was conducted using pre-trained language models augmented with business-specific data to enhance the quotation efficiency of discounted bills.

This element extraction model significantly improves the analysis and extraction of information from numerous messages related to commercial paper discount transactions, effectively addressing traditional inefficiencies and digitizing pricing information flowFuture enhancements will leverage big data analytics for automating document reviews and assessments to further reduce costs and increase overall efficiency.

Custodial services in the banking sector also stand to benefit from AI-driven efficienciesThese services play a vital role in building investor confidence and supporting the healthy development of capital markets.

One significant step in this domain is the "Client Instruction Parsing" process, crucial in executing transactions for fund transfers and payments

Traditionally, customers often send instructions via paper documents, requiring manual extraction of vital details by transaction teams, which is labor-intensive and prone to delays.

Turning to AI, the Bank of Communications has utilized NLP technology and historical client instruction data to train models capable of automatically parsing instructions for better accuracyBy transforming the existing flow into a more streamlined dynamics—moving from purely manual verification to an AI-supported process—the efficiency in transactions has seen a considerable boost, allowing for enhanced transaction capability and supervisory functions.

Furthermore, in investment banking, effective research capabilities are paramountThe surge of unstructured data necessitates better strategies for extraction and utilizationTo address this, the Bank has built a prototype system utilizing a "knowledge graph + NLP" framework to automate the extraction of industry knowledge from public sentiment data.

This system provides a structured approach to identifying relationships and entities involved in market events, thereby enhancing research frameworks through the visualization and correlation of key data points, leading to improved risk management and transparency in investment decisions.

In summary, the integration of AI technologies such as NLP, knowledge graphs, and deep learning into interbank operations is transformative

These innovations not only optimize trading, manage risks, and enhance client interactions but also empower banks to remain competitive in a dynamic market landscape.

The potential and value of AI within interbank operations, exemplified by such initiatives at the Bank of Communications, signify a substantial leap forward in banking practicesThis digital evolution is not merely about automation; it represents a critical transition to a collaborative future where AI facilitates a seamless integration of various banking services, thereby significantly improving operational efficiency.

Moreover, as the banking sector embraces this AI renaissance across a myriad of applications—from international banking operations to regulatory compliance—the promise of a more efficient, secure, and intelligent financial system becomes increasingly tangibleAs AI technologies continue to evolve, they will undeniably propel banking towards high-quality development and reshape the future of financial operations.