Industry
Banking
Banking Services Transformation: Unlocking Exponential Value
Highlights
- Trade finance is inherently complex, giving room for fraud and financial crime. Manual processes in fraud checks further complicate the situation.
- Traditional fraud management methods are inadequate to keep pace with the scale and complexity of global trade, especially in the wake of rapid digitalization and exponentially expanding transaction volumes.
- Adopting GenAI can greatly reduce manual handoffs involved in fraud check processes in trade finance, paving the way for superior fraud management.
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Introduction
Commercial banks are the cornerstone of global trade, facilitating transactions between importers and exporters through financial instruments such as letters of credit and bank guarantees.
These financial tools provide essential working capital and mitigate risks for importers and exporters by assuring payment to exporters and ensuring delivery of goods for importers.
However, trade finance is inherently complex with significant potential for financial crime. Commercial banks involved in trade finance operations have to contend with double financing, trade-based money laundering, the potential for the involvement of sanctioned or boycotted entities, and deceptive shipping practices to evade sanctions. In addition, manual processes and extensive paper-based documentation give rise to errors, inefficiencies, and delays while also hindering real-time monitoring to enable proactive fraud detection.
All these obstacles come together to impede commercial banks’ efforts to improve fraud risk management. Digital technologies coupled with generative artificial intelligence (GenAI) tools can help overcome these challenges by automating manual processes, improving real-time monitoring, enhancing data analysis and record management, and strengthening fraud controls.
Challenges to effective fraud management
Commercial banks face a plethora of challenges in detecting and preventing fraud in trade finance.
Let us examine the challenges in more detail.
When a party or parties to a trade finance transaction engage in illegal activities such as money-laundering, terrorist financing, fraud, organized crime, or racketeering, banks are exposed to financial, reputational, and compliance challenges. The process of verifying whether a party complies with legal requirements is performed manually, and decisions to proceed are based on human judgment and experience, which is error-prone and time-consuming.
Traditional manual processes are often insufficient to detect duplicate transactions or multiple submissions of the same application. With limited system checks available to identify duplicate transactions, financial institutions remain vulnerable to loss and reputational damage.
Money laundering checks are an integral part of trade finance operations. However, existing processes are manual--for example, processing huge sets of documents, examining transaction history to pinpoint unusual patterns, and, evaluating customer profiles to identify suspicious deviations from past activities, demand human judgment. Manual processes enhance the risk of errors and increase transaction turnaround, resulting in operational inefficiencies and bottlenecks.
In trade finance, tracking shipping routes is essential for proactive risk management. Real-time tracking helps identify potential theft and delays while enabling accurate calculation of costs and duties, facilitating timely delivery of goods and improving financial planning. Currently, banks manually monitor the route actually travelled by the ship and compare it with the one mentioned in the bill of lading to proactively flag deviations that may indicate theft, or a ship docking at a port subject to sanctions. But manual tracking is error-prone and time-consuming with the potential for regulatory violations and penalties, reputational loss, and poor customer experience.
For financial institutions, sanctions screening is crucial to avoid entering into transactions with blacklisted individuals and entities. This enhances regulatory compliance and prevents penalties associated with non-compliance. Financial institutions manually identify the entities to be screened and evaluate disambiguated matches or outcomes, which can result in omissions and significant penalties for non-compliance.
Financial institutions must comply with anti-boycott regulations of the US. The regulations prohibit US entities from complying with or supporting boycotts mandated by foreign governments against countries friendly to the US. The regulation bars commercial banks involved in trade finance from:
- Including boycott related terms and conditions in letters of credit.
- Agreeing to refuse to do business with boycotted entities.
- Providing information about business relationships with boycotted entities.
Human intelligence and judgment play a role in identifying and reviewing boycott clauses and reporting issues. An error can expose commercial banks to significant legal and financial risks.
Figure 1: Manual fraud checks in trade finance
Traditional methods for combating fraud such as manual checks, rule-based systems, and periodic audits often fail to keep up with the scale and complexity of contemporary trade (see Figure 1). With the acceleration of digitalization and the increase in transaction volumes, the industry must embrace adaptive, intelligent, and AI-driven solutions to recognize potential red flags, mitigate risks, and comply with regulations.
Putting it all together
Improving fraud detection and prevention in trade finance has become imperative.
By detecting anomalies and predicting potential risk events and arming commercial banks with the intelligence necessary to proactively prevent fraud, GenAI has the potential to greatly enhance fraud management in trade finance. Let us examine how GenAI can help mitigate the challenges to efficient fraud management.
Banks scan media and other sources for negative mentions or associations related to individuals and entities involved in trade transactions. This helps identify parties with a history of fraud, financial crime, or other suspicious activities. Using GenAI tools with natural language processing (NLP) capabilities, banks can scan various sites, analyze and interpret information about parties to trade finance transactions, and summarize negative or adverse mentions. Such constant monitoring provides human analysts with a continuous flow of intelligence in real-time, helping them understand key issues that influence decisions, enhancing compliance and due diligence processes, and reducing false positives.
GenAI solutions bring the capability to continuously monitor multiple parameters such as transaction currency and amounts, reference numbers, party details, shipping data, and application timestamps to identify matches or anomalies with remarkable accuracy. When the system detects a potential duplicate, it can trigger instant alerts for further investigation, enabling swift action. Furthermore, these solutions can integrate seamlessly with existing trade finance platforms, reducing operational overhead and minimizing manual intervention. Systemic intervention improves duplicate transaction checks by enabling more effective permutations and combinations in search.
Over and under-invoicing are common in trade-based money laundering (TBML). Financial institutions can leverage GenAI to systematically compare the price of goods in a transaction against prevailing market rates to quickly assess whether the invoiced values align with real-world expectations. A deviation range can be configured into the solution to flag variations from the acceptable threshold which will help identify suspicious transaction. GenAI tools with NLP capabilities can help compare the price in the invoice with those listed in trusted sources. Machine learning (ML) can detect under- or over-invoicing by analyzing vast amounts of transaction data to identify deviations from historical patterns. This proactive approach not only strengthens anti-money laundering (AML) defenses but also increases transparency and reduces reliance on human judgment.
GenAI can flag instances where the goods involved in a transaction do not align with the line of business of the parties involved, providing an additional layer of validation and reducing the risk of suspicious activities. For instance, if a grocery retailer imports substantial quantities of laptops or mobile phones, this is a red flag as these products are not aligned with the core business activity—GenAI and ML tools can rapidly identify such anomalies. They can also examine historical trends to identify deviations that may require further investigation to detect possible money-laundering.
Commercial banks currently rely on the shipping data in the bill of lading and manually check if the routes are accurate. Using GenAI tools can be much faster and more accurate in validating whether the reported routes align with expected shipping lanes, port calls, and historical shipping data. GenAI can flag inconsistencies such as unexpected detours, unscheduled port stops, or discrepancies between declared and observed routes in real-time, which could be indications of fraud.
GenAI can automate the identification of entities or countries for sanction checks. Furthermore, in the event of a match, the disambiguation process can be automated through the application of business rules and AI, minimizing false positives and ensuring that legitimate matches are handled with the highest level of diligence. This approach minimizes human intervention and supports operations in making well-informed decisions.
GenAI tools can be used to automatically identify clauses in letters of credit or bank guarantees that do not comply with the US anti-boycott regulations. This will facilitate timely reporting of such clauses in accordance with US regulatory requirements, thereby avoiding compliance risks and penalties. Commercial banks must leverage AI tools to build a database of previous violations along with transaction details, legalese, actions taken, and the rationale provided. This will enable a comparison of a specific clause with the database to check if there is a precedent and how it was handled, which will facilitate informed decisions.
Figure 2: AI-backed fraud checks in trade finance
It is indisputable that incorporating ML and GenAI-backed solutions can help transform fraud management in trade finance (see Figure 2). However, while some commercial banks have begun this transition, a majority still operate with a hybrid model comprising both automated and manual processes.
Commercial banks must rapidly shift to GenAI solutions to enable straight-through processing and reduce manual intervention in turn enhancing the efficiency of the various checks needed to prevent fraud and driving intelligent decisions in trade finance. It is essential, however, to ensure their AI initiatives align strategically to optimize impact and returns on investment.
However, a word of caution: GenAI can introduce new security vulnerabilities which could result in data leakage or expose firms to cyber-attacks, raising the cyber threat quotient. Strong security controls are therefore imperative to protect sensitive data. GenAI output is also susceptible to bias, hallucinations, and misinformation. Using GenAI reinforced by retrieval-augmented generation (RAG) frameworks along with diverse data followed by regular testing can help overcome bias in AI models. Banks must ensure ethical AI use by designing robust frameworks, policies, and accountability systems. Educating employees on ethical and secure AI use can prevent accidental misuse and build a culture of responsible AI adoption. Additionally, comparing the results of ML with those generated by GenAI, as well as historical trends, can help build confidence in the solution.
A worthwhile journey
For commercial banks, the cost of using manual processes to control and manage the risk of trade fraud can be extremely stiff.
The annual spends on risk, compliance, as well as sanctions and AML checks run into millions of dollars. Despite this, the impact of trade-based financial crime on the global economy runs into trillions every year. Furthermore, banks have to contend with penalties for non-compliance, reputational loss, and increased regulatory scrutiny, compromising their competitive position.
Another issue plaguing banks is the increasing sophistication of fraudsters and their use of advanced digital tools to exploit security vulnerabilities in banking systems and processes to commit trade fraud. The way forward for commercial banks lies in proactive fraud risk management, which will require banks to continuously adapt to keep pace with fraudsters. Consequently, for commercial banks, the use of AI powered predictive tools to detect trade fraud is no longer a nice-to-have but an urgent need. At the same time, seamlessly integrating these tools into their IT landscape can be challenging. However, the benefits far outweigh the effort and banks must act quickly to steal a march over their peers. For trouble-free integration and smooth operations, commercial banks must consider partnering with a service provider with the requisite domain and technology expertise as well as implementation experience, after a well-rounded market analysis.