How Generative AI restructure the electronic bank guarantee process

An Electronic bank guarantee document is a digital record or electronic document that represents the Electronic bank guarantee issued by a financial institution. It contains all the essential details and terms of the guarantee, including the beneficiary's information, the specified amount, the conditions under which the guarantee can be invoked, and the expiry date. E-bank guarantee documents serve as the electronic counterpart to traditional paper-based bank guarantee documents and are used to facilitate secure and efficient transactions in various industries, especially in international trade and business contracts.

Generative AI in the field of banking refers to the application of artificial intelligence, specifically generative models, to create, process, or enhance various aspects of banking operations. Generative AI systems, often powered by deep learning techniques, can generate text, extract text , images, and other data, making them valuable for tasks such as automating customer support with chatbots, generating financial reports, enhancing fraud detection algorithms, and even assisting in the creation of personalized financial plans. This technology contributes to increased efficiency, improved customer experiences, and more accurate decision-making in the banking industry.

In recent years, the finance and banking industry has undergone a significant transformation due to advances in technology. One such innovation that is going to restructure the industry is Generative AI. This advanced technology is being used to simplify and enhance various financial processes, including the processing of Electronic bank guarantee and also can be used in any document extraction ,OVD extraction and KYC solutions . This article might be helpful for you to explore the scope and potential of Generative AI in restructuring the handling of electronic bank guarantees.

Benefits of using Gen AI in Banking Sector

Our AI model is a prompt based technology where you can fetch data from a document using simple prompts. Our model is also capable of performing the data extraction using difficult prompts or chain of prompts. That means you can use multiple prompts to extract accurate data from documents.

Following are the key benefits of using Generative AI in banking:

• Personalized Customer Experiences
• Improved Fraud Detection
• Enhanced Risk Assessment
• Automated Document Generation
• Enhanced Compliance and Regulation
• Cost Savings and Efficiency
• Real-Time Decision Support
• Risk Management
• Improved Customer Engagement

Why Should you Integrate Gen AI with eBG Processing

Our Generative AI model offers a range of benefits to the processing of electronic bank guarantee, transforming various aspects of operations, customer experiences, and decision-making processes as discussed above in various use cases.

Digital bank guarantee generation

Generative AI models work by learning the underlying patterns and relationships in the data they are trained on. In the case of eBGs, the model will learn the different types of eBGs that exist, the different terms and conditions that are typically included, and the legal language that is required. Once the model has been trained, it can be used to generate new eBGs that are consistent with the data it has been trained on. AI-driven natural language processing (NLP) models have the capability to support banks in comprehensively analyzing complex regulatory and compliance documents. This valuable assistance helps banks in staying up-to-date with the latest regulatory mandates, ultimately mitigating the potential for non-compliance. To generate a new electronic bank guarantee (eBG), the model will receive essential details like the guarantee amount, beneficiary information, and the expiration date. Subsequently, using this data, the model will generate a comprehensive eBG document that includes all the required legal language. Generative AI has the potential to revolutionize the way that eBGs are generated and processed. By automating the process of generating eBGs, generative AI can save banks and businesses time and money. It can also help to improve the accuracy and consistency of eBGs.

Automated eBG Verification

Generative AI automates the verification of documents and compliance, significantly reducing the time required for this critical step. It ensures accuracy and eliminates the risk of human errors.

• Extracting data from eBG: Gen AI models can be used to extract data from digital bank guarantee documents and legal documents with the help of prompt. This data can then be used to verify the authenticity of the document and ensure that it complies with all relevant regulations.

Identifying the important clauses

Generative AI has the potential to restructure the way that eBGs are analyzed and understood. By automating the process of identifying important clauses, generative AI can save businesses and individuals time and money. It can also help to improve the accuracy and consistency of eBG analysis. It can also be used to identify important clauses in eBGs that are written in different languages. This can be helpful for businesses that operate internationally or that need to review eBGs from foreign banks.

Generative AI model can help in identifying the important clauses in an electronic bank guarantee (eBG) by:

• Understanding the context of the eBG: Generative AI models can be trained to understand the context of an eBG by analyzing its content, structure, and metadata. This allows the model to identify the clauses that are most relevant to the overall purpose of the eBG.
• Identifying keywords and phrases: Generative AI models can be used to identify keywords and phrases that are associated with important clauses in an eBG. This can be done by training the model on a dataset of eBGs that have been annotated with the important clauses.
• Analyzing the relationships between clauses: Generative AI models can be used to analyze the relationships between clauses in an eBG. This allows the model to identify clauses that are dependent on each other, as well as clauses that are inconsistent with each other.

Once the generative AI model has identified the important clauses in an eBG, it can provide this information to the user in a variety of ways. For example, the model could generate a summary of the important clauses, or it could highlight the important clauses in the eBG itself.

• Identifying the amount of the guarantee: This is one of the most important clauses in any eBG, as it specifies the maximum amount that the bank will pay to the beneficiary in the event of a default.
• Identifying the beneficiary of the guarantee: This is the party who will receive the payment from the bank in the event of a default.
• Identifying the expiry date of the guarantee: This is the date after which the guarantee is no longer valid.
• Identifying the conditions under which the guarantee can be called: These conditions will vary depending on the type of eBG, but they may include things like a default by the principal obligor or a failure to meet certain performance criteria.
• Identifying the exceptions to the guarantee: These are situations in which the bank is not obligated to make a payment under the guarantee.

Identifying the missing clauses

Generative AI has the potential to revolutionize the way that eBGs are reviewed and validated. By automating the process of identifying missing clauses, generative AI can save banks and businesses time and money. It can also help to improve the accuracy and consistency of eBG review.

Here are some specific examples of how generative AI can be used to identify missing clauses in an eBG:

• Identifying the missing beneficiary information: Generative AI can be used to identify the missing beneficiary information in an eBG, such as the name and address of the beneficiary. This information is essential for the eBG to be valid and enforceable
• Identifying the missing guarantee amount: Generative AI can be used to identify the missing guarantee amount in an eBG. This information is essential for the eBG to be valid and enforceable.
• Identifying the missing expiry date: Generative AI can be used to identify the missing expiry date in an eBG. This information is essential for the eBG to be valid and enforceable.
• Identifying the missing signatures: Generative AI can be used to identify the missing signatures in an eBG, such as the signatures of the bank and the guarantor. These signatures are essential for the eBG to be valid and enforceable.

eBG extraction

Through prompt-based methods, generative AI enhances data extraction from electronic bank guarantees. The AI model can intelligently develop responses that include the extracted data by providing it with a specific prompt including specifics about the guarantee, such as the beneficiary's information, guarantee amount, and expiry date. The AI uses natural language processing to understand and interpret the prompt, resulting in accurate and structured data outputs from the electronic bank guarantee documents. This method not only speeds up the extraction process, but it also maintains a high level of precision, decreasing the need for manual data entry and reducing errors.

eBG summarization

Generative AI assists in summarizing electronic bank guarantees by utilizing its natural language processing capabilities. It can analyze the content of these documents, identify key information such as the guarantee amount, beneficiary details, expiry date, and any critical terms and conditions. The AI then generates concise and coherent summaries that capture the essential elements of the electronic bank guarantee, making it easier for stakeholders to grasp the document's significance and contents swiftly. This automated summarization process not only saves time but also ensures that critical details are not overlooked, improving the overall efficiency and accuracy of summarizing these complex financial documents.

The summary includes essential agreement particulars, including party names, contract signing date, and agreement duration. Additionally, it should delineate critical terms and conditions, such as payment structure and amount, delivery details, methodology, and any noteworthy provisions.

Other use cases

1. Real-Time Tracking

With generative AI, all parties involved can access real-time updates on the status of the bank guarantee. This transparency enhances trust and reduces disputes.

2. Cost-Efficiency

Generative AI eliminates the need for extensive paperwork and physical storage, resulting in cost savings for financial institutions and their clients.

3. Enhanced Security

AI-driven solutions employ robust security measures to protect sensitive data, ensuring the integrity of electronic bank guarantees.

Overview of the Global Bank Guarantee Market

The global bank guarantee market has seen strong growth due to its importance in ensuring secure financial transactions across borders. Bank guarantees provide financial security for importers, exporters, and businesses, ensuring contract fulfillment and reducing default risks. This overview highlights the main trends, market segments, opportunities, and challenges shaping the future of the bank guarantee market.

Market Growth

In 2020, the bank guarantee market was valued at $20.28 billion and is projected to reach $41.48 billion by 2030, growing at a CAGR of 7.3%. The rise in international trade and the adoption of digital banking solutions are key drivers of this growth.