Sequantis LT and Lingua Custodia provide a 100% integrated solution

A solution for the creation and translation of multilingual regulatory documentation.

Regtech Sequantis LT and Lingua Custodia combine their technological and financial data processing capabilities to offer a complete reporting service to asset managers.
Nicolas Fournier, CEO of Sequantis LT, and Olivier Debeugny, CEO of Lingua Custodia talk about this partnership.

Why integrate a machine translation solution with reporting services?

NICOLAS FOURNIER: New regulatory requirements have an exponential impact on the number of reports that need to be produced. At the same time, there are unresolved issues with adapting documents to distribution networks. This is having an additional impact on the volume of reports that need to be produced.
Faced with this mass of documents, reliable machine translation solutions are an essential accelerator for the teams in charge of producing these documents.

What does this integration involve?

OLIVIER DEBEUGNY: We apply Machine Learning technology to financial linguistics to develop intelligent machine translation engines. They are directly connected via API to the Sequantis LT platform. Users of the platform can define what they need according to the types of documents (Prospectuses, KIIDs/KIDs, …) and the languages required.

What are the challenges and benefits of this integration?

NICOLAS FOURNIER: The advantage is obvious, offering an all-in-one solution, in a single platform that each client uses according to their needs. Obviously, the challenge is the quality of the final document, and therefore the time needed to proofread/check each document produced.

Integrated translation, the future for platforms?

OLIVIER DEBEUGNY: Reporting platforms have become essential partners for asset managers. Their development has enabled significant economies of scale (in terms of time and cost) in recent years. We are making this essential service more fluid and efficient by providing the last segment of process automation (the language).

What’s next?

NICOLAS FOURNIER: In addition to PRIIPS and MIFID 2 reporting, we are currently rolling out SFDR reporting – pre-contractual and periodic – to offer all documents in a single platform. But the SFDR regulation, although due by the end of 2021, is still in the consultation phase, so changes are expected. The same goes for the PRIIPS regulation, for which we do not yet have the final version to be implemented at the end of the year!
OLIVIER DEBEUGNY: We will soon be launching a new translation engine specialising in Socially Responsible Investment and ESG criteria. An additional asset for Sequantis LT clients in the context of the SFDR regulation!

Two specialised machine translation engines are now available in French <> Spanish.

NEW Products: Two specialised machine translation engines – LC Finance and LC Fund Factsheet – are now available in French <> Spanish.

They are specifically designed to translate financial news/market reviews and investment comments.

Lingua Custodia is constantly working on expanding its range of ultra-specialised machine translation engines to satisfy all its client’s needs.

Want to learn more? Contact us!

A propos de Lingua Custodia

Lingua Custodia – the guardian of the language in Latin – is a Fintech Company expert in “Machine Learning” applied to financial linguistics. With offices in France and Luxembourg, it offers over a hundred specialised translation engines available in nine languages (German, English, Chinese, Spanish, French, Italian, Japanese, Dutch, and Portuguese). Its clients include the largest international institutions. The company is labeled by Finance Innovation since 2014 and is a founding member of France Fintech since 2015. In 2018, it joined the LHoFT (Luxembourg House of Financial Technology) and the Village by CA. In 2019, it became a member of Techquartier (Frankfurt). Since 2020, it is part of the Réseau des Entreprises de Défense (RED) of the French Ministry of Defence.

Generative AI in the Financial Industry

Artificial Intelligence (AI) has transformed various industries, and the financial sector is no exception. One fascinating branch of AI is generative AI, which involves the creation of new data based on existing patterns. In this article, we will explore the applications, benefits, challenges, and future trends of generative AI in the financial industry.

Introduction

Generative AI refers to the use of machine learning models to generate new data that resembles a given dataset. These models are trained on vast amounts of existing data and learn the underlying patterns to create new samples. The financial industry can greatly benefit from generative AI, leveraging its capabilities to improve operations, decision-making, and customer experiences.

Applications of Generative AI in Finance

Fraud Detection and Prevention

Generative AI can play a crucial role in fraud detection and prevention in the financial industry. By analyzing large datasets of historical fraudulent transactions, machine learning models can learn to identify patterns and anomalies indicative of fraudulent activity. These models can generate synthetic data to augment the existing dataset, enabling more accurate fraud detection algorithms.

Algorithmic Trading

In the realm of algorithmic trading, generative AI techniques can be used to simulate market conditions and generate synthetic trading data. This enables traders and financial institutions to test and refine their trading strategies in a risk-free environment. By leveraging generative AI, they can optimize their trading algorithms, leading to more profitable and efficient trading decisions.

Customer Service and Support

Generative AI can enhance customer service and support in the financial industry by providing personalized and contextual assistance. Virtual assistants powered by generative AI can understand customer queries, generate relevant responses, and even simulate natural human conversations. This technology enables faster response times, improves customer satisfaction, and reduces the burden on human customer service representatives.

Risk Assessment and Management

Generative AI models can assist financial institutions in assessing and managing risk more effectively. By analyzing historical market data and generating simulated scenarios, these models can predict potential risks and their impact on financial portfolios. This helps institutions make informed decisions, optimize risk management strategies, and reduce potential losses.

Benefits of Generative AI in the Financial Industry

The integration of generative AI in the financial industry brings several notable benefits:

Increased Efficiency and Accuracy

Generative AI automates complex tasks and processes, significantly improving efficiency and accuracy. By generating synthetic data, financial institutions can train their models on a larger and more diverse dataset, leading to more accurate predictions and better decision-making.

Cost Reduction

By leveraging generative AI, financial institutions can automate repetitive tasks, reducing the need for human intervention. This automation results in cost savings, as fewer human resources are required to perform these tasks. Additionally, generative AI enables faster processing and analysis of large volumes of data, further reducing operational costs.

Enhanced Customer Experience

Generative AI enables the creation of personalized experiences for customers in the financial industry. By analyzing customer data and generating tailored recommendations, financial institutions can offer more relevant and customized services. This enhances customer satisfaction and loyalty, leading to increased customer retention and business growth.

Improved Risk Analysis

Generative AI models can simulate various scenarios and generate synthetic data to assess potential risks accurately. This empowers financial institutions to make informed decisions based on robust risk analysis. By having a deeper understanding of potential risks, they can implement effective risk mitigation strategies and minimize losses.

Challenges and Limitations of Generative AI in Finance

While generative AI offers tremendous potential, there are several challenges and limitations to consider:

Ethical Considerations

Generative AI raises ethical concerns, particularly regarding the use of synthetic data and its potential impact on individuals. Financial institutions must ensure that the generated data does not compromise privacy or perpetuate bias and discrimination.

Data Privacy and Security

The financial industry deals with sensitive customer information, making data privacy and security paramount. Generative AI models must adhere to strict security protocols to protect customer data and prevent unauthorized access or breaches.

Regulatory Compliance

Financial institutions must comply with various regulations and standards. The use of generative AI must align with these regulations, ensuring transparency, fairness, and accountability in decision-making processes.

Reliability and Interpretability

Generative AI models can sometimes produce results that are difficult to interpret or explain. Financial institutions must ensure the reliability and interpretability of the generated data to maintain trust and confidence in their decision-making processes.

Études de cas

Several financial institutions have already successfully implemented generative AI:

  • XYZ Bank utilized generative AI algorithms to detect previously unknown fraudulent patterns, resulting in a significant reduction in fraud-related losses.
  • ABC Investment Firm leveraged generative AI for algorithmic trading, leading to higher trading profitability and reduced risk exposure.
  • DEF Insurance Company employed generative AI-powered virtual assistants to enhance customer support, resulting in improved response times and customer satisfaction ratings.

Future Trends and Potential Impact

Generative AI in the financial industry is poised for further advancements and widespread adoption:

  • Continued advancements in generative AI technology will enable more accurate and sophisticated data generation, resulting in improved decision-making and risk assessment.
  • Integration of generative AI with other emerging technologies, such as blockchain and Internet of Things (IoT), will open up new possibilities for the financial industry, facilitating secure transactions and automated processes.
  • The impact of generative AI will extend beyond individual financial institutions, potentially revolutionizing the entire financial ecosystem, including banking, insurance, and investment sectors.

Conclusion

Generative AI presents exciting opportunities for the financial industry, enabling improved fraud detection, algorithmic trading, customer service, and risk management. While challenges exist, the benefits of generative AI outweigh the limitations. Financial institutions that embrace this technology can gain a competitive edge, drive innovation, and provide enhanced services to their customers.

Financial Translation with AI: Revolutionizing the Language Barrier

Introduction

In today’s interconnected global economy, financial translation plays a crucial role in facilitating communication and understanding between businesses operating in different countries. However, traditional translation methods often come with challenges such as time constraints, cost implications, and potential human errors. The advent of Artificial Intelligence (AI) has revolutionized the field of financial translation, providing innovative solutions to overcome these obstacles. In this article, we will explore the fascinating world of financial translation with AI and its implications for businesses worldwide.

The Power of AI in Financial Translation

Understanding the Basics of AI

AI refers to the simulation of human intelligence in machines, enabling them to perform tasks that typically require human intelligence. This technology has made remarkable advancements in various fields, including language processing and translation. By leveraging machine learning algorithms and natural language processing, AI-powered translation tools can analyze, understand, and generate human-like translations with remarkable accuracy.

The Benefits of AI in Financial Translation

Enhanced Efficiency and Speed

Financial translation with AI significantly improves efficiency and speed by automating the translation process. AI-powered tools can process vast amounts of financial documents and translate them in a fraction of the time it would take a human translator. This expedites cross-border communication, ensuring timely decision-making and reducing time-to-market for global businesses.

Cost Savings

Traditional financial translation services can be expensive, particularly for large-scale projects or ongoing translation needs. AI-powered solutions offer a cost-effective alternative, reducing the reliance on human translators and minimizing translation expenses. Businesses can allocate their resources more efficiently, optimizing their financial strategies.

Consistency and Accuracy

Human translators are susceptible to errors and inconsistencies, which can have severe consequences in financial contexts. AI-driven translation tools provide consistent and accurate translations, ensuring precision in financial terminology, legal frameworks, and regulatory requirements. This level of accuracy minimizes the risk of miscommunication, safeguarding businesses from potential financial and reputational damage.

Implementing AI in Financial Translation

AI Translation Software and Platforms

Various AI translation software and platforms are available in the market, designed specifically for financial translation purposes. These tools utilize sophisticated algorithms and language models trained on vast financial corpora to deliver high-quality translations. Some prominent examples include Google Translate, DeepL, and SYSTRAN.

Customized Machine Translation Systems

Large organizations often require specialized translations tailored to their industry-specific jargon and terminology. In such cases, businesses can develop their own AI-driven machine translation systems. These systems are trained on a vast amount of industry-specific data, ensuring accurate and contextually appropriate translations for financial documents, reports, and contracts.

Collaborative Human-AI Translation

While AI-driven translation tools are powerful, they are not without limitations. Collaborative human-AI translation approaches combine the strengths of both human translators and AI technology. In this approach, human translators work alongside AI translation tools, leveraging the AI’s speed and accuracy while providing the human touch in terms of nuance, cultural context, and stylistic preferences.

Frequently Asked Questions (FAQs)

Q1: What is financial translation with AI?

Financial translation with AI refers to the application of Artificial Intelligence technology in translating financial documents, reports, and communications. AI-powered translation tools utilize machine learning algorithms and natural language processing to generate accurate and contextually appropriate translations.

A1: How does AI improve the efficiency of financial translation?

AI improves the efficiency of financial translation by automating the process and reducing the time required to translate financial documents. AI-powered tools can process large volumes of data, enabling faster communication and decision-making for global businesses.

Q2: Can AI ensure the accuracy of financial translations?

AI-driven translation tools are designed to provide accurate translations by leveraging vast financial corpora and sophisticated language models. However, it’s important to note that human involvement is still crucial for nuanced understanding, cultural context, and stylistic preferences.

A2: How does financial translation with AI benefit businesses?

Financial translation with AI benefits businesses by enhancing efficiency, reducing costs, and ensuring consistency and accuracy in financial communications. It expedites cross-border transactions, minimizes the risk of errors, and optimizes financial strategies.

Q3: Are AI translation tools suitable for industry-specific financial translations?

Yes, AI translation tools can be customized and trained on industry-specific data to ensure accurate and contextually appropriate translations. This makes them suitable for industry-specific financial translations that require specialized terminology and knowledge.

A3: Can collaborative human-AI translation be more effective than AI alone?

Collaborative human-AI translation approaches can be more effective in certain cases, particularly when nuanced understanding, cultural context, and stylistic preferences are crucial. Human translators can work alongside AI tools, leveraging the benefits of both to deliver high-quality translations.

Conclusion

Financial translation with AI has emerged as a game-changer in the global business landscape. By harnessing the power of Artificial Intelligence, businesses can overcome the language barriers that hinder cross-border communication. AI-powered translation tools provide enhanced efficiency, cost savings, and accuracy, enabling businesses to expand their reach, enter new markets, and make informed financial decisions. As AI continues to advance, the future of financial translation looks promising, empowering businesses to thrive in a truly interconnected world.

Lingua Custodia.