The financial sector contends with huge volumes of content, ever-changing international regulations and fast turnaround times. Advancements in artificial intelligence (AI) are transforming how we do business – and the localization of financial documentation is no exception. This blog post covers how to best adopt AI technology for your finance translations.
Data privacy is naturally a top priority in the highly regulated banking and financial industries. Sensitive information processed in publicly available applications, like DeepL or Google Translate, could fall into the wrong hands or even be used for training purposes. The leaking of sensitive data (explicit and inferred) puts you at risk of lawsuits and substantial losses. Before using a large language model (LLM) or other AI algorithms for translation, carefully examine their data policies.
In addition, the introduction of regulations and guidelines regarding AI in your target jurisdictions should be monitored closely.
Gartner highlights data privacy, risk management, transparency and governance (including accountability and oversight) as common principles in these proposals. Businesses can prioritize these areas in their implementation of generative AI and anticipate future regulatory changes to get ahead.
To ensure maximum ROI, your teams need to identify where AI can add value. For example, Nimdzi’s research into LLM adoption in the language services industry found that LLMs excelled in tasks relating to evaluation compared to translation when augmenting machine translation (MT) workflows. These processes include improving the source texts, providing translation memory (TM) suggestions, and contextualizing and correcting MT output.
AI tools can also serve as the starting point for ideas. With the ability to find and summarize financial data and various insights from your target markets, analysts can parse information from documents such as international bank filings and include them in reports. Another way AI applications could be adopted is to help implement style guide rules and maintain your brand’s tone of voice and formality when addressing readers. As some documents are more creative than others (e.g., promotional content for financial products versus contracts and disclaimers), it’s important to continuously test AI models for different use cases and markets. The performance of your chosen tools is subject to change, especially as training evolves for additional languages.
Whichever applications you choose, your prompts and content must be reviewed by native specialists for fluent translations that fulfil their purpose.
Many are drawn to the instant gratification of rapid responses from tools like ChatGPT compared to traditional processes. However, relying on these immediate results for translating financial documents won’t meet the expectations of stakeholders or remove barriers to investment. It takes time to build a solid foundation for effective AI content creation and localization.
AI models such as LLMs are more general-purpose than neural machine translation (NMT) engines and require training with domain-specific samples. For instance, PwC’s natural language processing (NLP) model was trained to interpret “default” in the context of loans rather than the general meaning to generate accurate request results.
Certain languages need significantly more training and don’t score as well as NMT engines like DeepL. Have professional finance translators check their performance and finetune your content using the best practices for finance translation.
According to the International Monetary Fund (IMF), close human supervision is required to tackle the potential risks of generative AI use in financial institutions’ operations. Generative AI isn’t perfect and can “hallucinate” (provide false information) or even present biases from underlying datasets. Inaccuracies in reports, such as incorrect customer profiles or market sentiments, negatively affect risk-taking and investment decisions. Subject-matter experts must verify that responses to prompts are factual and meet the standards of local regulations. This is especially important in an industry where justification for actions and decisions should be clearly presented to stakeholders. For example, the Dutch Authority for the Financial Markets (AFM)’s Guidelines on Sustainability Claims require sustainability claims to be substantiated with up-to-date facts and a cogent explanation.
Additionally, generative AI requires frequent feedback to better tailor all output to local audiences. The AI language translation process therefore can’t go without thorough evaluation.
AI tools should be part of a comprehensive approach to localization that boosts productivity, earns your customers’ trust and maximizes ROI. Working with professional finance translators and prompt engineers who can adapt to continuous developments is key to long-term success.
As demonstrated above, without qualified expert native linguists, AI language translation is susceptible to non-compliance and other common errors when localizing financial documents.
Nimdzi’s study found that the language industry has the necessary expertise for assessing multilingual LLM applications. Localization teams deliver exceptional financial translations by:
Professional finance translation services ensure that AI will be leveraged to truly drive global growth. Our full guide to localizing financial documents includes more tips to improve your communications.
AI models have a lot of potential to make finance translation seamless for multiple markets. To find the opportunities for your company and alleviate risks, you’ll need the help of a trusted language service provider. Attached’s seasoned financial translators, language consultants and AI content reviewers deliver secure translations to optimize your international operations. Speak to our team for more information on the best approach to deploy AI language translation and get a free quote.