What is gen AI, and how does it work?
Gen AI falls under the broader category of artificial intelligence (AI), which is a set of technologies that allow computers to mimic human behavior. Traditionally, AI systems produce a simple, specific output. Gen AI, on the other hand, produces complex outputs, including text, voice, music, images and videos.
Now, let’s focus on large language models (LLMs), which are particularly relevant for insurance. These are a subset of gen AI that works with language, taking in and outputting text. LLMs learn and model language based on content fed into them. Some LLMs are being provided with very large quantities of text data to learn from – more or less the full public Internet. If printed on standard paper, this data would form a stack several thousand kilometers high! Well-known, publicly available LLM-based tools include OpenAI’s ChatGPT, Alphabet’s Gemini, Anthropic’s Claude and Mistral Large.
The use cases for LLMs are plentiful and varied. The average Internet user can generate practical, everyday content, such as writing a birthday message to a friend, or creating a trip itinerary. Meanwhile, in a professional context, LLMs can be helpful to draft emails, translate documents or summarize meetings. Organizations may use existing LLMs created by tech companies, or build their own. Gen AI can also integrate into business applications.
And gen AI goes beyond text-based LLMs. Other models can generate a range of content, including images and voices.
Generating opportunities – and risks
Benefits of gen AI include improved efficiency and productivity. Time-intensive professional tasks, like summarizing email chains or creating presentations, can be done with a single click thanks to gen AI. This saves employees’ time, allowing them to focus on more meaningful work.
But with these opportunities come risks. Publicly available gen AI models hold the risk of data leakage, since content users’ input is later used to train the next generation of models. Therefore, it’s important to avoid inputting personal or corporate information. They also sometimes produce misleading or inaccurate information, a phenomenon known as ‘hallucination.’ Additionally, biases in the input data are reflected in the output, meaning a model will only use knowledge and behaviors it has observed during its training phase. Biases can be mitigated with suppression techniques or by providing additional data. Because of these risks, it’s crucial for any organization integrating gen AI to implement clear guidelines and regularly train employees on how to use it responsibly.
How Allianz Trade uses gen AI
Human expertise, know-how and relationships are at the heart of our trade finance leadership at Allianz Trade. When we leverage new technologies, the ultimate aim is to enable our teams to dedicate their time where they bring the most value. This includes interacting with colleagues, partners and customers, and performing highly strategic tasks that make use of their unparalleled understanding of the nuanced challenges of our global client base.
When we integrate gen AI as one of our tools, it’s to enhance our processes to continue to deliver superior service to organizations around the world. Our teams play a vital role in ensuring gen AI is used effectively and responsibly, especially as it becomes increasingly prevalent. That’s why learning how to use it and other new technologies is also a focal point of our Learning & Development offer.
At Allianz Trade, by investing in gen AI and other emerging technologies, we’re continuously enhancing our analytical and predictive capabilities, helping companies trade with peace of mind.
