The financial sector is improving efficiency through fintech, while the insurance industry is increasingly focused on automation and advanced risk management.
Given the nature of their services, both sectors face challenges such as enhancing credit evaluation, strengthening security, and adapting to demographic shifts.
NABLAS provides AI solutions for time-series risk prediction and generative AI technologies to detect false applications and fraudulent contracts, as well as automate customer support—enhancing both operational efficiency and reliability.

Although it depends on the task and the state of data preparation, in most cases results can be confirmed within 2 to 3 months. For small-scale testing, it is possible to complete it in about 1 month.

It will vary depending on the type of AI used, the amount of data, and the scale of operation. If you contact us, we will provide you with a rough estimate including initial and operating costs.

Depending on your needs, we can restrict the scope of use of the data we handle, or limit its use as learning data.We can also customize the data to meet your security requirements, so please feel free to contact us.

Accuracy varies greatly depending on the quality of the data introduced and the pattern of fraudulent transactions, so it is not possible to give a general numerical answer. However, we can calculate and provide an approximate accuracy estimate through a PoC (proof of concept). Detection accuracy will improve through continuous learning based on the data.

Yes, it can be used. In recent years, new fraudulent methods have emerged, such as the misuse of generative AI to fabricate evidence images and documents. AI can analyze images, videos, and text from multiple angles to detect traces of tampering and unnatural patterns. In addition, by learning from past billing data and fraudulent cases, it is possible to detect abnormal billing behavior early on.