Overview
Up-selling/Product Recommendation
During the process of selecting a financial product for purchase, individuals are typically faced with a wide array of options, each with subtle variations. Due to a lack of sufficient knowledge, customers often make sub-optimal choices. To address this issue, We has developed a model that takes into account the customer’s initial choice of financial product and generates a list of closely related alternatives. These alternatives not only align with the customer’s financial feasibility but also have the potential to yield greater profits for the finance company. By providing customers with this comprehensive selection, they can make an informed decision and choose an optimal product that benefits both themselves and the finance company.
AI Applications
One AI application for businesses facing the choice between open-source and proprietary models to deploy generative AI is natural language processing (NLP) for customer service or support chatbots. Businesses can utilize generative AI models to develop chatbots that can understand and respond to customer queries in a more human-like manner. The choice between open-source and proprietary models can impact the accuracy, scalability, and customization capabilities of the NLP models deployed in these chatbots.
Additionally, another AI application is the development of recommendation systems. Generative AI models can be used to create personalized recommendations for products or content based on user behavior and preferences. The choice between open-source and proprietary models can affect the quality of the recommendations, as well as the ability to tailor the recommendation system to specific business needs.
Furthermore, businesses can leverage generative AI for content generation, such as automated text summarization, language translation, and creative writing. The choice between open-source and proprietary models can influence the linguistic fluency, coherence, and originality of the generated content.
In each of these applications, the decision between open-source and proprietary models for generative AI deployment can significantly impact the performance, interpretability, and ethical considerations of the AI systems utilized by businesses.
