Optimizing Customer Service Productivity in Banking Industry

In the fast-moving world of banking, delivering excellent customer service has become a critical factor for businesses to stand out. As with any other industry, Banks face the challenge of providing prompt and precise responses to their customers' inquiries. However, with the growing number of customer queries, maintaining high levels of productivity while offering exceptional service can be difficult. This is where Paradigm's advanced language model capabilities can be beneficial in optimizing customer service productivity in the banking industry.

Experience a new world

With the help of AI, an insured or advisor can ask a question in natural language and get a response in just a few seconds.

Question

User:

"How can I dispute a bank transaction carried out using my credit card?"

1. Prompting: Transmitting user input to generate the prompt
2. Context: Transmitting contextual informations (eg: user ID, contract ID)
   
3. Searching: A search engine performs a search for the most relevant content.
   
4. Embedding: The embedding process is applied to the content selected by the search engine in order to compare it with the prompt.
   

Answer

Chatbot:

"You can dispute a banking transaction carried out via your credit card by contacting your bank in writing and presenting the ticket issued by the TPE or proof of the payment order to which the dispute relates. You can also contact the credit card issuer in writing, attaching the receipt or proof of the payment order." Source Conditions-generales-particuliers.PDF page 29, 34 and 39

1. Time-saving: 2 sec average response time to user inquiries.
2. Increased user satisfaction: 90% customer satisfaction ratings.
3. Cost savings: 78% rate of inquiries resolved without human intervention
4. Improved autonomy: 80% time savings achieved through automation.
   

What is prompting?

Prompting involves providing a hint or suggestion to the language model to generate more accurate and relevant responses.

What is fine-tuning?

Fine-tuning refers to the process of training a pre-trained language model on a specific dataset to improve its performance on a specific task.

What is embedding?

Embedding is a process of representing text data in numerical format that a machine learning model can understand and process.

How does LightOn ensure data privacy

LightOn adheres to strict data privacy regulations and ensures that customer data is stored and processed securely in a private cloud or on-premises environment.