GenAI Intelligent Assistant
How do you deliver personalised support for banking customers?
- Do you need to reduce operating costs by providing customers with a new way of requesting services and managing their finances?
- Do you provide your customers with quick access to services while improving their engagement?
- Do you believe now is the time to lead in the next generation interfaces for retail banks?
The next generation interface for retail banks
GenAI Intelligent Assistant is a conversational interface. Leveraging Google Vertex AI and the core banking engine developed by Thought Machine, this offering has been designed to implement the next generation of interfaces for retail banking.
One such use case utilises the capabilities of advanced AI Large Language Models (LLMs), allowing the user to flexibly phrase questions, provide category prediction, determine query type interface, understand the intent of the user, and generates a meaningful response.
This use case can reduce a bank’s operating costs by providing customers with a new way of requesting services and managing their finances.
Delivering personalised support for customers
Banks have a high number of staff performing online bank assistant duties. A lot of these activities are redundant and repetitive. There is a need to increase the efficiency of customer service teams by freeing them from routine call / chat handling to focus on tasks that add more value.
Banks also need to drive operational cost savings, in order to reinvest in transformation-led initiatives that will enable them to drive customer acquisition and retention.
There is an increasing focus to significantly improve the customer experience compared to traditional AI-based chatbots, delivering more personalised and trusted support.
Our four key benefits of GenAI Intelligent Assistant
Reduced operating costs by providing customers with a new way of requesting services and managing their finances.
Improved customer engagement and satisfaction with improved net promoter score (NPS) ratings.
Quicker access to services and improved overall service performance.
Improved employee experience – reduction of repetitive tasks, focus on higher value tasks, change of role from policy implementer to troubleshooter.
How does it work?
Zero-shot learning (prompting):
Decodes the intent of the user query using a call to a LLM.
Category prediction:
LLMs have been used to generalise or specialise categories to determine item relevance.
Query type inference:
LLMs have been used to determine what type of query will produce the answer the user is looking for.
Inference:
Once the user intent and types of item they are interested in are known, the inference of what the user is asking for is established.
Get started with GenAI Intelligent Assistant
Our partners
Together, with the GenAI Intelligent Assistant, Thought Machine, Google and GFT can deliver significant value, cost efficiencies and innovation for banks.
Watch our demonstration video
Disocver how our GenAI Intelligent Assistant is a conversational interface. Leveraging Google Vertex AI and the core banking engine developed by Thought Machine, this offering has been designed to implement the next generation of interfaces for retail banking.
