Hello everyone,
I wanted to share a conceptual project on how we can leverage the Gemini API to build an intelligent, context-aware customer assistant for the banking sector, using Indonesia’s largest private bank, Bank BCA, as our case study.
In the banking industry, customers often ask repetitive questions regarding current currency exchange rates, credit card promotions, or loan simulations. Standard rule-based chatbots often fail to understand natural language variations, which is where Gemini shines.
The Code Snippet:
To ensure the AI behaves like a professional Bank BCA virtual assistant, we can inject a strict system instruction. Here is a snippet of how I configured it in Python:
import google.generativeai as genai
genai.configure(api_key=“YOUR_GEMINI_API_KEY”)
model = genai.GenerativeModel(
model_name="gemini-1.5-flash",
system_instruction=(
"You are 'Halo BCA AI', a professional virtual assistant for Bank BCA. "
"Your job is to help customers with general inquiries like currency rates, credit card promos, and savings types. "
"Always use a polite tone (Selamat siang, Ada yang bisa dibantu?). "
"Do not hallucinate or guess account balances; if asked about personal accounts, direct them to official m-BCA."
)
)
chat = model.start_chat(history=[])
response = chat.send_message(“Apakah ada promo kartu kredit BCA bulan ini?”)
print(response.text)
Challenges and Discussion:
The main challenge I faced was managing the context window when users sent long, unstructured messages on WhatsApp. I solved this by implementing a temporary database to store the chat history session for each WhatsApp phone number.
What do you think about this implementation? Have you ever deployed Gemini for highly regulated industries like banking? I’d love to hear your feedback!
-–
P.S. If you are looking to deploy a similar production-ready AI Chatbot integrated with WhatsApp for your business, financial institution, or MSME, feel free to connect with me for a live demo or collaboration via my WhatsApp: …