Partner Challenges
Take on real-world AI challenges from Switzerland's leading companies at the Swiss {ai} Weeks Hackathon in Zurich
From Talk to Task: Insights from Client Conversations
How can we use Generative AI to analyze transcripts or notes of client conversations and automatically extract actionable insights, such as client requests and action items?
Problem
Client conversations contain key pieces of information, but manually analyzing transcripts or meeting notes to extract actionable insights is time-consuming and prone to error. For this challenge, we invite participants to develop a solution that leverages Gen AI to automatically analyze conversation transcripts and extract key pieces of information. The goal is to empower client advisors with concise, accurate, and actionable insights, helping them save time and better serve their clients. The solution should be capable of: - Extracting specific information, such as client requests, and agreed-upon actions. - Ensuring accuracy and relevance, even in the presence of noisy or incomplete data.
Why hack? 🎯
- •This challenge offers a unique opportunity to work on cutting-edge Generative AI technologies in a domain with real-world impact.
- •Contribute to shaping the future of client interaction in banking.
- •Gain valuable experience in NLP, information extraction, and working with financial-domain challenges.
- •Explore the trade-offs between performance and cost in AI model selection.
UBS Events
Financial Peace of Mind
How would you invest the money from your last exit using an AI for financial peace of mind by turning data into actionable financial advice?
Problem
The manual process of aligning unstructured data (e.g., news, research) with structured data (e.g., financial instruments) is inefficient, error-prone, and can lead to missed investment opportunities. Leverage Large Language Models (LLMs) to automate this process, enabling real-time sentiment analysis, portfolio alignment, and opportunity identification (e.g., "What impact will the latest US tariff threat likely have on my investments, and how should I react?"). Push beyond automation and use agentic AI to generate investment rationales that drive actionable insights and help you achieve financial peace of mind.
Why hack? 🎯
- •This is your opportunity to redefine the future of investing by tackling one of the most pressing challenges in financial decision-making: turning overwhelming streams of unstructured and structured data into actionable, personalized insights to create peace of mind for investors.
- •As an AI expert, you have the chance to push the boundaries of Large Language Models (LLMs) and agentic AI to create a solution that not only automates but also elevates the investment process
- •By participating, you'll contribute to solving inefficiencies that plague traditional investment processes to provide rationales that inspire trust and financial peace of mind
- •Showcase your expertise in applying advanced AI to real-world problems and join us to make a lasting impact on the intersection of AI and finance
UBS Events
Swiss Financial Statement Analyst
Can you create an AI Agent that calculates Key Financial Ratios from annual reports of Swiss companies?
Problem
Swiss companies publish their annual reports in local language, and under varying accounting standards. Key Financial Ratios are important metrics to assess a company (e.g., Credit Worthiness). Calculating those ratios from clean and labelled data is a standard task. Can you write an AI Agent that does the job from PDF documents?
Why hack? 🎯
- •You learn about some of the fundamental concepts in the financial industry from distinguished experts in this field and have the opportunity to work on a carefully curated dataset to solve a real-world problem that, if solved reliably, can unlock high impact use-cases both in industry and academia.
- •You may think "RAG is a solved problem..". But is this true for domain specific documents in multiple languages? Let's find out here!