AI automation and data science for controlling and finance

Module 1: AI basics and application (2 days, live online)

• Introduction to the functionality of Large Language Models (LLMs) and their possible applications in the world of finance.
• Basics of text processing, data cleansing and structuring to optimize AI models.
• In-depth study of embeddings and vector databases for the efficient analysis and processing of text data.
• Development of simple data pipelines for the automated processing of financial documents (e.g. OCR).
• Overview of Retrieval Augmented Generation (RAG) to improve information retrieval.
• Practical examples of creating automated reports and using LLM technology for specific use cases.

Objectives: participants gain a deep understanding of the technical foundations of AI, gain practical insights into processing and analyzing data and are able to integrate the first simple AI solutions into their business processes.

Module 2: AI automation and AI app development (1 day, attendance)

• Use of no-code tools such as LangFlow to quickly automate processes in controlling, finance and accounting.
• Introduction to Python-based solutions for the automation of complex workflows, e.g. for the reconciliation of bank transactions with invoices or AI-supported automated bookings.
• AI-based automation of data analyses.
• AI-based parameterization of prediction models/risk models.
• Integration of ML models into existing workflows and real-time analyses via API interfaces.

Objective: participants learn to combine no-code tools and Python for the automation of controlling, finance and risk processes, to develop ML models and to integrate them efficiently into existing systems in order to exploit automation potential in their company.

Module 3: AI automation with agent frameworks (1 day, presence)

• Introduction to agent frameworks such as LangFlow for the automation of complex business processes.
• Development of workflows in which agents are used to automate routine tasks such as customer communication and payment processing.
• Integration of ML models in agent-based workflows for automated credit risk assessment.
• Creation and optimization of agents that independently process and analyze data and make decisions.
• Practical application and simulation of agents that optimize internal processes and thus increase efficiency.

Objective: participants learn how to use agent frameworks to develop complex automations and use AI models effectively in their business processes to reduce manual workload and automate decisions.

Module 4: Practical transfer "Development of an AI automation use case for your own company" (self-learning phase)

• In the self-learning phase, participants analyze processes with automation potential in their own company and select a specific use case, which they develop further using the methodology taught in the training .
• The aim is to establish a clear link between the business requirements and the possibilities of AI automation. On this basis, the participants create a pitch that demonstrates the benefits of their use case, including an assessment of resource savings (time, cost and efficiency), identification of technical and organizational requirements and presentation of the potential business impact.
• In addition, the participants develop an outline of the technical workflow for the implementation, which includes steps such as data extraction and preparation (e.g. using OCR), the integration of AI models such as LLMs or machine learning algorithms, the automation of follow-up actions such as reporting and notifications, and the identification of relevant interfaces such as APIs and databases.

Module 5: Practical transfer: development of AI use cases, presentation, discussion, feedback (1 day, live online)

• The participants present the results of their self-learning phase, including the AI use case, the cost-benefit analysis and the technical workflow outline.
• Joint reflection with the group and the trainer.
• Together, the use cases are honed with a view to the added value for your own company and implementation recommendations are made.
• The workshop offers participants a valuable opportunity to develop a customized concept for an AI automation use case for their company, including the associated code workflow.

Objective: The aim is to develop practical solutions that can sustainably increase the value of your company. The workshop supports the participants in optimizing their AI use cases in a practical way and developing concrete implementation strategies. Through individual feedback and valuable input from the group, a sound basis is created for successfully implementing the concepts developed in the corporate context and realizing sustainable increases in efficiency.