With the rise of digitalization, automation has become an indispensable trend in the investment banking industry. Automating routine and repetitive tasks can significantly boost efficiency and productivity. By adopting automation and AI technologies, investment banks can reduce human errors, enhance analytic capabilities, and improve client services. In this article, we will summarize key findings on how leading investment banks automate workflows by leveraging automation frameworks, robots, and AI assistants with a focus on pdf and github resources.
Automating pitchbook creation with RPA and AI
One of the most time-consuming tasks in investment banks is creating pitchbooks and investment memorandums from scratch. RPA bots can automate the data collection process from various sources and populate templates to generate pitchbooks in PDF formats. NLP techniques can further enhance the narrative sections. Github resources like annoying-pitchbook-bot provide open-source templates to demonstrate the process.
Streamlining reporting with Python-based automation
Generating performance reports across various asset classes can be automated using Python-based frameworks. Python libraries like pdfkit, reportlab can create dynamic PDF reports from templates by connecting to data sources. Jupyter notebooks on github like quarterly-returns-pdf-report demonstrate how to auto-generate PDF reports from Python.
Automating document workflows with intelligent OCR
Many investment banks still rely on legacy manual processes for handling documents. Intelligent OCR techniques can extract information from scanned documents and auto-file them. Machine learning algorithms can further classify documents and route to relevant teams. Some github projects like invoice-processing-ocr provide code samples for automating document workflows.
Chatbots and personal assistants for client services
Another area ripe for automation is client services. Chatbots and virtual assistants can handle common client requests like portfolio inquiries, market updates etc. They can be trained with NLP techniques to understand questions and provide answers by connecting to various data systems. Github projects like robo-advisor-chatbot demonstrate how to build a conversational AI for client services.
Automation and AI are reshaping how investment banks operate. By adopting the latest technologies, banks can boost efficiency, minimize errors, and enhance services. Open-source libraries and github resources provide useful templates and code samples to get started with automating pitchbooks, reporting, documents and other investment banking processes.