General Summary
During the Risk Live Europe conference in London, Deutsche Bank’s head of innovation, Tim Mason, unveiled seven significant generative AI (GenAI) use cases. These AI applications are designed to enhance efficiency across various divisions within the bank, ranging from document processing to email management and customer interaction.
Document Processing System
One of Deutsche Bank’s most promising AI applications involves processing documents to convert their contents into data compatible with the bank’s workflows. This system is already in operation within various banking divisions and aims to process up to a million documents by the end of the year. The tool has notably diminished false alerts by 30% to 50%.
Email Automation
Another primary AI application focuses on reading and processing incoming emails, such as customer complaints or trade queries. This AI can not only forward emails to the appropriate personnel but also automate responses and verify client information, enhancing the efficiency of email management.
Digital Assistant for Reports
Deutsche Bank has rolled out a digital assistant capable of summarizing static content from corporate filings like 10-K and 10-Q reports. This assistant aids the research division by generating reports and answering specific queries, even mimicking individual analysts’ writing styles.
Agent Chat and Internal Communication
GenAI-powered Agent Chat is being used to facilitate customer and internal staff communications, making support functions like procurement and legal more efficient. This tool aims to reduce the need for human agents by resolving customer issues through generative chat, boosting operational efficiency.
Data Collection and Verification
Large language models are employed to gather and verify data from public sources, refining client information master lists. This application ensures data accuracy and resolves discrepancies, bolstering trust in client information storage.
Coding Assistant
A specialized software coding assistant helps differentiate internally created code from that sourced from public repositories like GitHub. This differentiation is crucial for complying with regulatory queries, ensuring transparency, and adhering to best practices in code management.
Future Rollout and Integration
The AI initiatives form part of a broader three-year innovation strategy started in 2022. These applications are being piloted within specific divisions but are on track for a firm-wide rollout. They promise shared AI services and an integrated AI workspace with robust risk and control measures.
Deutsche Bank’s exploration and implementation of GenAI signify a monumental shift toward enhanced efficiency and innovation, positioning the bank as a leader in AI-driven financial services.
Resource
Deutsche Bank’s seven lead use cases for GenAI – Risk.net
