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its financial advisory workflows using an advanced semi-agentic, multi-step gen AI process on Vertex AI.%0A%0Acloud.google.com/blog/products/ai-machine-learning/how-commerzbank-is-transforming-financial-advisory-workflows-with-gen-ai"track-metadata-module="social icons" target="_blank" rel="noopener"><svg class="nRhiJb-Bz112c nRhiJb-Bz112c-OWXEXe-DX2B6 nRhiJb-Bz112c-OWXEXe-nSuQf" viewBox="0 0 24 24" role="presentation" aria-hidden="true" role="presentation" aria-hidden="true"><path d="M20 4H4c-1.1 0-2 .9-2 2v12c0 1.1.9 2 2 2h16c1.1 0 2-.9 2-2V6c0-1.1-.9-2-2-2zm-.8 2L12 10.8 4.8 6h14.4zM4 18V7.87l8 5.33 8-5.33V18H4z"></path></svg></a></li></ul></div></div></div></div></div><div><section class="nRhiJb-RWrDld WWjeSb"><div class="nRhiJb-DbgRPb-wNfPc-ma6Yeb nRhiJb-DbgRPb-wNfPc-cGMI2b"><div class="nRhiJb-DARUcf"><div class="VI2oYd"><section class="PBkdHd "><img class=" rFf1Dd" src="https://storage.googleapis.com/gweb-cloudblog-publish/original_images/Commerzbank-v3.gif" alt="https://storage.googleapis.com/gweb-cloudblog-publish/original_images/Commerzbank-v3.gif" loading="lazy"/></section></div></div></div></section><section class="nRhiJb-DARUcf"><div class="nRhiJb-DbgRPb-wNfPc-ma6Yeb nRhiJb-DbgRPb-wNfPc-cGMI2b"><section class="DA9Qj nRhiJb-ObfsIf nRhiJb-fmcmS-oXtfBe"><div class="nRhiJb-kR0ZEf-OWXEXe-GV1x9e-ibL1re"></div><div class="nRhiJb-kR0ZEf-OWXEXe-GV1x9e-II5mzb kWSSdd"><h5 class="cHE8Ub Qwf2Db-MnozTc Qwf2Db-MnozTc-OWXEXe-MnozTc-qWD73c">Tolga Bastürk</h5><p class="nRhiJb-qJTHM khCp7b">Product Owner, Commerzbank</p></div><div class="nRhiJb-kR0ZEf-OWXEXe-GV1x9e-II5mzb kWSSdd"><h5 class="cHE8Ub Qwf2Db-MnozTc Qwf2Db-MnozTc-OWXEXe-MnozTc-qWD73c">Anant Nawalgaria</h5><p class="nRhiJb-qJTHM khCp7b">Sr. Staff ML Engineer, Google</p></div></section></div></section><div class="nRhiJb-DARUcf"><div class="nRhiJb-ObfsIf nRhiJb-DbgRPb-wNfPc-ma6Yeb nRhiJb-DbgRPb-qWD73c-cGMI2b"><div class="nRhiJb-kR0ZEf-OWXEXe-GV1x9e-ibL1re dzoHJ"><div class="aLW4J" track-metadata-module="left rail"><h5 class="qZDXV Qwf2Db-MnozTc Qwf2Db-MnozTc-OWXEXe-MnozTc-wNfPc">Join us at Google Cloud Next</h5><p class="nRhiJb-cHYyed Mrfygc nRhiJb-BP2Omd-ma6Yeb-KE6vqe nRhiJb-DbgRPb-R6PoUb-cGMI2b">Early bird pricing available now through Feb 14th.</p><a class="nRhiJb-LgbsSe nRhiJb-LgbsSe-OWXEXe-CNusmb-o6Shpd " href="https://cloud.withgoogle.com/next?utm_source=cgc-blog&utm_medium=blog&utm_campaign=FY25-Q2-global-EXP106-physicalevent-er-next25-mc&utm_content=left-hand-rail-blog-cta&utm_term=-" track-metadata-module="left rail"track-metadata-module_headline="join us at google cloud next" track-name="register"track-type="button"track-metadata-position="cta"track-metadata-eventdetail="cloud.withgoogle.com/next?utm_source=cgc-blog&utm_medium=blog&utm_campaign=FY25-Q2-global-EXP106-physicalevent-er-next25-mc&utm_content=left-hand-rail-blog-cta&utm_term=-">Register </a></div></div><div class="OYL9D nRhiJb-kR0ZEf-OWXEXe-GV1x9e-OiUrBf" jsname="tx2NYc"><section class="Wy08Ac nRhiJb-qJTHM-OWXEXe-hJDwNd nRhiJb-DbgRPb-II5mzb-cGMI2b"><span class="dQQu7c" jsaction="rcuQ6b:npT2md" jscontroller="YSybTb" data-track-type="" soy-skip ssk='5:kbe95'><p><span style="vertical-align:baseline">In today's fast-paced financial landscape, staying competitive requires embracing innovation and efficiency. Commerzbank, a leading German bank, recognized the potential to streamline its internal workflows, particularly within its financial advisory division for corporate clients. </span></p> <p><span style="vertical-align:baseline">Given regulatory requirements, sales advisors need to carefully document investment suggestions in detailed protocols. It’s a highly manual and time consuming task. This has led to significant productivity bottlenecks and reduces the time available for advising customers.</span></p> <p><span style="vertical-align:baseline">"Our Sales advisor team spends a considerable amount of time in documentation of advisory calls," said </span><span style="vertical-align:baseline">Ulrich Heitbaum, COO of Corporate Clients segment at Commerzbank</span><span style="vertical-align:baseline">. "By partnering with Google to build a sophisticated GenAI system to automate this process, we considerably boost productivity. One thought leads us: Only lean, smoothly functioning processes and reliable technology lead to an outstanding - excellent - service delivery to the customer."</span></p> <p><span style="vertical-align:baseline">Recognizing the potential for improvement, Commerzbank partnered with Google Cloud to develop an advanced gen AI-powered solution that automates this labor-intensive process. By leveraging </span><a href="https://cloud.google.com/ai/generative-ai"><span style="text-decoration:underline;vertical-align:baseline">Google Cloud's AI and machine learning</span></a><span style="vertical-align:baseline"> capabilities, Commerzbank was able to automate this process and achieve significant gains in sales advisor productivity.</span></p></span></section><section class="Wy08Ac nRhiJb-qJTHM-OWXEXe-hJDwNd nRhiJb-DbgRPb-II5mzb-cGMI2b"><span class="dQQu7c" jsaction="rcuQ6b:npT2md" jscontroller="YSybTb" data-track-type="" soy-skip ssk='5:kbe95'><h3><strong style="vertical-align:baseline">The Challenge: Time-Consuming Manual Processes</strong></h3> <p><span style="vertical-align:baseline">Financial advisors play a crucial role in providing personalized financial advice to clients. However, the process of reviewing client interactions and extracting and summarizing relevant domain and client-specific information was highly manual and inefficient. Sales advisors had to dedicate significant time to listening to call recordings, identifying key details, and manually entering data into various systems. This process not only consumed valuable time but also increased the risk of errors and inconsistencies.</span></p> <h3><strong style="vertical-align:baseline">The Technical Solution: A Deep Dive into Commerzbank's gen-AI system</strong></h3> <p><span style="vertical-align:baseline">Commerzbank's solution for automating financial advisory workflows leverages a sophisticated multi-step gen-AI architecture built using </span><a href="https://cloud.google.com/vertex-ai"><span style="text-decoration:underline;vertical-align:baseline">Vertex AI</span></a><span style="vertical-align:baseline"> and designed for quality, scalability and extensibility. </span><span style="vertical-align:baseline">Gemini 1.5 Pro</span><span style="vertical-align:baseline">'s ability to </span><a href="https://cloud.google.com/transform/historic-year-for-ai-momentous-multimodal-moment-the-prompt"><span style="text-decoration:underline;vertical-align:baseline">understand multiple modalities</span></a><span style="vertical-align:baseline"> and process long context information played a key role in building this system that would not have been possible with any other model</span><span style="vertical-align:baseline">. A sample system implementation is available in </span><a href="https://github.com/GoogleCloudPlatform/generative-ai/blob/main/gemini/use-cases/diarized-transcription-summarization/diarized_transcription_and_summarization.ipynb" rel="noopener" target="_blank"><span style="text-decoration:underline;vertical-align:baseline">this codelab</span></a><span style="vertical-align:baseline">. The following section provides a breakdown of the key steps:</span></p></span></section><section class="QzPuud"><div><section><figure class="NEBdNd"><section class="PBkdHd DhGbH" jscontroller="SCGBie" jsaction="rcuQ6b:npT2md"><img class="JcsBte mZzdH ZOnyjc" src="https://storage.googleapis.com/gweb-cloudblog-publish/images/image1_v6TdxD7.max-1900x1900.png" alt="https://storage.googleapis.com/gweb-cloudblog-publish/images/image1_v6TdxD7.max-1900x1900.png" jsname='P3Vluc' jsaction="click:HTIlC" loading="lazy"/><section class="glue-modal glue-modal--dark QHdDac" role="dialog" aria-modal="true"><img class="JcsBte mZzdH ZOnyjc" src="https://storage.googleapis.com/gweb-cloudblog-publish/images/image1_v6TdxD7.max-1900x1900.png" alt="https://storage.googleapis.com/gweb-cloudblog-publish/images/image1_v6TdxD7.max-1900x1900.png" jsname='P3Vluc' jsaction="click:HTIlC" loading="lazy"/><button class="glue-modal__close-btn" tabindex="0" aria-label="Close this modal"></button></section></section></figure><div class="nRhiJb-cHYyed nRhiJb-DbgRPb-R6PoUb-ma6Yeb ZpqjUe"><span class="dQQu7c" jsaction="rcuQ6b:npT2md" jscontroller="YSybTb" data-track-type="" soy-skip ssk='5:kbe95'><p>An end-to-end architecture of the AI Advisor system</p></span></div></section></div></section><section class="Wy08Ac nRhiJb-qJTHM-OWXEXe-hJDwNd nRhiJb-DbgRPb-II5mzb-cGMI2b"><span class="dQQu7c" jsaction="rcuQ6b:npT2md" jscontroller="YSybTb" data-track-type="" soy-skip ssk='5:kbe95'><p><strong style="vertical-align:baseline">1. User interaction and data import (1, 2, 3):<br></strong><span style="vertical-align:baseline">The process begins with the sales advisor using a user-friendly frontend interface (1) to select the client calls they need to process. This interface communicates with a Java Spring backend (2) that manages the workflow. The backend then initiates the import of the selected audio recordings from Commerzbank's recording system (3) into Google Cloud Platform (GCP) storage buckets. This ensures the data is readily available for the AI processing pipeline.</span></p> <p><strong style="vertical-align:baseline">2. Audio chunking and storage (4.1, 4.2):<br></strong><span style="vertical-align:baseline">To handle potentially lengthy client calls, the imported audio recordings are divided into smaller, manageable segments (4.1). This "chunking" process allows the system to process even multi-hour calls efficiently. These audio chunks are then stored securely within GCP storage (4.2), ensuring data durability and accessibility for subsequent steps.</span></p> <p><strong style="vertical-align:baseline">3. Advanced diarization and transcription (4.3):<br></strong><span style="vertical-align:baseline">This step is crucial for generating a high-quality, structured transcript that captures the nuances of the conversation. Gemini 1.5 Pro is employed to create a diarized transcript, meaning each speaker is identified and their contributions are accurately attributed. This process occurs sequentially, with each audio chunk processed in order. To maximize accuracy, the model receives the transcript generated up to that point, along with carefully engineered prompts and a few-shot example of audio-to-text transcription. This ensures the final transcript is not only accurate in terms of content but also includes correct speaker identification and especially numerical information, which is crucial in a financial context. Once the final transcript is generated, the individual audio chunks from step 4.2 are deleted to optimize storage.</span></p> <p><strong style="vertical-align:baseline">4. Fact extraction (4.4):<br></strong><span style="vertical-align:baseline">With a comprehensive and long transcript in hand, Gemini 1.5 Pro long context is then used to analyze and extract relevant facts (4.4). This involves identifying key information related to the specific financial advisory document that needs to be completed. The model is prompted to recognize and extract crucial details such as client names, investment preferences, risk tolerance, and financial goals.</span></p> <p><strong style="vertical-align:baseline">5. Summary generation (4.5):<br></strong><span style="vertical-align:baseline">This step focuses on generating concise and accurate summaries for each field within the financial advisory document. Leveraging the extracted facts from the previous step and employing Chain-of-Thought (CoT) prompting, Gemini 1.5 Pro creates multiple German-language summaries tailored to the specific domain and the requirements of each form field. This ensures the generated summaries are not only informative but also comply with Commerzbank's internal guidelines and regulatory requirements.</span></p> <p><strong style="vertical-align:baseline">6. Summary optimization and explanation (4.6):<br></strong><span style="vertical-align:baseline">To ensure the highest quality output, the multiple summaries generated for each form field are </span><a href="https://cloud.google.com/blog/products/ai-machine-learning/enhancing-llm-quality-and-interpretability-with-the-vertex-gen-ai-evaluation-service?e=48754805"><span style="text-decoration:underline;vertical-align:baseline">evaluated and the best summary for each field is selected</span></a><span style="vertical-align:baseline"> using the </span><a href="https://cloud.google.com/vertex-ai/generative-ai/docs/models/evaluation-overview"><span style="text-decoration:underline;vertical-align:baseline">Vertex AI Gen AI Evaluation Service</span></a><span style="vertical-align:baseline"> (4.6). Importantly, the service also provides a human-readable explanation for its selection, enabling sales advisors to understand the reasoning behind the AI's choices and maintain trust in the automated process.</span></p> <p><span style="vertical-align:baseline">This multi-stage architecture, combining the power of Gemini 1.5 Pro with Vertex AI's evaluation capabilities, enables Commerzbank to automate a complex and time-consuming process with high accuracy and efficiency. By streamlining these workflows, Commerzbank empowers its sales advisors to focus on higher-value tasks, ultimately improving client service and driving business growth.</span></p> <h2><strong style="vertical-align:baseline">The Benefits: Increased Efficiency and Productivity</strong></h2> <p><span style="vertical-align:baseline">The impact of this AI-powered automation has been significant. By automating the manual tasks associated with financial advisory documentation, Commerzbank has achieved substantial productivity gains. Sales advisors now have more time to focus on higher-value activities, such as building client relationships and providing personalized financial advice.</span></p> <p><strong style="vertical-align:baseline">Key benefits of the solution include:</strong></p> <ul> <li><strong style="vertical-align:baseline">Reduced processing time:</strong><span style="vertical-align:baseline"> The automated solution significantly reduces the time required to process client interactions by achieving what takes a client 60-plus minutes in just a few minutes with manual human overview. This greatly accelerates time to business. </span></li> <li><strong style="vertical-align:baseline">Increased productivity:</strong><span style="vertical-align:baseline"> By automating manual tasks, the solution empowers sales advisors to focus on more strategic activities, leading to increased productivity and improved client service.</span></li> </ul> <h2><strong style="vertical-align:baseline">Looking into the Future</strong></h2> <p><span style="vertical-align:baseline">Commerzbank's collaboration with Google Cloud exemplifies the transformative power of AI in the financial services industry. By embracing innovative technologies, Commerzbank is streamlining its operations, empowering its employees, and enhancing the client experience. “Therefore, we set up a Strategic Initiative Corporate Clients AI powered sales force - to make our sales focus on high value activities” Sebastian Kauck, CIO Corporate Clients at Commerzbank. </span></p> <p><span style="vertical-align:baseline">They plan to scale this solution to other use cases and enhance its functionality, providing new and additional value to their sales team. This AI-powered solution is just one example of how Commerzbank is leveraging technology to stay ahead of the curve and deliver exceptional financial services, in addition to many other cloud and GenAI use cases.</span></p> <p><span style="vertical-align:baseline">This partnership has not only delivered significant productivity gains but has also laid the foundation for future innovation. Commerzbank plans to expand the use of AI and automation across other areas of its business, further optimizing its operations and enhancing its offerings to clients.</span></p> <hr> <p><sup><em><span style="vertical-align:baseline">This project was a joint collaboration between Anant Nawalgaria, Patrick Nestler, Florian Baumert and Markus Staab from Google and Tolga Bastürk, </span><span style="vertical-align:baseline">Otto Franke, Mirko Franke,</span><span style="vertical-align:baseline"> Gregor Wilde, Janine Unger, Enis Muhaxhiri, Andre Stubig, Ayse-Maria Köken and Andreas Racke from Commerzbank.</span></em></sup></p></span></section><section class="kcBhad"><section class="Fabbec"><span class="WrMNjb">Posted in</span><ul class="FzXI4e"><li class="I4B51b"><a href="https://cloud.google.com/blog/products/ai-machine-learning" track-metadata-position="body"track-metadata-eventdetail="cloud.google.com/blog/products/ai-machine-learning"track-metadata-module="tag 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Qwf2Db-MnozTc-OWXEXe-MnozTc-qWD73c nRhiJb-DbgRPb-c5RTEf-ma6Yeb nRhiJb-BFbNVe-r8s4j-bMElCd FI6Gl nRhiJb-fmcmS-oXtfBe" track-name="ai & machine learning"track-type="tag">AI & Machine Learning</div><h3 class="Qwf2Db-MnozTc HGFKtc Qwf2Db-MnozTc-OWXEXe-MnozTc-wNfPc">Enhance Gemini model security with content filters and system instructions</h3><p class="nRhiJb-cHYyed dTIXyb nRhiJb-DbgRPb-R6PoUb-ma6Yeb">By Salah Ahmed • 3-minute read</p></div></div></a></div></div><div class=" QaGyvd nRhiJb-kR0ZEf-OWXEXe-GV1x9e-c5RTEf nRhiJb-kR0ZEf-OWXEXe-GV1x9e-qWD73c-V2iZpe"><div class="mA0uBe"><a href="https://cloud.google.com/blog/products/api-management/using-apigee-api-management-for-ai" class="lD2oe" track-name="operationalizing generative ai apps with apigee"track-type="card"track-metadata-eventdetail="cloud.google.com/blog/products/api-management/using-apigee-api-management-for-ai"><div class="AhkbS "><div class="hqnDEf"><section class="PBkdHd "><img class=" D5RK8d" src="https://storage.googleapis.com/gweb-cloudblog-publish/images/02_-_API_Management_TYbidbQ.max-700x700.jpg" alt="https://storage.googleapis.com/gweb-cloudblog-publish/images/02_-_API_Management_TYbidbQ.max-700x700.jpg" loading="lazy"/></section></div><div class="JUOx5b"><div class="Qwf2Db-MnozTc Qwf2Db-MnozTc-OWXEXe-MnozTc-qWD73c nRhiJb-DbgRPb-c5RTEf-ma6Yeb nRhiJb-BFbNVe-r8s4j-bMElCd FI6Gl nRhiJb-fmcmS-oXtfBe" track-name="api management"track-type="tag">API Management</div><h3 class="Qwf2Db-MnozTc HGFKtc Qwf2Db-MnozTc-OWXEXe-MnozTc-wNfPc">Operationalizing generative AI apps with Apigee</h3><p class="nRhiJb-cHYyed dTIXyb nRhiJb-DbgRPb-R6PoUb-ma6Yeb">By Michael Vakoc • 7-minute read</p></div></div></a></div></div><div class=" QaGyvd nRhiJb-kR0ZEf-OWXEXe-GV1x9e-c5RTEf nRhiJb-kR0ZEf-OWXEXe-GV1x9e-qWD73c-V2iZpe"><div class="mA0uBe"><a href="https://cloud.google.com/blog/products/networking/cross-cloud-network-solutions-support-for-ai-workloads" class="lD2oe" track-name="networking support for ai workloads"track-type="card"track-metadata-eventdetail="cloud.google.com/blog/products/networking/cross-cloud-network-solutions-support-for-ai-workloads"><div class="AhkbS "><div class="hqnDEf"><section class="PBkdHd "><img class=" D5RK8d" src="https://storage.googleapis.com/gweb-cloudblog-publish/images/0-hero-hpc.max-700x700.png" alt="https://storage.googleapis.com/gweb-cloudblog-publish/images/0-hero-hpc.max-700x700.png" loading="lazy"/></section></div><div class="JUOx5b"><div class="Qwf2Db-MnozTc Qwf2Db-MnozTc-OWXEXe-MnozTc-qWD73c nRhiJb-DbgRPb-c5RTEf-ma6Yeb nRhiJb-BFbNVe-r8s4j-bMElCd FI6Gl nRhiJb-fmcmS-oXtfBe" track-name="networking"track-type="tag">Networking</div><h3 class="Qwf2Db-MnozTc HGFKtc Qwf2Db-MnozTc-OWXEXe-MnozTc-wNfPc">Networking support for AI workloads</h3><p class="nRhiJb-cHYyed dTIXyb nRhiJb-DbgRPb-R6PoUb-ma6Yeb">By Ammett Williams • 4-minute read</p></div></div></a></div></div></section></div></section></article></div></div><c-data id="i1" jsdata=" 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Vertex AI.",[1732302000],"https://storage.googleapis.com/gweb-cloudblog-publish/original_images/Commerzbank-v3.gif","https://cloud.google.com/blog/products/ai-machine-learning/how-commerzbank-is-transforming-financial-advisory-workflows-with-gen-ai",[1732302001,899295000]],[["Tolga Bast\u00fcrk","Product Owner, Commerzbank"],["Anant Nawalgaria","Sr. Staff ML Engineer, Google"]],[null,"\u003cscript type\u003d\"application/ld+json\"\u003e{\"@context\":\"https://schema.org\",\"@type\":\"BlogPosting\",\"@id\":\"https://cloud.google.com/blog/products/ai-machine-learning/how-commerzbank-is-transforming-financial-advisory-workflows-with-gen-ai\",\"headline\":\"How Commerzbank is transforming financial advisory workflows with gen AI\",\"description\":\"Commerzbank has automated its financial advisory workflows using an advanced semi-agentic, multi-step gen AI process on Vertex AI.\",\"image\":\"https://storage.googleapis.com/gweb-cloudblog-publish/original_images/Commerzbank-v3.gif\",\"author\":[{\"@type\":\"Person\",\"name\":\"Tolga Bast\u00fcrk\",\"url\":\"\"},{\"@type\":\"Person\",\"name\":\"Anant Nawalgaria\",\"url\":\"\"}],\"datePublished\":\"2024-11-22\",\"publisher\":{\"@type\":\"Organization\",\"name\":\"Google Cloud\",\"logo\":{\"@type\":\"ImageObject\",\"url\":\"https://www.gstatic.com/devrel-devsite/prod/v8bb8fa0afe9a8c3a776ebeb25d421bb443344d789b3607754dfabea418b8c4be/cloud/images/cloud-logo.svg\"}},\"url\":\"https://cloud.google.com/blog/products/ai-machine-learning/how-commerzbank-is-transforming-financial-advisory-workflows-with-gen-ai\",\"keywords\":[\"AI \\u0026 Machine Learning\",\"Financial Services\",\"Customers\",\"Developers \\u0026 Practitioners\",\"Google Cloud in Europe\"],\"timeRequired\":\"PT5M\"}\u003c/script\u003e"],["How Commerzbank is transforming financial advisory workflows with gen AI"],["https://storage.googleapis.com/gweb-cloudblog-publish/original_images/Commerzbank-v3.gif","https://storage.googleapis.com/gweb-cloudblog-publish/original_images/Commerzbank-v3.gif 1288w, https://storage.googleapis.com/gweb-cloudblog-publish/original_images/Commerzbank-v3.gif 2436w"," 1288px, 2436px","https://storage.googleapis.com/gweb-cloudblog-publish/original_images/Commerzbank-v3.gif"],null,[[null,null,[null,[null,"\u003cp\u003e\u003cspan style\u003d\"vertical-align:baseline\"\u003eIn today's fast-paced financial landscape, staying competitive requires embracing innovation and efficiency. Commerzbank, a leading German bank, recognized the potential to streamline its internal workflows, particularly within its financial advisory division for corporate clients.\u00a0\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003e\u003cspan style\u003d\"vertical-align:baseline\"\u003eGiven regulatory requirements, sales advisors need to carefully document investment suggestions in detailed protocols. It\u2019s a highly manual and time consuming task. This has led to significant productivity bottlenecks and reduces the time available for advising customers.\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003e\u003cspan style\u003d\"vertical-align:baseline\"\u003e\"Our Sales advisor team spends a considerable amount of time in documentation of advisory calls,\" said \u003c/span\u003e\u003cspan style\u003d\"vertical-align:baseline\"\u003eUlrich Heitbaum, COO of Corporate Clients segment at Commerzbank\u003c/span\u003e\u003cspan style\u003d\"vertical-align:baseline\"\u003e. \"By partnering with Google to build a sophisticated GenAI system to automate this process, we considerably boost productivity. One thought leads us: Only lean, smoothly functioning processes and reliable technology lead to an outstanding - excellent - service delivery to the customer.\"\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003e\u003cspan style\u003d\"vertical-align:baseline\"\u003eRecognizing the potential for improvement, Commerzbank partnered with Google Cloud to develop an advanced gen AI-powered solution that automates this labor-intensive process. By leveraging \u003c/span\u003e\u003ca href\u003d\"https://cloud.google.com/ai/generative-ai\"\u003e\u003cspan style\u003d\"text-decoration:underline;vertical-align:baseline\"\u003eGoogle Cloud's AI and machine learning\u003c/span\u003e\u003c/a\u003e\u003cspan style\u003d\"vertical-align:baseline\"\u003e capabilities, Commerzbank was able to automate this process and achieve significant gains in sales advisor productivity.\u003c/span\u003e\u003c/p\u003e"]]],[null,null,[null,[null,"\u003ch3\u003e\u003cstrong style\u003d\"vertical-align:baseline\"\u003eThe Challenge: Time-Consuming Manual Processes\u003c/strong\u003e\u003c/h3\u003e\n\u003cp\u003e\u003cspan style\u003d\"vertical-align:baseline\"\u003eFinancial advisors play a crucial role in providing personalized financial advice to clients. However, the process of reviewing client interactions and extracting and summarizing relevant domain and client-specific information was highly manual and inefficient. Sales advisors had to dedicate significant time to listening to call recordings, identifying key details, and manually entering data into various systems. This process not only consumed valuable time but also increased the risk of errors and inconsistencies.\u003c/span\u003e\u003c/p\u003e\n\u003ch3\u003e\u003cstrong style\u003d\"vertical-align:baseline\"\u003eThe Technical Solution: A Deep Dive into Commerzbank's gen-AI system\u003c/strong\u003e\u003c/h3\u003e\n\u003cp\u003e\u003cspan style\u003d\"vertical-align:baseline\"\u003eCommerzbank's solution for automating financial advisory workflows leverages a sophisticated multi-step gen-AI architecture built using \u003c/span\u003e\u003ca href\u003d\"https://cloud.google.com/vertex-ai\"\u003e\u003cspan style\u003d\"text-decoration:underline;vertical-align:baseline\"\u003eVertex AI\u003c/span\u003e\u003c/a\u003e\u003cspan style\u003d\"vertical-align:baseline\"\u003e and designed for quality, scalability and extensibility. \u003c/span\u003e\u003cspan style\u003d\"vertical-align:baseline\"\u003eGemini 1.5 Pro\u003c/span\u003e\u003cspan style\u003d\"vertical-align:baseline\"\u003e's ability to \u003c/span\u003e\u003ca href\u003d\"https://cloud.google.com/transform/historic-year-for-ai-momentous-multimodal-moment-the-prompt\"\u003e\u003cspan style\u003d\"text-decoration:underline;vertical-align:baseline\"\u003eunderstand multiple modalities\u003c/span\u003e\u003c/a\u003e\u003cspan style\u003d\"vertical-align:baseline\"\u003e and process long context information played a key role in building this system that would not have been possible with any other model\u003c/span\u003e\u003cspan style\u003d\"vertical-align:baseline\"\u003e. A sample system implementation is available in \u003c/span\u003e\u003ca href\u003d\"https://github.com/GoogleCloudPlatform/generative-ai/blob/main/gemini/use-cases/diarized-transcription-summarization/diarized_transcription_and_summarization.ipynb\" rel\u003d\"noopener\" target\u003d\"_blank\"\u003e\u003cspan style\u003d\"text-decoration:underline;vertical-align:baseline\"\u003ethis codelab\u003c/span\u003e\u003c/a\u003e\u003cspan style\u003d\"vertical-align:baseline\"\u003e. The following section provides a breakdown of the key steps:\u003c/span\u003e\u003c/p\u003e"]]],[null,null,null,null,null,null,null,null,[[[null,"\u003cp\u003eAn end-to-end architecture of the AI Advisor system\u003c/p\u003e"],["https://storage.googleapis.com/gweb-cloudblog-publish/images/image1_v6TdxD7.max-1900x1900.png","https://storage.googleapis.com/gweb-cloudblog-publish/images/image1_v6TdxD7.max-1100x1100.png 1060w, https://storage.googleapis.com/gweb-cloudblog-publish/images/image1_v6TdxD7.max-1900x1900.png 1829w"," 1060px, 1829px","https://storage.googleapis.com/gweb-cloudblog-publish/images/image1_v6TdxD7.max-1900x1900.png"],null,3]]],[null,null,[null,[null,"\u003cp\u003e\u003cstrong style\u003d\"vertical-align:baseline\"\u003e1. User interaction and data import (1, 2, 3):\u003cbr\u003e\u003c/strong\u003e\u003cspan style\u003d\"vertical-align:baseline\"\u003eThe process begins with the sales advisor using a user-friendly frontend interface (1) to select the client calls they need to process. This interface communicates with a Java Spring backend (2) that manages the workflow. The backend then initiates the import of the selected audio recordings from Commerzbank's recording system (3) into Google Cloud Platform (GCP) storage buckets. This ensures the data is readily available for the AI processing pipeline.\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong style\u003d\"vertical-align:baseline\"\u003e2. Audio chunking and storage (4.1, 4.2):\u003cbr\u003e\u003c/strong\u003e\u003cspan style\u003d\"vertical-align:baseline\"\u003eTo handle potentially lengthy client calls, the imported audio recordings are divided into smaller, manageable segments (4.1). This \"chunking\" process allows the system to process even multi-hour calls efficiently. These audio chunks are then stored securely within GCP storage (4.2), ensuring data durability and accessibility for subsequent steps.\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong style\u003d\"vertical-align:baseline\"\u003e3. Advanced diarization and transcription (4.3):\u003cbr\u003e\u003c/strong\u003e\u003cspan style\u003d\"vertical-align:baseline\"\u003eThis step is crucial for generating a high-quality, structured transcript that captures the nuances of the conversation. Gemini 1.5 Pro is employed to create a diarized transcript, meaning each speaker is identified and their contributions are accurately attributed. This process occurs sequentially, with each audio chunk processed in order. To maximize accuracy, the model receives the transcript generated up to that point, along with carefully engineered prompts and a few-shot example of audio-to-text transcription. This ensures the final transcript is not only accurate in terms of content but also includes correct speaker identification and especially numerical information, which is crucial in a financial context. Once the final transcript is generated, the individual audio chunks from step 4.2 are deleted to optimize storage.\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong style\u003d\"vertical-align:baseline\"\u003e4. Fact extraction (4.4):\u003cbr\u003e\u003c/strong\u003e\u003cspan style\u003d\"vertical-align:baseline\"\u003eWith a comprehensive and long transcript in hand, Gemini 1.5 Pro long context is then used to analyze and extract relevant facts (4.4). This involves identifying key information related to the specific financial advisory document that needs to be completed. The model is prompted to recognize and extract crucial details such as client names, investment preferences, risk tolerance, and financial goals.\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong style\u003d\"vertical-align:baseline\"\u003e5. Summary generation (4.5):\u003cbr\u003e\u003c/strong\u003e\u003cspan style\u003d\"vertical-align:baseline\"\u003eThis step focuses on generating concise and accurate summaries for each field within the financial advisory document. Leveraging the extracted facts from the previous step and employing Chain-of-Thought (CoT) prompting, Gemini 1.5 Pro creates multiple German-language summaries tailored to the specific domain and the requirements of each form field. This ensures the generated summaries are not only informative but also comply with Commerzbank's internal guidelines and regulatory requirements.\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong style\u003d\"vertical-align:baseline\"\u003e6. Summary optimization and explanation (4.6):\u003cbr\u003e\u003c/strong\u003e\u003cspan style\u003d\"vertical-align:baseline\"\u003eTo ensure the highest quality output, the multiple summaries generated for each form field are \u003c/span\u003e\u003ca href\u003d\"https://cloud.google.com/blog/products/ai-machine-learning/enhancing-llm-quality-and-interpretability-with-the-vertex-gen-ai-evaluation-service?e\u003d48754805\"\u003e\u003cspan style\u003d\"text-decoration:underline;vertical-align:baseline\"\u003eevaluated and the best summary for each field is selected\u003c/span\u003e\u003c/a\u003e\u003cspan style\u003d\"vertical-align:baseline\"\u003e using the \u003c/span\u003e\u003ca href\u003d\"https://cloud.google.com/vertex-ai/generative-ai/docs/models/evaluation-overview\"\u003e\u003cspan style\u003d\"text-decoration:underline;vertical-align:baseline\"\u003eVertex AI Gen AI Evaluation Service\u003c/span\u003e\u003c/a\u003e\u003cspan style\u003d\"vertical-align:baseline\"\u003e (4.6). Importantly, the service also provides a human-readable explanation for its selection, enabling sales advisors to understand the reasoning behind the AI's choices and maintain trust in the automated process.\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003e\u003cspan style\u003d\"vertical-align:baseline\"\u003eThis multi-stage architecture, combining the power of Gemini 1.5 Pro with Vertex AI's evaluation capabilities, enables Commerzbank to automate a complex and time-consuming process with high accuracy and efficiency. By streamlining these workflows, Commerzbank empowers its sales advisors to focus on higher-value tasks, ultimately improving client service and driving business growth.\u003c/span\u003e\u003c/p\u003e\n\u003ch2\u003e\u003cstrong style\u003d\"vertical-align:baseline\"\u003eThe Benefits: Increased Efficiency and Productivity\u003c/strong\u003e\u003c/h2\u003e\n\u003cp\u003e\u003cspan style\u003d\"vertical-align:baseline\"\u003eThe impact of this AI-powered automation has been significant. By automating the manual tasks associated with financial advisory documentation, Commerzbank has achieved substantial productivity gains. Sales advisors now have more time to focus on higher-value activities, such as building client relationships and providing personalized financial advice.\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong style\u003d\"vertical-align:baseline\"\u003eKey benefits of the solution include:\u003c/strong\u003e\u003c/p\u003e\n\u003cul\u003e\n\u003cli\u003e\u003cstrong style\u003d\"vertical-align:baseline\"\u003eReduced processing time:\u003c/strong\u003e\u003cspan style\u003d\"vertical-align:baseline\"\u003e The automated solution significantly reduces the time required to process client interactions by achieving what takes a client 60-plus minutes in just a few minutes with manual human overview. This greatly accelerates time to business.\u00a0\u003c/span\u003e\u003c/li\u003e\n\u003cli\u003e\u003cstrong style\u003d\"vertical-align:baseline\"\u003eIncreased productivity:\u003c/strong\u003e\u003cspan style\u003d\"vertical-align:baseline\"\u003e By automating manual tasks, the solution empowers sales advisors to focus on more strategic activities, leading to increased productivity and improved client service.\u003c/span\u003e\u003c/li\u003e\n\u003c/ul\u003e\n\u003ch2\u003e\u003cstrong style\u003d\"vertical-align:baseline\"\u003eLooking into the Future\u003c/strong\u003e\u003c/h2\u003e\n\u003cp\u003e\u003cspan style\u003d\"vertical-align:baseline\"\u003eCommerzbank's collaboration with Google Cloud exemplifies the transformative power of AI in the financial services industry. By embracing innovative technologies, Commerzbank is streamlining its operations, empowering its employees, and enhancing the client experience. \u201cTherefore, we set up a Strategic Initiative Corporate Clients AI powered sales force - to make our sales focus on high value activities\u200b\u201d Sebastian Kauck, CIO Corporate Clients at Commerzbank.\u00a0\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003e\u003cspan style\u003d\"vertical-align:baseline\"\u003eThey plan to scale this solution to other use cases and enhance its functionality, providing new and additional value to their sales team. This AI-powered solution is just one example of how Commerzbank is leveraging technology to stay ahead of the curve and deliver exceptional financial services, in addition to many other cloud and GenAI use cases.\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003e\u003cspan style\u003d\"vertical-align:baseline\"\u003eThis partnership has not only delivered significant productivity gains but has also laid the foundation for future innovation. Commerzbank plans to expand the use of AI and automation across other areas of its business, further optimizing its operations and enhancing its offerings to clients.\u003c/span\u003e\u003c/p\u003e\n\u003chr\u003e\n\u003cp\u003e\u003csup\u003e\u003cem\u003e\u003cspan style\u003d\"vertical-align:baseline\"\u003eThis project was a joint collaboration between Anant Nawalgaria, Patrick Nestler, Florian Baumert and Markus Staab from Google and Tolga Bast\u00fcrk, \u003c/span\u003e\u003cspan style\u003d\"vertical-align:baseline\"\u003eOtto Franke, Mirko Franke,\u003c/span\u003e\u003cspan style\u003d\"vertical-align:baseline\"\u003e Gregor Wilde, Janine Unger, Enis Muhaxhiri, Andre Stubig, Ayse-Maria K\u00f6ken and Andreas Racke from Commerzbank.\u003c/span\u003e\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e"]]]],[["Containers \u0026 Kubernetes","With MultiKueue, grab GPUs for your GKE cluster, wherever they may be","Together, MultiKueue, GKE, and Dynamic Workload Scheduler let you provision GPU resources in a GKE cluster regardless of region.",["https://storage.googleapis.com/gweb-cloudblog-publish/images/07_-_Containers__Kubernetes_iY4YTLa.max-700x700.jpg","https://storage.googleapis.com/gweb-cloudblog-publish/images/07_-_Containers__Kubernetes_iY4YTLa.max-400x400.jpg 324w, https://storage.googleapis.com/gweb-cloudblog-publish/images/07_-_Containers__Kubernetes_iY4YTLa.max-700x700.jpg 648w"," 324px, 648px","https://storage.googleapis.com/gweb-cloudblog-publish/images/07_-_Containers__Kubernetes_iY4YTLa.max-700x700.jpg"],null,5,null,"https://cloud.google.com/blog/products/containers-kubernetes/using-multikueue-to-provision-global-gpu-resources",null,1,[["Jean-Baptiste Leroy","Customer Engineer"]],null,"56049"],["AI \u0026 Machine Learning","Enhance Gemini model security with content filters and system instructions","Learn how to create safer Gemini model outputs that strengthen customer relationships by using content filters and system instructions.",["https://storage.googleapis.com/gweb-cloudblog-publish/images/01_-_AI__Machine_Learning_H1ZyZG8.max-700x700.jpg","https://storage.googleapis.com/gweb-cloudblog-publish/images/01_-_AI__Machine_Learning_H1ZyZG8.max-400x400.jpg 324w, https://storage.googleapis.com/gweb-cloudblog-publish/images/01_-_AI__Machine_Learning_H1ZyZG8.max-700x700.jpg 648w"," 324px, 648px","https://storage.googleapis.com/gweb-cloudblog-publish/images/01_-_AI__Machine_Learning_H1ZyZG8.max-700x700.jpg"],null,3,null,"https://cloud.google.com/blog/products/ai-machine-learning/enhance-gemini-model-security-with-content-filters-and-system-instructions",null,1,[["Salah Ahmed","Senior Product Manager, Google Cloud"],["Anand Iyer","Group Product Manager, Google Cloud"]],null,"56045"],["API Management","Operationalizing generative AI apps with Apigee","Learn how Apigee API management can help with AI tasks like security, staying within budget, troubleshooting, and improving the end-user experience.",["https://storage.googleapis.com/gweb-cloudblog-publish/images/02_-_API_Management_TYbidbQ.max-700x700.jpg","https://storage.googleapis.com/gweb-cloudblog-publish/images/02_-_API_Management_TYbidbQ.max-400x400.jpg 324w, https://storage.googleapis.com/gweb-cloudblog-publish/images/02_-_API_Management_TYbidbQ.max-700x700.jpg 648w"," 324px, 648px","https://storage.googleapis.com/gweb-cloudblog-publish/images/02_-_API_Management_TYbidbQ.max-700x700.jpg"],null,7,null,"https://cloud.google.com/blog/products/api-management/using-apigee-api-management-for-ai",null,1,[["Michael Vakoc","Product Manager"],["Ruben Gonzalez","Customer Engineer"]],null,"56046"],["Networking","Networking support for AI workloads","In this blog we look at some of the benefits of the Cross-Cloud Network in supporting AI and HPC workloads, both managed and self-managed.",["https://storage.googleapis.com/gweb-cloudblog-publish/images/0-hero-hpc.max-700x700.png","https://storage.googleapis.com/gweb-cloudblog-publish/images/0-hero-hpc.max-400x400.png 324w, https://storage.googleapis.com/gweb-cloudblog-publish/images/0-hero-hpc.max-700x700.png 648w"," 324px, 648px","https://storage.googleapis.com/gweb-cloudblog-publish/images/0-hero-hpc.max-700x700.png"],null,4,null,"https://cloud.google.com/blog/products/networking/cross-cloud-network-solutions-support-for-ai-workloads",null,1,[["Ammett Williams","Developer Relations Engineer"]],null,"56018"]],null,"AI \u0026 Machine Learning",["Join us at Google Cloud Next","Early bird pricing available now through Feb 14th.","Register","https://cloud.withgoogle.com/next?utm_source\u003dcgc-blog\u0026utm_medium\u003dblog\u0026utm_campaign\u003dFY25-Q2-global-EXP106-physicalevent-er-next25-mc\u0026utm_content\u003dleft-hand-rail-blog-cta\u0026utm_term\u003d-"],[["AI \u0026 Machine Learning","https://cloud.google.com/blog/products/ai-machine-learning","ai-machine-learning"],["Financial Services","https://cloud.google.com/blog/topics/financial-services","financial-services"],["Customers","https://cloud.google.com/blog/topics/customers","customers"],["Developers \u0026 Practitioners","https://cloud.google.com/blog/topics/developers-practitioners","developers"],["Google Cloud in Europe","https://cloud.google.com/blog/topics/google-cloud-in-europe","google-cloud-in-europe"]],null,null,5], sideChannel: {}});</script><script id="wiz_jd" nonce="AJEoGQ-Zo_3JCp9vsGfv-w">if (window['_wjdc']) {const wjd = {}; window['_wjdc'](wjd); delete window['_wjdc'];}</script><script aria-hidden="true" id="WIZ-footer" nonce="AJEoGQ-Zo_3JCp9vsGfv-w">window.wiz_progress&&window.wiz_progress(); window.stopScanForCss&&window.stopScanForCss(); ccTick('bl');</script></body></html><footer id="ZCHFDb"><footer class="nRhiJb-RWrDld nRhiJb-yePe5c QJnbF" jscontroller="NsSboe" 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