[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"node-by-slug-\u002Fen\u002Finsights\u002FRAG-in-finance-en":3,"related-content-view-26540-en":394},["Reactive",4],{"entity":5,"errors":-1},{"__typename":6,"status":7,"metatag":8,"id":202,"path":203,"hideNavigation":204,"title":205,"translations":206,"sharedHeaderRefMedia":214,"sharedTeaserTitle":13,"sharedTeaserText":239,"sharedTeaserRefMedia":241,"sharedParagraphs":249,"sharedTaxrefIndustries":347,"sharedTaxrefExpertises":354,"sharedIntroText":367,"sharedCtaLink":204,"sharedDate":369,"sharedAuthor":371},"NodeInsight",true,[9,14,18,21,25,30,34,38,42,46,50,54,58,62,66,69,73,76,79,81,84,87,91,95,99,103,107,111,115,119,123,127,131,135,139,142,145,148,151,154,157,160,163,166,170,174,177,181,185,189,192,196,200],{"tag":10,"attributes":11},"meta",{"name":12,"content":13},"title","RAG in finance: accelerating client advice with reliable AI",{"tag":10,"attributes":15},{"name":16,"content":17},"description","Explore key insights from the 2025 Banking Transformation Summit and learn why RAG is emerging as a foundational approach for AI in finance.",{"tag":10,"attributes":19},{"name":20,"content":17},"abstract",{"tag":10,"attributes":22},{"name":23,"content":24},"icbm","51.525772, -0.082716",{"tag":26,"attributes":27},"link",{"rel":28,"href":29},"canonical","https:\u002F\u002Fcms.zuehlke.com\u002Fen\u002Finsights\u002FRAG-in-finance",{"tag":10,"attributes":31},{"name":32,"content":33},"robots","index, 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client advice in financial services with Retrieval Augmented Generation",[207,210],{"path":203,"langcode":208},{"id":209},"en",{"path":211,"langcode":212},"\u002Fde\u002Finsights\u002FRAG-im-Finanzwesen",{"id":213},"de",{"id":215,"sharedMediaLegend":204,"mediaImage":216,"name":238},"171861",{"url":217,"width":218,"height":219,"alt":220,"title":204,"size":221,"mime":222,"variations":204,"headerMinimalVariations":223},"https:\u002F\u002Fcms.zuehlke.com\u002Fsites\u002Fdefault\u002Ffiles\u002Fimages\u002FAI%20chatbot%20concept.jpeg",1033,715,"AI chatbot concept",132145,"image\u002Fjpeg",[224,229,230,234,237],{"name":225,"url":226,"width":227,"height":228},"FOCAL_POINT32_SMALL","https:\u002F\u002Fcms.zuehlke.com\u002Fsites\u002Fdefault\u002Ffiles\u002Fstyles\u002Ffocal_point_3_2_small\u002Fpublic\u002Fimages\u002FAI%20chatbot%20concept.jpeg.webp?itok=w3bTbIgi",800,533,{"name":225,"url":226,"width":227,"height":228},{"name":231,"url":232,"width":218,"height":233},"FOCAL_POINT169_MEDIUM","https:\u002F\u002Fcms.zuehlke.com\u002Fsites\u002Fdefault\u002Ffiles\u002Fstyles\u002Ffocal_point_16_9_medium\u002Fpublic\u002Fimages\u002FAI%20chatbot%20concept.jpeg.webp?itok=AoQNQgEW",582,{"name":235,"url":236,"width":218,"height":233},"FOCAL_POINT169_LARGE","https:\u002F\u002Fcms.zuehlke.com\u002Fsites\u002Fdefault\u002Ffiles\u002Fstyles\u002Ffocal_point_16_9_large\u002Fpublic\u002Fimages\u002FAI%20chatbot%20concept.jpeg.webp?itok=_QAKvqNu",{"name":235,"url":236,"width":218,"height":233},"AI chatbot concept.jpeg",{"processed":240},"\u003Cp>Explore key insights from the 2025 Banking Transformation Summit and learn why RAG is emerging as a foundational approach for AI in finance.\u003C\u002Fp>",{"id":242,"sharedMediaLegend":204,"mediaImage":243,"name":248},"171860",{"url":244,"width":245,"height":246,"alt":220,"title":204,"size":247,"mime":222,"variations":204},"https:\u002F\u002Fcms.zuehlke.com\u002Fsites\u002Fdefault\u002Ffiles\u002Fimages\u002FAI%20chatbot%20concept.jpg",1280,720,106726,"AI chatbot concept.jpg",[250,256,262,300,308,314,339],{"__typename":251,"id":252,"ctaLink":204,"pgSharedText":253,"pgSharedTitle":204,"pgSharedCtaLink":204},"ParagraphPgText","31157",{"value":254,"processed":254,"format":255},"\u003Cp>At the \u003Ca href=\"https:\u002F\u002Fftsummit.net\u002Fbanking\u002F\" target=\"_blank\">2025 Banking Transformation Summit\u003C\u002Fa>, I explored a pivotal shift in how financial institutions can harness AI in delivering client advice. While the first wave of AI adoption brought us chatbots and copilots, the next phase demands more precision, trust, and enterprise alignment.\u003C\u002Fp>\u003Cp>This is where \u003Cstrong>Retrieval-Augmented Generation (RAG) \u003C\u002Fstrong>comes in. It’s a core architectural building block for LLM-based applications that bridges the gap between the specific needs of financial services, particularly when dealing with client-related information.\u003C\u002Fp>\u003Cp>In this article, I’ll share the key takeaways from my talk at the Summit and explain why RAG is emerging as a foundational approach for AI in financial services.\u003C\u002Fp>","full_html",{"__typename":251,"id":257,"ctaLink":204,"pgSharedText":258,"pgSharedTitle":261,"pgSharedCtaLink":204},"31158",{"value":259,"processed":260,"format":255},"\u003Cp>Public Large Language Models (LLMs) like ChatGPT, Copilot, and Gemini have demonstrated impressive capabilities in natural language understanding and generation. However, their limitations become starkly apparent in regulated, data-sensitive environments like \u003Ca href=\"\u002Fnode\u002F26262\" data-entity-type=\"node\" data-entity-uuid=\"199e61a6-092e-4197-a9ef-da425d9c5af6\" data-entity-substitution=\"canonical\">banking\u003C\u002Fa> and \u003Ca href=\"\u002Fnode\u002F26265\" data-entity-type=\"node\" data-entity-uuid=\"3030ad8f-55fa-49b2-a8d3-3b9853c5b126\" data-entity-substitution=\"canonical\">insurance\u003C\u002Fa>:\u003C\u002Fp>\u003Cul>\u003Cli>\u003Cstrong>Accuracy:\u003C\u002Fstrong> LLMs often hallucinate, generating plausible but incorrect answers due to a lack of context or training data quality.\u003C\u002Fli>\u003Cli>\u003Cstrong>Confidentiality:\u003C\u002Fstrong> Sensitive client data cannot be used to train public models, limiting their relevance in real-world scenarios.\u003C\u002Fli>\u003Cli>\u003Cstrong>Currency:\u003C\u002Fstrong> Updating LLMs with new information is computationally expensive and infrequent, making them ill-suited for fast-moving domains.\u003C\u002Fli>\u003Cli>\u003Cstrong>Provenance:\u003C\u002Fstrong> These models are black boxes, offering no transparency into how answers are derived.\u003C\u002Fli>\u003Cli>\u003Cstrong>Numeracy:\u003C\u002Fstrong> Handling of tabular, numeric, or historic data remains a weak spot of large language models.\u003C\u002Fli>\u003Cli>\u003Cstrong>Localisation:\u003C\u002Fstrong> LLMs are trained on English-language documents from outside the regions where financial institutions are based. This leads to poor responses in non-English languages or when cultural context is important.\u003C\u002Fli>\u003C\u002Ful>\u003Cp>To truly transform client advice functions, we must move beyond generic tools and \u003Ca href=\"\u002Fnode\u002F26486\" data-entity-type=\"node\" data-entity-uuid=\"915b40f2-5188-4476-b86c-7e831565887f\" data-entity-substitution=\"canonical\">build AI systems\u003C\u002Fa> that are context-aware, secure, and explainable.\u003C\u002Fp>\u003Ch2>RAG: A smarter way to create value in client advisory services\u003C\u002Fh2>","\u003Cp>Public Large Language Models (LLMs) like ChatGPT, Copilot, and Gemini have demonstrated impressive capabilities in natural language understanding and generation. However, their limitations become starkly apparent in regulated, data-sensitive environments like \u003Ca href=\"\u002Fen\u002Findustries\u002Fbanking\" data-entity-type=\"node\" data-entity-uuid=\"199e61a6-092e-4197-a9ef-da425d9c5af6\" data-entity-substitution=\"canonical\" title=\"INDUSTRY: Banking (EN)\">banking\u003C\u002Fa> and \u003Ca href=\"\u002Fen\u002Findustries\u002Finsurance\" data-entity-type=\"node\" data-entity-uuid=\"3030ad8f-55fa-49b2-a8d3-3b9853c5b126\" data-entity-substitution=\"canonical\" title=\"INDUSTRY: Insurance (EN)\">insurance\u003C\u002Fa>:\u003C\u002Fp>\u003Cul>\u003Cli>\u003Cstrong>Accuracy:\u003C\u002Fstrong> LLMs often hallucinate, generating plausible but incorrect answers due to a lack of context or training data quality.\u003C\u002Fli>\u003Cli>\u003Cstrong>Confidentiality:\u003C\u002Fstrong> Sensitive client data cannot be used to train public models, limiting their relevance in real-world scenarios.\u003C\u002Fli>\u003Cli>\u003Cstrong>Currency:\u003C\u002Fstrong> Updating LLMs with new information is computationally expensive and infrequent, making them ill-suited for fast-moving domains.\u003C\u002Fli>\u003Cli>\u003Cstrong>Provenance:\u003C\u002Fstrong> These models are black boxes, offering no transparency into how answers are derived.\u003C\u002Fli>\u003Cli>\u003Cstrong>Numeracy:\u003C\u002Fstrong> Handling of tabular, numeric, or historic data remains a weak spot of large language models.\u003C\u002Fli>\u003Cli>\u003Cstrong>Localisation:\u003C\u002Fstrong> LLMs are trained on English-language documents from outside the regions where financial institutions are based. This leads to poor responses in non-English languages or when cultural context is important.\u003C\u002Fli>\u003C\u002Ful>\u003Cp>To truly transform client advice functions, we must move beyond generic tools and \u003Ca href=\"\u002Fen\u002Fexpertise\u002Fai-implementation\" data-entity-type=\"node\" data-entity-uuid=\"915b40f2-5188-4476-b86c-7e831565887f\" data-entity-substitution=\"canonical\" title=\"EXPERTISE: AI implementation (EN)\">build AI systems\u003C\u002Fa> that are context-aware, secure, and explainable.\u003C\u002Fp>\u003Ch2>RAG: A smarter way to create value in client advisory services\u003C\u002Fh2>","Why do financial institutions need more than LLMs for providing client advice?",{"__typename":263,"id":264,"caption":204,"captionSecondary":204,"pgSharedTitle":204,"pgMediaRefMedia":265,"secondaryImage":204,"edgeToEdge":299,"fullWidth":7},"ParagraphPgMedia","31173",{"id":266,"sharedMediaLegend":204,"mediaImage":267,"name":298},"171863",{"url":268,"width":269,"height":270,"alt":271,"title":204,"size":272,"mime":273,"variations":204,"mediaVariations":274,"infographVariations":292},"https:\u002F\u002Fcms.zuehlke.com\u002Fsites\u002Fdefault\u002Ffiles\u002Fimages\u002FAccelerating%20client%20advice%20graphic_1.png",2707,1460,"Accelerating client advice graphic",338723,"image\u002Fpng",[275,280,281,286,291],{"name":276,"url":277,"width":278,"height":279},"FIXED_WIDTH_SMALL","https:\u002F\u002Fcms.zuehlke.com\u002Fsites\u002Fdefault\u002Ffiles\u002Fstyles\u002Ffixed_width_small\u002Fpublic\u002Fimages\u002FAccelerating%20client%20advice%20graphic_1.png.webp?itok=x_kFQhSP",900,485,{"name":276,"url":277,"width":278,"height":279},{"name":282,"url":283,"width":284,"height":285},"FIXED_WIDTH_MEDIUM","https:\u002F\u002Fcms.zuehlke.com\u002Fsites\u002Fdefault\u002Ffiles\u002Fstyles\u002Ffixed_width_medium\u002Fpublic\u002Fimages\u002FAccelerating%20client%20advice%20graphic_1.png.webp?itok=voIjVpuf",1200,647,{"name":287,"url":288,"width":289,"height":290},"FIXED_WIDTH_LARGE","https:\u002F\u002Fcms.zuehlke.com\u002Fsites\u002Fdefault\u002Ffiles\u002Fstyles\u002Ffixed_width_large\u002Fpublic\u002Fimages\u002FAccelerating%20client%20advice%20graphic_1.png.webp?itok=oLpnJuZh",1600,863,{"name":287,"url":288,"width":289,"height":290},[293,294,295,296,297],{"name":276,"url":277,"width":278,"height":279},{"name":276,"url":277,"width":278,"height":279},{"name":276,"url":277,"width":278,"height":279},{"name":282,"url":283,"width":284,"height":285},{"name":282,"url":283,"width":284,"height":285},"Accelerating client advice graphic.png",false,{"__typename":251,"id":301,"ctaLink":302,"pgSharedText":305,"pgSharedTitle":204,"pgSharedCtaLink":307},"31174",{"title":303,"url":304,"internal":7},"Discover our Augmented Generation accelerator","\u002Fen\u002Fexpertise\u002Fai-implementation\u002Fzuhlke-augmented-generation",{"value":306,"processed":306,"format":255},"\u003Cp>RAG is a hybrid AI architecture that combines the generative power of LLMs with the precision of enterprise data. Here’s how it works:\u003C\u002Fp>\u003Col>\u003Cli>\u003Cstrong>Internal databases\u003C\u002Fstrong> are embedded with documents, such as research notes, policy terms, client records, and regulatory texts.\u003C\u002Fli>\u003Cli>\u003Cstrong>Semantic search\u003C\u002Fstrong> retrieves the most relevant content based on meaning, not just keywords.\u003C\u002Fli>\u003Cli>\u003Cstrong>Prompt engineering\u003C\u002Fstrong> \u003Cstrong>and model grounding\u003C\u002Fstrong> ensure responses are accurate, relevant, and traceable.\u003C\u002Fli>\u003C\u002Fol>\u003Cp>This approach enables AI agents to answer complex client queries with confidence, citing the exact clause or document that supports the answer.\u003C\u002Fp>",{"title":303,"url":304,"internal":7},{"__typename":251,"id":309,"ctaLink":204,"pgSharedText":310,"pgSharedTitle":313,"pgSharedCtaLink":204},"31213",{"value":311,"processed":312,"format":255},"\u003Cp>A compelling example of RAG in action is Zühlke’s collaboration with UNIQA, Austria‘s second largest insurer. By piloting an \u003Ca href=\"\u002Fnode\u002F26101\" data-entity-type=\"node\" data-entity-uuid=\"a2bfcbc3-2b68-4e9b-89eb-4893411b005e\" data-entity-substitution=\"canonical\">award-winning AI chatbot\u003C\u002Fa> to support insurance sales, we helped empower customer service teams to do more with less.\u003C\u002Fp>","\u003Cp>A compelling example of RAG in action is Zühlke’s collaboration with UNIQA, Austria‘s second largest insurer. By piloting an \u003Ca href=\"\u002Fen\u002Fabout-us\u002Fnews\u002Fzuhlke-and-uniqa-win-the-microsoft-visionary-award\" data-entity-type=\"node\" data-entity-uuid=\"a2bfcbc3-2b68-4e9b-89eb-4893411b005e\" data-entity-substitution=\"canonical\" title=\"Zühlke and UNIQA win the Microsoft Visionary Award\">award-winning AI chatbot\u003C\u002Fa> to support insurance sales, we helped empower customer service teams to do more with less.\u003C\u002Fp>","Real-world impact: UNIQA’s AI-powered people",{"__typename":315,"id":316,"caption":204,"imageRight":299,"pgSharedTitle":204,"pgSharedCtaLink":317,"textMedia":320,"pgSharedText":337},"ParagraphPgTextMedia","31214",{"title":318,"url":319,"internal":7},"Explore the full UNIQA success story","\u002Fen\u002Fcase-studies\u002Funiqa-ai-chatbot-increases-efficency",{"id":321,"sharedMediaLegend":204,"mediaImage":322,"name":336},"171870",{"url":323,"width":324,"height":325,"alt":326,"title":204,"size":327,"mime":273,"variations":204,"textMediaVariations":328},"https:\u002F\u002Fcms.zuehlke.com\u002Fsites\u002Fdefault\u002Ffiles\u002Fimages\u002FWoman%20looks%20at%20AI%20chatbot%20on%20phone.png",955,764,"Woman looks at AI chatbot on phone",663084,[329,331,332,333,335],{"name":276,"url":330,"width":278,"height":246},"https:\u002F\u002Fcms.zuehlke.com\u002Fsites\u002Fdefault\u002Ffiles\u002Fstyles\u002Ffixed_width_small\u002Fpublic\u002Fimages\u002FWoman%20looks%20at%20AI%20chatbot%20on%20phone.png.webp?itok=ZfHcWj8S",{"name":276,"url":330,"width":278,"height":246},{"name":276,"url":330,"width":278,"height":246},{"name":282,"url":334,"width":324,"height":325},"https:\u002F\u002Fcms.zuehlke.com\u002Fsites\u002Fdefault\u002Ffiles\u002Fstyles\u002Ffixed_width_medium\u002Fpublic\u002Fimages\u002FWoman%20looks%20at%20AI%20chatbot%20on%20phone.png.webp?itok=6ZuMRskh",{"name":282,"url":334,"width":324,"height":325},"Woman looks at AI chatbot on phone.png",{"value":338,"processed":338,"format":255},"\u003Cp>Collaboration outcomes:\u003C\u002Fp>\u003Cul>\u003Cli>\u003Cstrong>50% reduction\u003C\u002Fstrong> in time and effort to answer tariff-related questions.\u003C\u002Fli>\u003Cli>\u003Cstrong>95% response accuracy\u003C\u002Fstrong>, with cited sources and clarifying follow-ups.\u003C\u002Fli>\u003Cli>\u003Cstrong>NPS score of 80\u003C\u002Fstrong>, reflecting a significant boost in customer satisfaction.\u003C\u002Fli>\u003C\u002Ful>\u003Cp>By automating repetitive, document-heavy tasks, UNIQA’s teams can now focus on higher-value interactions. They’re now spending more time building trust, resolving complex issues, and deepening client relationships.\u003C\u002Fp>",{"__typename":251,"id":340,"ctaLink":341,"pgSharedText":344,"pgSharedTitle":204,"pgSharedCtaLink":346},"31215",{"title":342,"url":343,"internal":7},"Discover our AI services","\u002Fen\u002Fexpertise\u002Fai-implementation",{"value":345,"processed":345,"format":255},"\u003Cp>To sum up, the evolution of AI in client advisory services isn’t just about efficiency, it’s also about customer experience, accuracy, and compliance.  Achieving this level of performance requires more than prompt-engineering and LLMs alone.\u003C\u002Fp>\u003Cp>From our experience, RAG-based systems can achieve accuracy of up to 95%, though they still require a human in the loop. If higher accuracy is required, it’s necessary to train dedicated models.\u003C\u002Fp>\u003Ch2>The road ahead\u003C\u002Fh2>\u003Cp>As financial institutions continue to digitise and personalise services, the ability to deliver \u003Cstrong>accurate, timely, and trusted\u003C\u002Fstrong> information becomes a competitive differentiator. RAG delivers a scalable, secure, and explainable AI architecture that aligns with both regulatory expectations and customer demands.\u003C\u002Fp>\u003Cp>At Zühlke, we believe the future of client advice lies in \u003Cstrong>AI systems that are not just smart, but strategic\u003C\u002Fstrong>. 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