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12, 2026","authors":[{"authorImage":{"id":"image-dc3a629989","src":"https://www.snowflake.com/adobe/dynamicmedia/deliver/dm-aid--033c2dfe-61af-4c66-9a61-c8954f343e69/sarahhurd-headshot-final.png?quality=85&preferwebp=true","alt":"Sarah Hurd","lazyEnabled":true,"height":"1200","width":"896",":type":"snowflake-site/components/image"},"authorCta":{"id":"button-27efa73c37","showOutboundIcon":false,"buttonLink":{"valid":true,"url":"/en/blog/authors/sarah-hurd/"},"linkTargetContentType":"DOCUMENT_LEARN",":type":"snowflake-site/components/button","linkType":"SNOWFLAKE_INTERNAL","text":"Sarah Hurd"}}],"tag":{"tagText":"금융 서비스","tagColor":"#29B5E8"},"image":{"id":"image-2ebf9a51bb","src":"https://www.snowflake.com/adobe/dynamicmedia/deliver/dm-aid--0b3bbbc8-4cc3-45b0-b760-f49670d1e59f/agenticai-fsi.jpg?quality=85&preferwebp=true","alt":"Three people having a meeting in a conference room","lazyEnabled":true,"height":"2160","width":"3840",":type":"snowflake-site/components/image"},"title":{"lines":["금융 서비스 산업, 성과 측정과 거버넌스를 기반으로 에이전틱 AI 확장"],"type":"heading2",":type":"snowflake-site/components/title-v2"},":type":"snowflake-site/components/blog/blog-hero"}},":itemsOrder":["blog_hero"]},"responsivegrid_content":{"gridClassNames":"aem-Grid aem-Grid--12 aem-Grid--default--12","columnClassNames":{"blog_text_153020208":"aem-GridColumn aem-GridColumn--default--12","blog_text_1415833133":"aem-GridColumn aem-GridColumn--default--12","blog_text":"aem-GridColumn aem-GridColumn--default--12","quote_item_543698975":"aem-GridColumn aem-GridColumn--default--12","quote_item":"aem-GridColumn aem-GridColumn--default--12"},"columnCount":12,"appliedCssClassNames":"snowflake-layout-container-inner-padding-small",":items":{"blog_text":{"id":"blog-text-a195bf1863","text":"\u003Cp\u003E\u003Cb\u003E핵심 요약\u003C/b\u003E\u003C/p\u003E\n\u003Cul\u003E\n\u003Cli\u003E\u003Cp\u003E\u003Cb\u003E금융 서비스 기업들은 AI 활용 측면에서 빠르게 발전해 왔으며, AI 파일럿과 제한적인 사용 사례를 넘어 이제는 성과에 대한 책임을 중시하는 단계로 전환하고 있습니다.\u003C/b\u003E 기업들은 측정 가능한 비즈니스 가치에 점점 더 집중하고 있으며, 금융 서비스 응답자의 68%는 생성형 AI를 통해 정량화 가능한 긍정적 ROI를 얻고 있다고 답했습니다.\u003C/p\u003E\n\u003C/li\u003E\n\u003Cli\u003E\u003Cp\u003E\u003Cb\u003E에이전틱 AI는 다음 단계의 핵심 영역으로 부상하고 있지만, 이를 안전하게 확장할 수 있는 기업을 가르는 요소는 거버넌스가 될 것입니다.\u003C/b\u003E 금융 서비스 기업의 30%가 이미 에이전틱 AI를 프로덕션 환경에서 운영하고 있으며, 이를 활용 중인 기업들은 분석, 예측, 고객 상호작용, 프로세스 자동화에서 강력한 성과를 보이고 있습니다. 이에 따라 권한, 감사 가능성, 감독, 데이터 액세스에 대한 통제는 필수적입니다.\u003C/p\u003E\n\u003C/li\u003E\n\u003Cli\u003E\u003Cp\u003E\u003Cb\u003EAI는 단순한 자동화를 넘어 인력 측면에서도 긍정적인 변화를 만들어내고 있습니다.\u003C/b\u003E 금융 서비스 기업은 AI가 업무 환경에 미치는 긍정적 효과를 다른 산업보다 더 크게 체감하고 있습니다. 실제로 응답자의 78%는 AI 기반 자동화가 일자리에 전반적으로 긍정적인 영향을 미쳤다고 답했습니다.\u003C/p\u003E\n\u003C/li\u003E\n\u003Cli\u003E\u003Cp\u003E\u003Cb\u003E독점 데이터는 AI 경쟁 우위의 기반이 되고 있습니다.\u003C/b\u003E 금융 서비스 기업의 92%가 독점 데이터를 활용해 대규모 언어 모델을 학습, 튜닝 또는 보강하고 있다고 답했습니다. 이는 차별화된 AI 성과를 창출하는 데 있어 신뢰할 수 있고 거버넌스가 적용된 엔터프라이즈 데이터가 얼마나 중요한지를 보여줍니다.\u003C/p\u003E\n\u003C/li\u003E\n\u003C/ul\u003E\n\u003Cp\u003E금융 서비스 리더들이 AI를 바라보는 관점은 빠르게 변화하고 있습니다. 이제 중요한 질문은 인공지능이 이 산업을 근본적으로 재편할 것인지가 아닙니다. 이미 변화는 시작되었습니다. 이제 금융 서비스 기업의 과제는 분명합니다. 업계에서 요구되는 신뢰, 거버넌스, 보안, 통제 수준을 유지하면서 AI를 측정 가능한 비즈니스 가치로 연결하는 것입니다.\u003C/p\u003E\n\u003Cp\u003E\u003Ca rel=\"nofollow noopener noreferrer\" target=\"_blank\" href=\"https://www.snowflake.com/ko/lp/radical-roi-generative-ai/?utm_cta=website-resource-homepage-promo-card-radical-roi-gen-ai\"\u003ESnowflake의 새로운 연구\u003C/a\u003E에 따르면 금융 서비스 기업들의 AI 도입은 한층 성숙한 단계에 접어들고 있습니다. 기업마다 도입 성숙도에는 차이가 있지만, 이들은 ROI를 정량화하고, 독점 데이터를 활용하며, 고부가가치 엔터프라이즈 워크플로우에 AI를 적용하고, 에이전틱 AI의 초기 이점을 포착하기 시작했습니다.\u003C/p\u003E\n\u003Cp\u003E이는 금융 서비스 산업이 AI를 실험적으로 탐색하던 단계를 넘어, 이제 성과와 책임성을 본격적으로 요구하는 단계로 빠르게 나아가고 있음을 보여줍니다.\u003C/p\u003E\n\u003Ch2\u003EAI 기반 자동화가 일자리에 미친 긍정적인 영향\u003C/h2\u003E\n\u003Cp\u003E이번 연구에서 가장 눈에 띄는 결과 중 하나는 AI 기반 자동화가 인력에 미치는 긍정적인 영향 측면에서 금융 서비스가 비교 대상 산업 중 가장 앞서 있다는 점입니다. 비교 대상 산업에는 광고 및 미디어, 헬스케어 및 라이프사이언스, 제조 산업, 리테일/소비재, 테크놀로지 및 통신 산업이 포함됩니다. \u003C/p\u003E\n\u003Cp\u003E금융 서비스 응답자의 78%는 AI 기반 자동화가 일자리에 전반적으로 긍정적인 영향을 미쳤다고 답했습니다. 이는 테크놀로지 및 통신(75%), 헬스케어 및 라이프사이언스(69%), 광고 및 미디어(68%), 제조 산업(68.1%), 리테일/소비재(61%)보다 높은 수치입니다.\u003Cbr\u003E\n \u003C/p\u003E\n","richText":true,":type":"snowflake-site/components/blog/blog-text"},"quote_item":{"id":"quote-item-138cb138fa","alignment":"left","layout":"card","showQuoteIcon":false,"quote":"금융 서비스 응답자의 78%는 AI 기반 자동화가 일자리에 전반적으로 긍정적인 영향을 미쳤다고 답했습니다.","showHangingPunctuation":false,":type":"snowflake-site/components/quote-item"},"blog_text_1415833133":{"id":"blog-text-accbc0ad2d","text":"\u003Cp\u003E이 결과는 AI 도입이 곧 일자리 축소로 이어진다는 일반적인 인식과는 다른 흐름을 보여줍니다. 금융 서비스에서는 더 복합적인 양상이 나타납니다. AI는 업무 방식을 변화시키고 있지만, 응답자들은 이러한 변화를 긍정적으로 보는 경향이 더 큽니다.\u003C/p\u003E\r\n\u003Cp\u003E금융 서비스 업무의 특성을 고려하면 자연스러운 결과입니다. 이 업계의 직원들은 복잡하고 반복적이며 데이터 집약적인 문서 중심 업무를 자주 처리해야 하기 때문입니다. AI는 문서 요약, 데이터 준비, 규정 준수 모니터링, 헬프 데스크 티켓 자동화와 같은 작업 전반에서 수작업 부담을 줄이는 데 도움을 줄 수 있습니다.\u003C/p\u003E\r\n\u003Cp\u003E경영진은 이를 통해 인력 문제를 다른 관점에서 바라볼 수 있습니다. 중요한 것은 사람을 자동화로 대체하는 것이 아니라, 직원들이 더 빠르게 일하고 더 나은 의사결정을 내리며 더 높은 가치의 업무에 집중할 수 있도록 지원하는 데 있습니다.\u003C/p\u003E\r\n\u003Ch2\u003E금융 서비스 업계의 생성형 AI ROI 측정 본격화 \u003C/h2\u003E\r\n\u003Cp\u003E금융 서비스 기업들은 생성형 AI 도입을 측정 가능한 가치로 연결할 가능성이 가장 높은 기업군 중 하나이기도 합니다. 금융 서비스 응답자의 68%는 생성형 AI의 ROI가 긍정적이라는 사실을 정량적으로 확인했다고 답했습니다. 이보다 약간 높은 산업은 테크놀로지 및 통신(70%)이며, 금융 서비스는 광고 및 미디어(64%), 리테일/소비재(59%), 헬스케어 및 라이프사이언스(57%), 제조 산업(56%)을 앞서고 있습니다.\u003C/p\u003E\r\n\u003Cp\u003E이는 주목할 만한 신호입니다. 금융 서비스 기업들은 다른 비즈니스 영역에서와 마찬가지로 AI에도 성과 중심의 엄격한 관리 기준을 적용하고 있는 것으로 보입니다.\u003C/p\u003E\r\n\u003Cp\u003E이와 같은 엄격함은 중요합니다. 비즈니스 영향을 정량화하면 리더는 어떤 사용 사례를 확장하고, 어떤 파일럿을 중단하며, 어디에 투자를 배분할지 판단할 수 있습니다. 또한 AI 실험이 파편화되거나 비즈니스 전략과 단절되는 것을 방지하는 데도 도움이 됩니다.\u003C/p\u003E\r\n\u003Cp\u003E금융 서비스 리더에게 필요한 접근 방식은 명확합니다. AI 전략은 새로운 기술을 도입하는 데 그치지 않고, 측정 가능한 비즈니스 성과를 창출하는 방향으로 설계되어야 합니다.\u003C/p\u003E\r\n\u003Ch2\u003EAI 경쟁력의 기반으로 부상하는 독점 데이터\u003C/h2\u003E\r\n\u003Cp\u003E금융 서비스에서 데이터는 언제나 전략적 자산이었습니다. 이제 AI의 확산으로 독점 데이터는 기업의 AI 경쟁력을 좌우하는 핵심 자산이 되고 있습니다. 금융 서비스 응답자의 92%는 조직이 독점 데이터로 대규모 언어 모델을 학습, 튜닝 또는 보강하고 있다고 답했습니다. \u003C/p\u003E\r\n\u003Cp\u003E이는 금융 서비스가 테크놀로지 및 통신 산업(96%), 헬스케어 및 라이프사이언스(93%), 광고 및 미디어(93%), 리테일/소비재(91%), 제조 산업(88%)과 함께 고도화된 AI 도입 단계에 있음을 시사합니다.\u003C/p\u003E\r\n\u003Cp\u003E이는 중요한 변화입니다. 일반적인 AI 도구로는 일반적인 결과만 생성할 수 있습니다. 하지만 상호운용 가능한 AI 시스템은 독점 엔터프라이즈 데이터를 기반으로 더 관련성 높고, 맥락에 맞으며, 차별화된 결과를 제공할 수 있습니다.\u003C/p\u003E\r\n\u003Cp\u003E금융 서비스 기업의 경우, 이는 다음과 같은 AI 시스템을 의미할 수 있습니다.\u003C/p\u003E\r\n\u003Cul\u003E\r\n\u003Cli\u003E\u003Cp\u003E고객별 서비스 추천\u003C/p\u003E\r\n\u003C/li\u003E\r\n\u003Cli\u003E\u003Cp\u003E리스크 및 사기 분석\u003C/p\u003E\r\n\u003C/li\u003E\r\n\u003Cli\u003E\u003Cp\u003E내부 정책 및 규정 준수 가이드\u003C/p\u003E\r\n\u003C/li\u003E\r\n\u003Cli\u003E\u003Cp\u003E시장 및 시나리오 분석\u003C/p\u003E\r\n\u003C/li\u003E\r\n\u003Cli\u003E\u003Cp\u003E개인화된 금융 인사이트\u003C/p\u003E\r\n\u003C/li\u003E\r\n\u003Cli\u003E\u003Cp\u003E상품 및 포트폴리오 인텔리전스\u003C/p\u003E\r\n\u003C/li\u003E\r\n\u003Cli\u003E\u003Cp\u003E엔터프라이즈 지식 관리\u003C/p\u003E\r\n\u003C/li\u003E\r\n\u003C/ul\u003E\r\n\u003Cp\u003E하지만 독점 데이터 활용은 더 큰 책임을 수반합니다. AI 시스템이 민감한 엔터프라이즈 및 고객 데이터에 더 많이 의존할수록 액세스를 관리하고, 품질을 모니터링하며, 개인정보를 보호하고, 투명성을 유지하는 일이 더욱 중요해집니다. \u003C/p\u003E\r\n\u003Cp\u003E결국 금융 서비스에서 AI 경쟁력은 강력한 거버넌스가 뒷받침될 때 지속될 수 있습니다.\u003C/p\u003E\r\n\u003Ch2\u003E에이전틱 AI, 새로운 경쟁력의 중심으로\u003C/h2\u003E\r\n\u003Cp\u003E생성형 AI는 직원이 정보와 상호작용하는 방식을 완전히 바꿔 놓았습니다. 에이전틱 AI는 AI가 직원을 대신해 수행할 수 있는 일의 범위를 바꿉니다.\u003C/p\u003E\r\n\u003Cp\u003E에이전틱 AI 시스템은 정해진 범위 내에서 작업을 추론하고, 도구를 사용하며, 단계를 조율하고, 워크플로우를 완료할 수 있습니다. 금융 서비스 분야에서 이는 리스크와 규제, 운영, 규정 준수, 사기 조사, 예측, 전략 계획 전반에 걸쳐 새로운 형태의 자동화 가능성을 열어줍니다.\u003C/p\u003E\r\n\u003Cp\u003E프로덕션 환경에서의 에이전틱 AI 도입률만 놓고 보면, 금융 서비스 업계가 선두는 아닙니다. 현재 에이전틱 AI를 프로덕션에서 사용 중이라고 답한 금융 서비스 응답자는 30%로, 광고 및 미디어(42%), 헬스케어 및 라이프사이언스(33%), 테크놀로지 및 통신 산업(32%), 제조 산업(32%)보다 낮으며, 리테일/소비재(28%)보다는 높았습니다.\u003C/p\u003E\r\n\u003Cp\u003E하지만 이 기술을 도입하고 있는 조직들 가운데 금융 서비스 기업들은 특히 강력한 성과를 보이고 있습니다.\u003C/p\u003E\r\n\u003Cul\u003E\r\n\u003Cli\u003E\u003Cp\u003E94%는 에이전틱 AI를 통해 데이터 분석과 전략적 의사 결정 지원이 개선되었다고 답했습니다.\u003C/p\u003E\r\n\u003C/li\u003E\r\n\u003Cli\u003E\u003Cp\u003E92%는 고급 모델링을 통해 예측과 시나리오 계획이 향상됐다고 답했습니다.\u003C/p\u003E\r\n\u003C/li\u003E\r\n\u003Cli\u003E\u003Cp\u003E91%는 에이전트를 통해 고객 대면 상호 작용이 개선됐다고 답했습니다.\u003C/p\u003E\r\n\u003C/li\u003E\r\n\u003Cli\u003E\u003Cp\u003E83%는 반복적이고 규칙 기반인 프로세스에서 사람의 개입을 대체하거나 줄였다고 답했습니다.\u003C/p\u003E\r\n\u003C/li\u003E\r\n\u003Cli\u003E\u003Cp\u003E71%는 복잡하고 정교한 프로세스에서 사람의 개입을 대체하거나 줄였다고 답했습니다.\u003C/p\u003E\r\n\u003C/li\u003E\r\n\u003C/ul\u003E\r\n\u003Cp\u003E이러한 성과는 금융 서비스 분야와 특히 관련이 깊습니다. 해당 산업의 업무 상당 부분이 데이터 집약적이고, 프로세스 중심적이며, 의사 결정 중심적이기 때문입니다. 에이전틱 워크플로우의 다음 단계는 팀이 다단계 고객 워크플로우를 관리하도록 지원하는 것부터 사기 조사, 온보딩 간소화, 시나리오 계획 지원에 이르기까지 광범위한 사용 사례를 포괄합니다.\u003Cbr\u003E\r\n \u003C/p\u003E\r\n","richText":true,":type":"snowflake-site/components/blog/blog-text"},"quote_item_543698975":{"id":"quote-item-bafed95b19","alignment":"left","layout":"card","showQuoteIcon":false,"quote":"에이전틱 워크플로우는 다단계 워크플로우 관리와 사기 조사부터 온보딩 간소화, 시나리오 계획 지원까지 다양한 사용 사례로 확장되고 있습니다.","showHangingPunctuation":false,":type":"snowflake-site/components/quote-item"},"blog_text_153020208":{"id":"blog-text-a42c581402","text":"\u003Cp\u003E그리고 그 핵심은 데이터 거버넌스입니다. AI 시스템이 답변을 생성하는 단계에서 행동을 수행하는 단계로 발전함에 따라, 금융 서비스 기업은 권한, 감사 가능성, 사람의 감독, 데이터 액세스에 대한 강력한 통제가 필요합니다. 예를 들어 민감 데이터는 자동으로 분류되어야 하고, 데이터 계보도 자동으로 캡처되어야 합니다. 또한 액세스 정책은 개별 소프트웨어나 애플리케이션 수준이 아니라 플랫폼 수준에서 적용되어야 합니다. 나아가 에이전틱 제어 영역이나 에이전틱 미션 컨트롤 센터를 구현하는 기업은 경쟁 우위를 확보할 수 있습니다. 이 계층은 데이터, 모델, 애플리케이션을 조율해 에이전트가 명확히 정의된 가드레일 내에서 공동의 목표를 달성하도록 지원하므로, 운영 리스크를 완화하는 데 매우 중요합니다. \u003C/p\u003E\r\n\u003Ch2\u003EAI 확장의 성패를 가르는 데이터 준비성과 컨텍스트\u003C/h2\u003E\r\n\u003Cp\u003E가장 성숙한 AI 전략은 엔터프라이즈 데이터의 품질, 접근성, 거버넌스뿐만 아니라 데이터의 비즈니스 컨텍스트 또는 \u003Ca rel=\"nofollow noopener noreferrer\" target=\"_blank\" href=\"https://www.snowflake.com/ko/blog/agent-context-layer-trustworthy-data-agents/\"\u003E시맨틱\u003C/a\u003E에 달려 있습니다. AI 결과물의 품질은 데이터 자체와 그 데이터에 담긴 비즈니스 의미가 결합된 수준만큼 강력해집니다.\u003C/p\u003E\r\n\u003Cp\u003E고객 확인 절차(Know Your Customer) 검증 워크플로우와 거래 결제 예외를 구분할 수 있는 에이전트라면, 팀이 실제로 채택하여 조직 전반에서 실질적인 가치를 창출할 수 있습니다.\u003C/p\u003E\r\n\u003Cp\u003E금융 서비스 리더들은 특히 데이터 액세스 문제가 AI 성공의 핵심 과제임을 잘 알고 있습니다. 응답자의 96%는 조직이 데이터 자산 환경을 통합하거나 일원화하는 솔루션에 적극적으로 투자하고 있다고 답했습니다. 또한 89%는 데이터 엔지니어링이 AI 프로젝트 성공의 핵심이라고 답했습니다.\u003C/p\u003E\r\n\u003Cp\u003E하지만 과제는 여전히 남아 있습니다.\u003C/p\u003E\r\n\u003Cul\u003E\r\n\u003Cli\u003E\u003Cp\u003E62.8%는 조직에 데이터 엔지니어링 역량 격차가 있다고 답했으며,\u003C/p\u003E\r\n\u003C/li\u003E\r\n\u003Cli\u003E\u003Cp\u003E57%는 분산된 데이터 시스템과 사일로로 인해 AI 이니셔티브가 지연되고 있다고 답했습니다.\u003C/p\u003E\r\n\u003C/li\u003E\r\n\u003Cli\u003E\u003Cp\u003E51%는 조직이 전체 데이터 자산 환경에 대한 가시성을 충분히 확보하지 못하고 있다고 답했습니다.\u003C/p\u003E\r\n\u003C/li\u003E\r\n\u003C/ul\u003E\r\n\u003Cp\u003E이 지점이 바로 전략적 병목이자 전략적 기회입니다. 금융 서비스 기업들이 AI에 대한 뚜렷한 목표를 세우고 있더라도, 데이터가 파편화되어 있으면 AI 확장에는 한계가 있을 수밖에 없습니다. 데이터가 사일로화되어 있거나, 일관성이 없거나, 거버넌스를 적용하기 어렵다면 AI 시스템은 신뢰할 수 있는 결과와 안정적이며 규정을 준수하는 작업을 수행하는 데 어려움을 겪게 됩니다.\u003C/p\u003E\r\n\u003Cp\u003E에이전틱 AI에서는 이 기반이 더욱 중요합니다. 사용자를 대신해 행동하는 시스템은 정확하고 거버넌스가 적용된 컨텍스트 데이터에 액세스할 수 있어야 합니다. 이러한 기반이 없다면 기업은 단발성 사용 사례에서 엔터프라이즈 전환으로 나아가는 데 어려움을 겪을 수 있습니다.\u003C/p\u003E\r\n\u003Ch2\u003E금융 서비스 AI 리더십의 다음 단계\u003C/h2\u003E\r\n\u003Cp\u003E금융 서비스 산업이 AI 도입의 새로운 단계에 진입한 것은 분명한 사실입니다. 데이터는 이 산업이 점점 더 성숙하고, 규율을 갖추며, 성과 중심적으로 변화하고 있음을 보여줍니다. 하지만 에이전틱 워크플로우의 다음 단계로 넘어가려면 보다 포괄적인 접근 방식이 필요합니다. \u003C/p\u003E\r\n\u003Cp\u003E이를 위해서는 신뢰할 수 있는 데이터 기반, 강력한 거버넌스와 시맨틱 체계, 엔터프라이즈급 도구, 인력 역량 강화, 일관된 측정이 필요합니다.\u003C/p\u003E\r\n\u003Cp\u003E경영진과 의사 결정권자가 나아갈 방향은 명확합니다.\u003C/p\u003E\r\n\u003Cul\u003E\r\n\u003Cli\u003E\u003Cp\u003E측정 가능한 비즈니스 가치를 제공하는 AI 사용 사례를 우선순위에 두기\u003C/p\u003E\r\n\u003C/li\u003E\r\n\u003Cli\u003E\u003Cp\u003E거버넌스가 적용된 컨텍스트 기반 독점 데이터에 기반한 AI 시스템 구축하기\u003C/p\u003E\r\n\u003C/li\u003E\r\n\u003Cli\u003E\u003Cp\u003E선도적인 AI 모델이 기업 데이터에서 직접 작동할 수 있도록 지원하는 상호운용 가능한 데이터 플랫폼에 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