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McConnell","src":"https://www.snowflake.com/adobe/dynamicmedia/deliver/dm-aid--e2f76845-4c2e-44c0-a0f7-3cf019b1301c/sully.headshot.v1.jpg?quality=85&preferwebp=true","lazyEnabled":true,"width":"587","height":"671",":type":"snowflake-site/components/image"},"authorCta":{"id":"button-e17522a26e","showOutboundIcon":false,"buttonLink":{"valid":true,"url":"/en/blog/authors/sully-mcconnell/"},"linkTargetContentType":"DOCUMENT_LEARN","linkType":"SNOWFLAKE_INTERNAL",":type":"snowflake-site/components/button","text":"Sully McConnell"}}],"tag":{"tagText":"Servizi finanziari","tagColor":"#29B5E8"},"title":{"lines":["AI agentica nei servizi finanziari e assicurativi"],"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":"aem-GridColumn aem-GridColumn--default--12"},"columnCount":12,"appliedCssClassNames":"snowflake-layout-container-inner-padding-small",":items":{"blog_text":{"id":"blog-text-82fc68ef85","text":"\u003Cp\u003EMolte società di servizi finanziari stanno sperimentando con l’AI attraverso programmi pilota, ma rimangono diverse sfide da risolvere. Le preoccupazioni principali includono la sicurezza dei dati, l’accuratezza dei large language model (LLM) e il controllo rigoroso da parte delle autorità di regolamentazione del ruolo dell’AI nel processo decisionale finanziario. I casi d’uso attuali sono in gran parte interni, con alcune soluzioni chatbot rivolte ai clienti che rispondono a richieste di assistenza non critiche. \u003C/p\u003E\n\u003Cp\u003EDi recente, in tutti i settori, compresi i servizi finanziari, si è sviluppato l’interesse per gli agenti basati sull’AI generativa, spesso definiti “agentic AI” o AI agentica. L’AI agentica si basa sui LLM per eseguire i task in modo autonomo, utilizzando le capacità di comprensione, generazione e interazione del linguaggio naturale dei modelli. LLM fondamentali come Llama, Anthropic Claude, Mistral, GPT e Gemini vengono addestrati su enormi quantità di dati di testo (nella misura in cui stanno esaurendo i dati di testo per ulteriori training) e possono svolgere una varietà di attività relative al linguaggio. Questi modelli, se integrati in sistemi di AI agentica su una piattaforma come Snowflake, forniscono la base per comprendere il contesto, generare risposte, promuovere maggiore automazione ed efficienza e prendere decisioni in tempo reale più equilibrate in base agli input ricevuti. \u003C/p\u003E\n\u003Ch2\u003ESistemi agentici\u003C/h2\u003E\n\u003Cp\u003ENei sistemi agentici, un \u003Ci\u003Eagente coordinatore\u003C/i\u003E è un componente centrale che gestisce e dirige le attività di altri agenti (o sottosistemi) per raggiungere gli obiettivi generali. Questo agente di coordinamento funge tipicamente da direttore d’orchestra, orchestra le azioni, alloca le risorse e prende decisioni ad alto livello. Per svolgere i propri compiti in modo efficace, l’agente coordinatore può utilizzare strumenti, modelli o altri agenti \u003Ci\u003Eorientati al dominio\u003C/i\u003E o \u003Ci\u003Eorientati al task\u003C/i\u003E in modi complementari, garantendo che il sistema sia in grado di comprendere il contesto ed eseguire azioni specifiche per ottenere i risultati desiderati.\u003C/p\u003E\n\u003Ch3\u003EOrientato al dominio \u003C/h3\u003E\n\u003Cp\u003EI servizi orientati al dominio sono specializzati nella comprensione e nell'elaborazione del linguaggio relativo a uno specifico settore, campo o area di conoscenza. In quanto tali, possono generare risposte o decisioni più accurate, pertinenti e consapevoli del contesto. \u003C/p\u003E\n\u003Cp\u003ENell’AI agentica, i modelli orientati al dominio possono aiutare il sistema a comprendere e svolgere in modo più efficace task specifici. Nel settore assicurativo, ad esempio, tali modelli potrebbero generare testi delle polizze scritto con linguaggio più accurato, valutare i fattori di rischio o interpretare i dati dei sinistri, allineandosi agli standard del settore.\u003C/p\u003E\n\u003Ch3\u003EOrientata al task \u003C/h3\u003E\n\u003Cp\u003ED’altro canto, i servizi orientati ai task mirano al raggiungimento di obiettivi specifici. Questi modelli sono addestrati a comprendere la sequenza di passaggi necessari per completare uno specifico task e si integrano con sistemi più ampi per eseguire le azioni in modo autonomo. I modelli task-oriented sono progettati non solo per comprendere il linguaggio, ma anche per interagire con gli strumenti, recuperare i dati e intraprendere azioni per perseguire un obiettivo ben definito. \u003C/p\u003E\n\u003Ch3\u003ECortex Analyst: generazione di SQL ad alta precisione\u003C/h3\u003E\n\u003Cp\u003ESnowflake Cortex Analyst può essere considerato un \u003Ci\u003Eservizio agentico task-oriented\u003C/i\u003E perché ottimizzato per un obiettivo specifico, ovvero la generazione accurata di SQL per recuperare i dati da una tabella Snowflake. A tale scopo, Cortex Analyst esegue questi task:\u003C/p\u003E\n\u003Cul\u003E\n\u003Cli\u003E\u003Cp\u003E\u003Cb\u003EInterpretazione degli intenti degli utenti: \u003C/b\u003EIl modello elabora l’input di linguaggio naturale o informazioni contestuali (ad esempio, il tipo di dati che l’utente sta cercando) e verifica che esistano per rispondere alla domanda o risalire all’utente e chiedere chiarezza.\u003C/p\u003E\n\u003C/li\u003E\n\u003Cli\u003E\u003Cp\u003E\u003Cb\u003EGenerazione di query SQL fruibili: \u003C/b\u003Esulla base di questa interpretazione, crea codice SQL che può essere eseguito per recuperare o manipolare i dati pertinenti in un database. La sua accuratezza nella generazione di SQL deriva dalla comprensione del contesto di ciò che è memorizzato in ogni tabella. \u003C/p\u003E\n\u003C/li\u003E\n\u003Cli\u003E\u003Cp\u003E\u003Cb\u003ECompletamento task: \u003C/b\u003El’obiettivo finale di questo processo è generare un SQL altamente accurato che soddisfi una specifica esigenza di query o estrazione di dati, un task chiaro e definito.\u003C/p\u003E\n\u003C/li\u003E\n\u003C/ul\u003E\n\u003Ch2\u003ESistemi agentici nel settore assicurativo\u003C/h2\u003E\n\u003Cp\u003EPer ottenere insight fruibili, le aziende, soprattutto in settori complessi come quello assicurativo, devono spingersi oltre l’accesso ai database e attingere anche a contratti, documenti e altri dati non strutturati che possono essere cercati utilizzando un altro tipo di servizio orientato al task: un vector store. \u003C/p\u003E\n\u003Cp\u003ERappresentando i documenti come vettori numerici, gli agenti AI possono cercare e leggere automaticamente i documenti delle richieste di risarcimento, estrarre dettagli pertinenti (come tipo di incidente, valutazione dei danni, copertura delle polizze, ecc.) e generare query SQL per recuperare dati aggiuntivi da un database delle richieste di risarcimento o da un sistema di polizze.\u003C/p\u003E\n\u003Cp\u003EUn LLM può quindi essere utilizzato per formulare raccomandazioni, aggiornare lo stato della richiesta o avviare ulteriori azioni, il tutto grazie al coordinamento tra la comprensione dei documenti (dati non strutturati) e l’interazione con il database (strutturato).\u003C/p\u003E\n\u003Ch2\u003EIl ruolo di Accenture e Snowflake nel facilitare l’intelligenza artificiale\u003C/h2\u003E\n\u003Cp\u003EAccenture e Snowflake sono in prima linea nell’abilitare l’AI agentica per le aziende, per rispondere a esigenze sia tecnologiche che strategiche. Il nostro approccio si concentra su:\u003C/p\u003E\n\u003Cul\u003E\n\u003Cli\u003E\u003Cp\u003EFornire una piattaforma dati e AI unificata con strumenti e funzionalità all’avanguardia che consente a sviluppatori, data scientist e team dati di creare e distribuire rapidamente applicazioni e modelli AI con sicurezza e governance di livello enterprise, con la facilità dell’infrastruttura gestita Snowflake\u003C/p\u003E\n\u003C/li\u003E\n\u003Cli\u003E\u003Cp\u003ELa governance unificata end-to-end, dall’ingestion all’applicazione, consente ai team di fornire una nuova ondata di data agent che utilizzano il recupero preconfigurato ad alta precisione per dati strutturati e non strutturati\u003C/p\u003E\n\u003C/li\u003E\n\u003Cli\u003E\u003Cp\u003EGuidare i clienti nella selezione di casi d’uso di alto valore, avvalendosi della competenza di Accenture nella consulenza di dirigenti di serie C e creando valore per i clienti attraverso la piattaforma dati e AI unificata Snowflake che semplifica il processo\u003C/p\u003E\n\u003C/li\u003E\n\u003C/ul\u003E\n\u003Ch3\u003EUna piattaforma AI Data Cloud all-in-one\u003C/h3\u003E\n\u003Cp\u003ESnowflake e Accenture offrono una soluzione completa per l’enterprise AI, progettata per gestire efficacemente i casi d’uso dell’AI agentica:\u003C/p\u003E\n\u003Cul\u003E\n\u003Cli\u003E\u003Cp\u003E\u003Cb\u003ETutti i dati in un unico posto: \u003C/b\u003ESnowflake fornisce una piattaforma scalabile e unificata per gestire tutte le tue esigenze di dati, che si tratti di tabelle Snowflake o file PDF nell’object storage, in un unico posto, riducendo i silos, migliorando la qualità dei dati e promuovendo un’integrazione perfetta. \u003C/p\u003E\n\u003C/li\u003E\n\u003Cli\u003E\u003Cp\u003E\u003Cb\u003EGovernance robusta: \u003C/b\u003Ela nostra piattaforma governata garantisce la sicurezza e la conformità dei dati, dall’ingestion dei dati all’applicazione AI, in modo che nessuno dei tuoi dati venga utilizzato per addestrare modelli esterni (a meno che tu non dia istruzioni per il fine‐tuning). Questo rende Snowflake una scelta eccezionale per la sperimentazione e l’implementazione di nuovi casi d’uso di AI agentica.\u003C/p\u003E\n\u003C/li\u003E\n\u003Cli\u003E\u003Cp\u003E\u003Cb\u003EStrumenti AI creati per le aziende\u003C/b\u003E\u003C/p\u003E\n\u003Cul\u003E\n\u003Cli\u003E\u003Cp\u003E\u003Cb\u003ESnowflake Cortex Search e Snowflake Cortex Analyst:\u003C/b\u003E interagisci con i dati aziendali attraverso interfacce conversazionali e fornisci un accesso sicuro ai LLM fondamentali.\u003C/p\u003E\n\u003C/li\u003E\n\u003Cli\u003E\u003Cp\u003E\u003Cb\u003EDocument AI: \u003C/b\u003Eautomatizza l’elaborazione di vari tipi di documenti, dai moduli standard alle note scritte a mano.\u003C/p\u003E\n\u003C/li\u003E\n\u003Cli\u003E\u003Cp\u003E\u003Cb\u003ESnowflake ML: \u003C/b\u003Econsente ai data scientist di creare, addestrare e distribuire modelli di machine learning o personalizzare i modelli di embedding direttamente all’interno di Snowflake utilizzando linguaggi di programmazione familiari come Python, sfruttando al contempo le potenti funzionalità di elaborazione dati di Snowflake per flussi di lavoro scalabili ed efficienti.\u003C/p\u003E\n\u003C/li\u003E\n\u003C/ul\u003E\n\u003C/li\u003E\n\u003Cli\u003E\u003Cp\u003E\u003Cb\u003ECompetenza nel settore: \u003C/b\u003EAccenture comprende le esigenze dei clienti e come risolvere i problemi utilizzando l’AI agentica.\u003C/p\u003E\n\u003C/li\u003E\n\u003C/ul\u003E\n\u003Cp\u003ECombinando queste capacità, Snowflake e Accenture consentono alle aziende di personalizzare le proprie soluzioni AI mantenendo flessibilità e controllo. \u003C/p\u003E\n\u003Ch3\u003EUn framework strategico per l’adozione dell’AI\u003C/h3\u003E\n\u003Cp\u003EL’implementazione dell’AI agentica richiede un approccio strutturato. Ecco cosa consigliamo alle aziende per orientarsi nel loro percorso di adozione:\u003C/p\u003E\n\u003Cul\u003E\n\u003Cli\u003E\u003Cp\u003E\u003Cb\u003EIniziare con i casi d’uso che sbloccano un alto valore aziendale: \u003C/b\u003EConcentrarsi sui casi d’uso in cui è accettabile un’accuratezza approssimativa. Dai la priorità alle aree in cui processi decisionali più rapidi o accelerazione dei processi forniscono più valore di un’accurata precisione. Trova l’equilibrio tra gestione del rischio e creazione di valore rapida sul mercato.\u003C/p\u003E\n\u003C/li\u003E\n\u003Cli\u003E\u003Cp\u003E\u003Cb\u003EPuntare innanzitutto sulle aree a bassa regolamentazione:\u003C/b\u003E In settori altamente regolamentati, come i servizi finanziari, l’implementazione dell’AI nelle aree ad alta intensità di conformità può essere più lenta a causa di rigorosi standard di assistenza e processi di approvazione. Inizia con domini meno regolamentati per ottenere risultati più rapidi, affrontando gradualmente i casi d’uso regolamentati man mano che l’affidabilità dell’AI aumenta.\u003C/p\u003E\n\u003C/li\u003E\n\u003Cli\u003E\u003Cp\u003E\u003Cb\u003EMantenere un essere umano nel loop (per ora):\u003C/b\u003E Per mitigare i rischi legati alla explainability, al servizio clienti e all’accuratezza, mantieni la supervisione umana nei processi basati sull’AI. Man mano che la tecnologia matura e l’affidabilità aumenta, è possibile ridurre gradualmente l’intervento umano. Tuttavia, per i casi d’uso regolamentati o ad alto rischio, la revisione umana dovrebbe rimanere una componente chiave fino a quando non sarà completamente stabilita l’affidabilità dell’AI.\u003C/p\u003E\n\u003C/li\u003E\n\u003C/ul\u003E\n\u003Ch2\u003ECaso d’uso: agente AI per i sinistri di Accenture\u003C/h2\u003E\n\u003Cp\u003EAccenture ha sfruttato la piattaforma Snowflake per creare un \u003Ca href=\"https://www.snowflake.com/content/dam/snowflake-site/en/resource-library/pdfs/solution-briefs/Accenture-FSI-Claims-AI.pdf\" target=\"_blank\" rel=\"nofollow noopener noreferrer\"\u003Eagente sinistri assicurativi basato sull’AI\u003C/a\u003E rivoluzionario, combinando gli strumenti Snowflake per automatizzare le parti chiave del processo di rimborso con l’esperienza di Accenture nell’AI agentica. Questo agente AI può esaminare i documenti, riassumere le informazioni, prendere decisioni sulle richieste di risarcimento e generare lettere di risarcimento personalizzate per i clienti, spiegando i motivi alla base di un’approvazione o di un rifiuto.\u003C/p\u003E\n\u003Cp\u003ESecondo Mike Lao, responsabile del team dati e AI di Accenture, “Alla base del nostro Gen AI Claims Agent c’è l’AI Data Cloud Snowflake, che include funzionalità come Document AI in grado di elaborare vari documenti, come patenti di guida, ricevute e moduli. Utilizziamo anche Cortex Analyst di Snowflake, che aiuta l’AI agent per i sinistri ad analizzare dati e documenti di policy per prendere decisioni informate.”\u003C/p\u003E\n\u003Cp\u003EL’attenzione di Accenture alle richieste di risarcimento deriva da insight relativi al settore dei servizi finanziari, in cui l’approvazione delle richieste di risarcimento non sempre richiede un’accuratezza del 100%. È interessante notare che i clienti sono spesso disposti ad accettare pagamenti parziali in cambio di un’elaborazione più rapida. Come rilevato da Accenture, c’è margine per un pagamento flessibile delle richieste di risarcimento: le decisioni possono essere impugnate internamente, evitando interventi normativi. Questo contrasta con i requisiti di precisione più stringenti di aree come la consulenza finanziaria, che richiede responsabilità fiduciaria. I sinistri, al confronto, sono meno regolamentati e offrono alle compagnie assicurative un margine di manovra maggiore per adeguare i pagamenti entro i propri margini. La capacità di accelerare i pagamenti può migliorare in modo significativo sia i profitti che la soddisfazione dei clienti.\u003C/p\u003E\n\u003Cp\u003EPer garantire affidabilità e soddisfazione dei clienti, Accenture ha incluso l’ingegno umano nell’AI Claims Agent. Mike Lao ha sottolineato: \"Tenere un essere umano al corrente è importante. Utilizziamo l’AI per reinventare i processi di lavoro e scalare l’adozione dell’AI nelle organizzazioni per soddisfare le aspettative dei clienti.”\u003C/p\u003E\n\u003Cp\u003EIl team Accenture di Manila, Filippine, offre una profonda esperienza nel pagamento delle richieste di risarcimento danni alle compagnie assicurative globali. Questo progetto segna il primo passo verso una più ampia adozione degli agenti AI in vari settori dei servizi finanziari. Come ha osservato Kaushik GD, Head of Financial Services di Snowflake in APJ, “in futuro ci aspettiamo che gli agenti AI assistano nella pianificazione finanziaria e nella consulenza personalizzata sugli investimenti. 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