[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"article-ai-big-data-blockchain-finance-convergence-en":3,"tags-ai-big-data-blockchain-finance-convergence-en":34,"related-lang-ai-big-data-blockchain-finance-convergence-en":43,"related-posts-ai-big-data-blockchain-finance-convergence-en":47,"series-research-8e325341-ee9d-4b99-bffc-9fd818221970":84},{"id":4,"title":5,"content":6,"summary":7,"source":8,"source_url":9,"author":10,"image_url":11,"keywords":12,"language":18,"translated_content":10,"views":19,"is_premium":20,"created_at":21,"updated_at":21,"cover_image":11,"published_at":22,"rewrite_status":23,"rewrite_error":10,"rewritten_from_id":24,"slug":25,"category":26,"related_article_id":27,"status":28,"google_indexed_at":29,"x_posted_at":10,"tweet_text":10,"title_rewritten_at":10,"title_original":10,"key_takeaways":30,"topic_cluster_id":10,"embedding":10,"is_canonical_seed":20},"8e325341-ee9d-4b99-bffc-9fd818221970","How AI, Big Data, and Blockchain Fit Together in Finance","\u003Cp data-speakable=\"summary\">Springer’s ICFT 2025 paper maps how AI, big data, and blockchain combine in finance.\u003C\u002Fp>\u003Cp>In a chapter published on \u003Ca href=\"https:\u002F\u002Flink.springer.com\u002Fchapter\u002F10.1007\u002F978-981-92-0126-6_30\" target=\"_blank\" rel=\"noopener\">SpringerLink\u003C\u002Fa> on 29 April 2026, Mohamed Amine Issami argues that finance is moving from isolated tools to a connected stack built around \u003Ca href=\"https:\u002F\u002Flink.springer.com\u002Fsearch?query=artificial+intelligence\" target=\"_blank\" rel=\"noopener\">artificial intelligence\u003C\u002Fa>, \u003Ca href=\"https:\u002F\u002Flink.springer.com\u002Fsearch?query=big+data\" target=\"_blank\" rel=\"noopener\">big data\u003C\u002Fa>, and \u003Ca href=\"https:\u002F\u002Flink.springer.com\u002Fsearch?query=blockchain\" target=\"_blank\" rel=\"noopener\">blockchain\u003C\u002Fa>. The paper is short on hype and heavy on system design: it focuses on what happens when these technologies reinforce each other, and where the new failure modes show up.\u003C\u002Fp>\u003Cp>The chapter sits in \u003Ca href=\"https:\u002F\u002Flink.springer.com\u002Fbook\u002F10.1007\u002F978-981-92-0126-6\" target=\"_blank\" rel=\"noopener\">Financial Technology\u003C\u002Fa>, part of the \u003Ca href=\"https:\u002F\u002Flink.springer.com\u002Fseries\u002F7899\" target=\"_blank\" rel=\"noopener\">Communications in Computer and Information Science\u003C\u002Fa> series, and it was published as part of \u003Ca href=\"https:\u002F\u002Flink.springer.com\u002Fconference\u002Ficft\" target=\"_blank\" rel=\"noopener\">ICFT 2025\u003C\u002Fa>. That matters because this is not a product announcement or a vendor pitch. It is an academic attempt to define a convergence model for fintech technologies, then stress-test it against governance, privacy, oracles, decentralized AI, zero-knowledge proofs, and quantum-era threats.\u003C\u002Fp>\u003Ctable>\u003Cthead>\u003Ctr>\u003Cth>Fact\u003C\u002Fth>\u003Cth>Value\u003C\u002Fth>\u003C\u002Ftr>\u003C\u002Fthead>\u003Ctbody>\u003Ctr>\u003Ctd>Chapter title\u003C\u002Ftd>\u003Ctd>Artificial Intelligence, Big Data, and Blockchain: The Synergistic Convergence Reshaping Financial Services\u003C\u002Ftd>\u003C\u002Ftr>\u003Ctr>\u003Ctd>Publication date\u003C\u002Ftd>\u003Ctd>29 April 2026\u003C\u002Ftd>\u003C\u002Ftr>\u003Ctr>\u003Ctd>Pages\u003C\u002Ftd>\u003Ctd>370–380\u003C\u002Ftd>\u003C\u002Ftr>\u003Ctr>\u003Ctd>Print ISBN\u003C\u002Ftd>\u003Ctd>978-981-92-0125-9\u003C\u002Ftd>\u003C\u002Ftr>\u003Ctr>\u003Ctd>Online ISBN\u003C\u002Ftd>\u003Ctd>978-981-92-0126-6\u003C\u002Ftd>\u003C\u002Ftr>\u003Ctr>\u003Ctd>DOI\u003C\u002Ftd>\u003Ctd>10.1007\u002F978-981-92-0126-6_30\u003C\u002Ftd>\u003C\u002Ftr>\u003C\u002Ftbody>\u003C\u002Ftable>\u003Ch2>What the paper is really saying\u003C\u002Fh2>\u003Cp>The core claim is simple: these three technologies become more useful together than apart. Big data gives AI better inputs, AI turns that data into predictions and automation, and blockchain adds a tamper-resistant record layer that can support trust in financial workflows. In the paper’s framing, the result is a convergence model for fintech technologies, or CMFT, where the value comes from interaction rather than from any single tool.\u003C\u002Fp>\n\u003Cfigure class=\"my-6\">\u003Cimg src=\"https:\u002F\u002Fxxdpdyhzhpamafnrdkyq.supabase.co\u002Fstorage\u002Fv1\u002Fobject\u002Fpublic\u002Fcovers\u002Finline-1777922464163-k7t8.png\" alt=\"How AI, Big Data, and Blockchain Fit Together in Finance\" class=\"rounded-xl w-full\" loading=\"lazy\" \u002F>\u003C\u002Ffigure>\n\u003Cp>That framing is useful because financial services rarely fail in one layer only. Fraud detection depends on data quality, model quality, and the integrity of the audit trail. Lending decisions depend on analytics, identity, and compliance. Cross-border settlement depends on coordination, verification, and speed. The paper treats those as connected problems, which is the right way to think about modern finance.\u003C\u002Fp>\u003Cp>The author also places governance at the center of the story. That is a smart move. A lot of AI-and-blockchain writing treats regulation as a late-stage patch. Here, governance is part of the architecture, alongside privacy and oracle design. That makes the chapter more practical than the usual “AI plus blockchain” slide deck.\u003C\u002Fp>\u003Cul>\u003Cli>AI gets better when the data pipeline is richer and cleaner.\u003C\u002Fli>\u003Cli>Blockchain helps create an auditable record of who changed what and when.\u003C\u002Fli>\u003Cli>Big data expands model capacity, but also expands the blast radius of bad inputs.\u003C\u002Fli>\u003Cli>Financial services need these systems to work together under compliance pressure, not in isolation.\u003C\u002Fli>\u003C\u002Ful>\u003Ch2>The risk section is the most interesting part\u003C\u002Fh2>\u003Cp>Issami does not treat convergence as a free lunch. The chapter flags AI-model poisoning, blockchain scalability limits, and systemic vulnerabilities as the main hazards in an integrated ecosystem. That combination matters because each technology can make the others harder to secure. A poisoned model can produce bad decisions at scale. A congested blockchain can slow verification. A weak integration layer can turn a small flaw into an operational problem.\u003C\u002Fp>\u003Cp>That warning lands well in 2026 because financial institutions are already pushing into AI-assisted risk scoring, tokenized assets, and on-chain automation. The more these systems interact, the more a bug in one layer can ripple into another. The paper’s point is that convergence creates efficiency, but it also creates shared failure paths.\u003C\u002Fp>\u003Cblockquote>“The future of finance is not about silos, but about integrated systems that can be trusted, transparent, and secure.” — Mohamed Amine Issami\u003C\u002Fblockquote>\u003Cp>That quote captures the paper’s tone better than the abstract does. It is optimistic, but it does not pretend that trust appears automatically. Trust has to be designed into the stack, especially when \u003Ca href=\"\u002Ftag\u002Fmachine-learning\">machine learning\u003C\u002Fa> decisions and blockchain records are both part of the workflow.\u003C\u002Fp>\u003Ch2>How this compares with the current fintech stack\u003C\u002Fh2>\u003Cp>Traditional fintech tools often solve one problem at a time. AI handles classification or forecasting. Blockchain handles recordkeeping or settlement. Big data handles storage and processing. The chapter argues that the next step is a joined system where each layer improves the others. That sounds abstract until you compare the operational tradeoffs.\u003C\u002Fp>\n\u003Cfigure class=\"my-6\">\u003Cimg src=\"https:\u002F\u002Fxxdpdyhzhpamafnrdkyq.supabase.co\u002Fstorage\u002Fv1\u002Fobject\u002Fpublic\u002Fcovers\u002Finline-1777922464998-d742.png\" alt=\"How AI, Big Data, and Blockchain Fit Together in Finance\" class=\"rounded-xl w-full\" loading=\"lazy\" \u002F>\u003C\u002Ffigure>\n\u003Cp>Here is the practical difference:\u003C\u002Fp>\u003Cul>\u003Cli>Standalone AI can be fast, but it often lacks a trustworthy provenance trail.\u003C\u002Fli>\u003Cli>Standalone blockchain can preserve records, but it can be slow and expensive at scale.\u003C\u002Fli>\u003Cli>Standalone big data platforms can ingest huge volumes, but they do not guarantee better decisions.\u003C\u002Fli>\u003Cli>A combined architecture can support personalized financial services, if governance and security are built in from the start.\u003C\u002Fli>\u003C\u002Ful>\u003Cp>The chapter also points to decentralized AI and zero-knowledge proofs as part of the next wave of experimentation. Those are not casual mentions. Decentralized AI could spread training or \u003Ca href=\"\u002Ftag\u002Finference\">inference\u003C\u002Fa> across participants, while zero-knowledge proofs could let institutions prove something about data without exposing the data itself. In finance, that combination is especially attractive because privacy and auditability often pull in opposite directions.\u003C\u002Fp>\u003Cp>Quantum computing appears in the abstract for a reason too. If financial systems become more dependent on cryptography-heavy rails, then quantum resilience stops being a distant research topic and becomes a planning issue. The paper does not claim to solve that problem, but it correctly includes it in the same conversation as model poisoning and scalability.\u003C\u002Fp>\u003Ch2>Why the “Regulatory Sandbox 2.0” idea matters\u003C\u002Fh2>\u003Cp>The chapter ends by proposing a “Regulatory Sandbox 2.0” structure with multi-stakeholder governance. That is the most policy-aware part of the argument. A sandbox is useful only if it lets regulators, firms, and technical teams test real systems under controlled conditions. The “2.0” version implied here is broader: it is not just a place to test products, but a way to test how AI, data infrastructure, and blockchain rules interact.\u003C\u002Fp>\u003Cp>That approach fits the problems the paper identifies. AI-model poisoning is a technical issue, but it is also a governance issue. Blockchain scalability is an engineering issue, but it also affects market structure and user experience. Systemic vulnerability is the kind of risk that only shows up when different teams stop talking to each other. A multi-stakeholder sandbox is one way to force those conversations early.\u003C\u002Fp>\u003Cp>For banks, regulators, and fintech builders, the chapter’s message is fairly direct: stop evaluating AI, big data, and blockchain as separate procurement choices. Start testing them as one operating system for financial services. That means measuring model integrity, data provenance, latency, compliance exposure, and privacy controls together, not in separate review cycles.\u003C\u002Fp>\u003Cp>If the industry takes that advice seriously, the next debate will not be whether finance should use AI or blockchain. It will be which combinations can survive stress testing, and \u003Ca href=\"\u002Fnews\u002Fai-models-2026-which-one-to-use-en\">which one\u003C\u002Fa>s need guardrails before they hit production.\u003C\u002Fp>\u003Ch2>What to watch next\u003C\u002Fh2>\u003Cp>The most useful takeaway from this Springer chapter is that the convergence story is maturing. The easy part is saying that AI, big data, and blockchain belong together. The hard part is deciding how to secure them, regulate them, and keep them usable under real financial pressure. That is where the next wave of fintech work will be judged.\u003C\u002Fp>\u003Cp>My bet: the teams that win will be the ones that treat provenance, privacy, and model integrity as product features, not afterthoughts. The paper does not spell out a vendor roadmap, but it does point to a clear research agenda. The next question is whether the industry builds those controls before a high-profile failure forces the issue.\u003C\u002Fp>","Springer’s ICFT 2025 paper maps how AI, big data, and blockchain combine in finance, while flagging poisoning, scalability, and governance risks.","link.springer.com","https:\u002F\u002Flink.springer.com\u002Fchapter\u002F10.1007\u002F978-981-92-0126-6_30",null,"https:\u002F\u002Fxxdpdyhzhpamafnrdkyq.supabase.co\u002Fstorage\u002Fv1\u002Fobject\u002Fpublic\u002Fcovers\u002Finline-1777922464163-k7t8.png",[13,14,15,16,17],"artificial intelligence","big data","blockchain","fintech","regulatory sandbox","en",0,false,"2026-05-04T19:20:38.265059+00:00","2026-05-04T19:20:38.247+00:00","done","5abf31ef-f4dc-44a4-80e9-e860a9320b41","ai-big-data-blockchain-finance-convergence-en","research","53a6508d-4883-4475-a754-31ac7b262c76","published","2026-05-05T09:00:18.417+00:00",[31,32,33],"AI, big data, and blockchain create more value together than separately in financial services.","The paper highlights real risks: model poisoning, scalability limits, and systemic vulnerabilities.","A multi-stakeholder Regulatory Sandbox 2.0 is proposed to test governance before broad deployment.",[35,37,39,41,42],{"name":14,"slug":36},"big-data",{"name":17,"slug":38},"regulatory-sandbox",{"name":13,"slug":40},"artificial-intelligence",{"name":15,"slug":15},{"name":16,"slug":16},{"id":27,"slug":44,"title":45,"language":46},"ai-big-data-blockchain-finance-convergence-zh","AI、大數據、區塊鏈怎麼接上金融","zh",[48,54,60,66,72,78],{"id":49,"slug":50,"title":51,"cover_image":52,"image_url":52,"created_at":53,"category":26},"94994abd-e24d-4fd1-b941-942d03d19acf","turboquant-seo-shift-small-sites-en","TurboQuant and the SEO Shift for Small 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