{"id":57053,"date":"2026-02-18T16:28:39","date_gmt":"2026-02-18T06:28:39","guid":{"rendered":"https:\/\/www.cloudproinc.com.au\/?p=57053"},"modified":"2026-02-18T16:28:43","modified_gmt":"2026-02-18T06:28:43","slug":"goldman-sachs-is-automating-accounting-with-ai-what-it-means-for-you","status":"publish","type":"post","link":"https:\/\/cloudproinc.com.au\/index.php\/2026\/02\/18\/goldman-sachs-is-automating-accounting-with-ai-what-it-means-for-you\/","title":{"rendered":"Goldman Sachs Is Automating Accounting With AI What It Means for You"},"content":{"rendered":"\n

In this blog post Goldman Sachs Is Automating Accounting With AI What It Means for You<\/strong> we will unpack what\u2019s really happening when a major bank starts automating accounting with AI, and what practical, low-risk moves Australian businesses can take next.<\/p>\n\n\n\n\n\n\n\n

If you\u2019re a CIO, IT manager, CTO, or tech lead in a 50\u2013500 person business, the headline isn\u2019t \u201cbanks are doing cool AI stuff.\u201d It\u2019s this: the cost and speed expectations for back-office work are changing<\/strong>, and your finance and operations teams will start asking why month-end still hurts.<\/p>\n\n\n\n

Let\u2019s keep this grounded. Most organisations don\u2019t need a \u201crobot accountant.\u201d They need fewer manual steps, fewer spreadsheet hand-offs, tighter controls, and better audit trails. AI can help\u2014but only if you wrap it in the right guardrails.<\/p>\n\n\n\n

What Goldman Sachs is doing in plain English<\/h2>\n\n\n\n

Goldman Sachs has been working with AI models (notably Anthropic\u2019s Claude) to build AI agents<\/strong> that can complete multi-step internal tasks. Think of an agent as software that can: read information, follow rules, take actions in other systems, and report what it did.<\/p>\n\n\n\n

In the reporting, these agents have been aimed at workflows such as trade and transaction accounting<\/strong> and other operational processes. That matters because these are high-volume, rules-heavy tasks where small errors create big downstream costs.<\/p>\n\n\n\n

The main technology behind it (high-level first)<\/h2>\n\n\n\n

The core technology is a large language model (LLM)<\/strong>. An LLM is the \u201cbrain\u201d that can read and write human language, extract meaning, and follow instructions. You\u2019ve seen this style of AI in tools like ChatGPT and Claude.<\/p>\n\n\n\n

On its own, an LLM is just a conversation engine. The big leap is when you combine an LLM with:<\/p>\n\n\n\n

    \n
  • Your business context<\/strong> (policies, procedures, chart of accounts, process maps).<\/li>\n\n\n\n
  • Tools and permissions<\/strong> (APIs and service accounts to safely read\/write data in finance systems).<\/li>\n\n\n\n
  • Workflow orchestration<\/strong> (logic that says \u201cfirst do A, then B, then ask for approval, then post\u201d).<\/li>\n\n\n\n
  • Controls<\/strong> (logging, approvals, segregation of duties, and model safety checks).<\/li>\n<\/ul>\n\n\n\n

    AI assistant vs AI agent (why this matters)<\/h3>\n\n\n\n

    AI assistant<\/strong> is typically \u201chelp me write\/summarise\/analyse.\u201d It\u2019s useful, but it often leaves humans to do the operational steps.<\/p>\n\n\n\n

    AI agent<\/strong> is \u201cdo the work\u201d within defined boundaries\u2014like reconciling exceptions, drafting journals for approval, or preparing a variance explanation using source data.<\/p>\n\n\n\n

    That shift from \u201chelp me\u201d to \u201chandle it\u201d is what makes executives pay attention. It moves AI from productivity to process cost reduction<\/strong>.<\/p>\n\n\n\n

    What this means for mid-sized Australian businesses<\/h2>\n\n\n\n

    Goldman\u2019s scale is obviously different. But the underlying pain points\u2014manual handling, fragmented systems, inconsistent processes\u2014are very similar in mid-market organisations.<\/p>\n\n\n\n

    1) Month-end close will become a competitive advantage<\/h3>\n\n\n\n

    If your month-end close takes 10\u201315 business days, you\u2019re making decisions on old information. That creates real cost: delayed hiring decisions, late pricing adjustments, and slow responses to budget blowouts.<\/p>\n\n\n\n

    AI can reduce close time by automating the \u201cglue work\u201d:<\/p>\n\n\n\n

      \n
    • Chasing missing invoices and approvals<\/li>\n\n\n\n
    • Matching transactions to the right categories based on rules and history<\/li>\n\n\n\n
    • Explaining variances using notes pulled from source systems<\/li>\n\n\n\n
    • Preparing reconciliation packs and checklists<\/li>\n<\/ul>\n\n\n\n

      Business outcome:<\/strong> faster decisions and less overtime during close.<\/p>\n\n\n\n

      2) The real ROI is less rework, not fewer people<\/h3>\n\n\n\n

      A lot of AI talk gets stuck on headcount reduction. In practice, the first win is usually reducing rework<\/strong>: fewer mistakes, fewer handoffs, fewer \u201cwho changed this spreadsheet?\u201d moments.<\/p>\n\n\n\n

      For many organisations, finance doesn\u2019t want to cut staff. They want their best people spending time on analysis, not copy\/paste.<\/p>\n\n\n\n

      Business outcome:<\/strong> the same team can support more growth without burning out.<\/p>\n\n\n\n

      3) Audit and compliance expectations are rising (even if you\u2019re not regulated like a bank)<\/h3>\n\n\n\n

      When AI starts touching financial processes, auditors will ask basic questions:<\/p>\n\n\n\n

        \n
      • Who approved it?<\/li>\n\n\n\n
      • What data did it use?<\/li>\n\n\n\n
      • What changed, and when?<\/li>\n\n\n\n
      • Can we reproduce the result?<\/li>\n<\/ul>\n\n\n\n

        This is where a lot of \u201cquick AI wins\u201d fail. If someone is using an AI tool ad-hoc, outside governed systems, you can end up with invisible risk.<\/p>\n\n\n\n

        Business outcome:<\/strong> safer automation that stands up to audit scrutiny.<\/p>\n\n\n\n

        4) Security becomes more important, not less<\/h3>\n\n\n\n

        Accounting automation touches sensitive information: payroll, vendor bank details, invoices, customer contracts, and sometimes M&A or pricing.<\/p>\n\n\n\n

        In Australia, you also have to consider privacy obligations and breach impact. Even if you\u2019re not a bank, a data leak can still become an incident that hits customers and your reputation.<\/p>\n\n\n\n

        That\u2019s why we treat AI as part of your security program, not a side experiment. At CloudPro Inc, we typically anchor this in:<\/p>\n\n\n\n

          \n
        • Microsoft 365<\/strong> (your core productivity suite\u2014email, documents, collaboration)<\/li>\n\n\n\n
        • Microsoft Purview<\/strong> (which helps classify and protect sensitive data)<\/li>\n\n\n\n
        • Microsoft Defender<\/strong> (which helps detect and respond to security threats)<\/li>\n\n\n\n
        • Microsoft Intune<\/strong> (which manages and secures all your company devices)<\/li>\n\n\n\n
        • Essential 8<\/strong> (the Australian government\u2019s cybersecurity framework many organisations are expected to align to)<\/li>\n<\/ul>\n\n\n\n

          Business outcome:<\/strong> lower risk of AI becoming a new \u201cleak path\u201d for sensitive finance data.<\/p>\n\n\n\n

          A realistic scenario (what this looks like outside a global bank)<\/h2>\n\n\n\n

          Imagine a 200-person professional services firm in Melbourne. Month-end takes 12 business days. The finance manager spends days chasing timesheets, matching expenses to cost centres, and manually explaining project margin variances to leadership.<\/p>\n\n\n\n

          They don\u2019t need \u201cAI to do accounting.\u201d They need a better workflow:<\/p>\n\n\n\n

            \n
          • An AI assistant that drafts variance explanations using data from project systems and the general ledger<\/li>\n\n\n\n
          • An agent that flags exceptions (missing approvals, unusual transactions) and routes them to the right person<\/li>\n\n\n\n
          • Automated creation of a close checklist and evidence pack for audit<\/li>\n<\/ul>\n\n\n\n

            The result is not magic. It\u2019s fewer late nights, fewer errors, and leadership getting cleaner numbers earlier.<\/p>\n\n\n\n

            How to implement this safely (practical steps)<\/h2>\n\n\n\n

            Step 1: Pick one process with measurable pain<\/h3>\n\n\n\n

            Good candidates are repetitive, rules-based, and high-volume:<\/p>\n\n\n\n

              \n
            • Invoice coding suggestions<\/li>\n\n\n\n
            • Reconciliation exception handling<\/li>\n\n\n\n
            • Vendor onboarding checks<\/li>\n\n\n\n
            • Close commentary drafts (not final posting)<\/li>\n<\/ul>\n\n\n\n

              Step 2: Separate \u201crecommend\u201d from \u201cpost\u201d<\/h3>\n\n\n\n

              In early phases, AI should recommend<\/strong> actions, not execute irreversible ones.<\/p>\n\n\n\n

              Example: AI drafts a journal with supporting evidence and routes it for approval. A human approves, and only then does it get posted.<\/p>\n\n\n\n

              Step 3: Put guardrails around data and identity<\/h3>\n\n\n\n

              This is where your Microsoft stack matters. Use the same identity controls you rely on for everything else:<\/p>\n\n\n\n

                \n
              • Single sign-on and multi-factor authentication<\/li>\n\n\n\n
              • Least-privilege access to finance data<\/li>\n\n\n\n
              • Conditional access policies (e.g., block risky logins)<\/li>\n\n\n\n
              • Device compliance via Intune<\/li>\n<\/ul>\n\n\n\n

                Step 4: Log everything and make it auditable<\/h3>\n\n\n\n

                If an AI agent touched a process, you should be able to answer: what happened, who approved it, and what evidence was used.<\/p>\n\n\n\n

                Step 5: Align to Essential 8 (even if you\u2019re not required yet)<\/h3>\n\n\n\n

                Essential 8 is a practical baseline for reducing cyber risk. AI doesn\u2019t replace it. In many cases, AI increases the need to get the basics right\u2014patching, access control, backups, application control, and macros governance.<\/p>\n\n\n\n

                For developers and IT leaders (a simple reference architecture)<\/h2>\n\n\n\n

                Here\u2019s a non-scary way to think about the \u201cagent\u201d pattern in a Microsoft environment:<\/p>\n\n\n\n

                  \n
                • Interface<\/strong>: a chat UI in Teams or a small internal web app<\/li>\n\n\n\n
                • Brain<\/strong>: an LLM (OpenAI or Anthropic Claude) with a system prompt and policies<\/li>\n\n\n\n
                • Knowledge<\/strong>: secure retrieval from finance policies and process docs (so it uses your rules, not internet guesses)<\/li>\n\n\n\n
                • Actions<\/strong>: connectors\/APIs to finance systems, approvals, ticketing, and document storage<\/li>\n\n\n\n
                • Controls<\/strong>: approvals, logging, rate limits, and monitoring<\/li>\n<\/ul>\n\n\n\n

                  Example pseudo-flow:<\/p>\n\n\n\n

                  \/\/ Pseudocode: \u201crecommend, don\u2019t post\u201d close assistant\nonCloseDay():\n data = fetchGLExtract(read_only=true)\n exceptions = findAnomalies(data)\n\n for item in exceptions:\n evidence = gatherEvidence(item) \/\/ invoices, approvals, prior month patterns\n draft = llm.generate(\n task=\"Draft variance explanation and suggested journal\",\n inputs={ item, evidence, companyPolicy }\n )\n\n createApprovalRequest(\n approver=financeController,\n content=draft,\n attachments=evidence,\n action=\"RecommendOnly\"\n )\n\n logAllSteps()\n<\/code><\/pre>\n\n\n\n
                  This is intentionally boring. Boring is good in finance.<\/p>\n\n\n\n
                  Where CloudPro Inc fits (and why most AI projects stall)<\/h2>\n\n\n\n
                  Most mid-market AI initiatives don\u2019t fail because the model is \u201cnot smart enough.\u201d They fail because:<\/p>\n\n\n\n
                    \n
                  • Data is messy and scattered<\/li>\n\n\n\n
                  • Access control is inconsistent<\/li>\n\n\n\n
                  • There\u2019s no clear approval workflow<\/li>\n\n\n\n
                  • No one owns the process end-to-end<\/li>\n<\/ul>\n\n\n\n
                    CloudProInc is a Melbourne-based Microsoft Partner and Wiz Security Integrator with 20+ years of enterprise IT experience. We\u2019re hands-on, and we build AI into the systems you already rely on\u2014Azure, Microsoft 365, Intune, Windows 365, and your security controls\u2014so it\u2019s usable, supportable, and safe.<\/p>\n\n\n\n
                    Wrap-up<\/h2>\n\n\n\n
                    Goldman Sachs automating accounting with AI is a signal: AI is moving from \u201chelpful chat\u201d to real operational automation<\/strong>. For your business, the opportunity isn\u2019t copying a global bank. It\u2019s choosing one painful finance workflow, tightening controls, and using AI to reduce rework and speed up decision-making.<\/p>\n\n\n\n
                    If you\u2019re not sure whether your current month-end process, security posture, or Microsoft setup is costing you more than it should, we\u2019re happy to take a look and map out a practical next step\u2014no pressure, no strings attached.<\/p>\n\n\n\n
                    <\/p>\n","protected":false},"excerpt":{"rendered":"
                    Goldman Sachs is using AI agents to speed up accounting and back-office work. 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