Goldman Sachs plans to deploy Anthropic\u2019s Claude model in trade accounting and client onboarding, and, according to an article in American Banker, presents this as part of a broader push among large banks to use generative artificial intelligence to improve efficiency. The focus is on operational processes that sit in the back office and have traditionally relied on large teams performing tasks like document review, reconciliation, and compliance checks.<\/p>\n
Several banks already use generative AI in knowledge work. JPMorganChase provides employees with access to a large language model suite for information retrieval and data analysis, while the Bank of America\u2019s Erica assistant answers internal technology and human resources queries. Citi and Goldman both use AI to help developers with coding. The article suggests a more recent development is the application of generative AI to operational tasks like trade accounting and know-your-customer (KYC).<\/p>\n
Automating the edge-cases<\/p>\n
Automatable processes in the sector are often rules-based, involving collecting data, validating it against internal and external databases, and assembling required documentation. In theory, conventional software has been used to automate such work. However, Marco Argenti, Goldman\u2019s chief information officer, argues that if a rules-based system resolves most cases, a small percentage of transactions fall outside defined parameters that can translate into thousands of individual items at the type of scale in question. He cites the example of identity verification in KYC compliance, where minor discrepancies or documents approaching expiry can create edge cases requiring judgement.
\nArgenti says that neural networks can address these micro-decisions as they\u2019re capable of applying contextual reasoning where fixed rules might be missing or don\u2019t necessarily give a clear answer. In this scenario, generative AI augments existing rules systems rather than supplanting them. Operational improvements, therefore lie in the reduction in the number of cases that require manual intervention and thus shortening time needed to resolve the exceptions.<\/p>\n
The coding experience<\/p>\n
Goldman\u2019s prior experience with Claude models used internally for software development informed its decision to extend AI to other areas of operations. Developers use a version of Claude with Cognition\u2019s Devin agent to aid them with programming. In this context, human developers set specifications and regulatory parameters, the agent produces code, and humans review outputs. The agent is also used to run code tests and validations. He describes this as a change to devs\u2019 workflows, with agents operating according to defined instructions. The benefit is increased developer productivity and the faster completion of projects.s
\nFor trade accounting and client onboarding, Goldman and Anthropic AI project owners observed existing workflows with domain experts to identify work bottlenecks. The implemented agents review documents, extract entities, determine whether additional documentation is required, assess ownership structures, and can trigger further compliance checks. Tasks automated in this way tend to be document-heavy and require individual judgement. By automating extraction and preliminary assessment, the agents reduce the time analysts spend on comparison work.
\nIndranil Bandyopadhyay, principal analyst at Forrester, says that reconciliation in trade accounting requires comparing fragmented data in internal ledgers, counterparty confirmations, and the perusal of bank statements, and that a typical workflow depends on accurate extraction and matching of figures and text to existing documents. Claude\u2019s ability to process large context windows and follow instructions, he says, makes it suited to just such workflows. The labour involved in client onboarding, such as parsing passports and corporate registration documents, and the cross-referencing of all sources means AI\u2019s ability to extract structured data and flag inconsistencies makes the technology a good fit, reducing overall workloads.
\nBandyopadhyay stresses that accounting and compliance platforms remain the canonical systems of record. Claude operates in the workflow layer, handling extraction and comparison so human analysts can handle the code\u2019s exceptions.. In his assessment, the operational value in a regulated environments like banking lies in such a division of labour.
\nJonathan Pelosi, head of financial services at Anthropic says Claude is trained to surface uncertainty and to provide source attribution, creating an audit trail \u2013 reducing the effect of hallucinations. Bandyopadhyay also notes the importance of human oversight and validation, saying institutions should design systems so that errors are detected early.
\nGoldman\u2019s Marco Argenti rejects the view that AI systems are inherently easier to deceive than people, arguing that social engineering exploits human vulnerabilities and that AI can detect subtle anomalies at scale, and reiterates the need to combine human judgement with automated scrutiny in teams. His claim implies a increase in operational capacity without proportional increases in staff, even with the issues known to affect AI rollouts.
\nAI in banking operations
\nIn the banking sector, generative AI is a tool that improves operational performance by accelerating document processing, reducing exception handling time, and increasing throughput in high volume workflows. But the need to retain human oversight to counteract AI\u2019s errors means the retention of and reliance on existing systems of records remains.
\n(Image source: \u201cDreams\u2026\u201d by noahwesley is licensed under CC BY-NC-SA 2.0)
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The post Goldman Sachs deploys Anthropic systems with success appeared first on AI News.<\/p>\n","protected":false},"excerpt":{"rendered":"
Goldman Sachs plans to deploy Anthropic\u2019s Claude model in trade accounting and client onboarding, and, according to an article in American Banker, presents this as part of a broader push among large banks to use generative artificial intelligence to improve efficiency. The focus is on operational processes that sit in the back office and have [\u2026]<\/p>\n
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