Tuesday,28 May 2024

Trade Ledger utilises OpenAI to address data processing, & data fragmentation in cash-to-cash cycle

5 min read
视频:

Interviewed By Foo Boon Ping

Martin McCann, founder of Trade Ledger, discusses how OpenAI's large language model can help SMEs gain insights from their data and how Trade Ledger's Working Capital Copilot, powered by advanced language models, is addressing cash management challenges for CFOs in SMEs while also offering cost reductions of up to 60% for banks.

Martin McCann, founder of Trade Ledger™ a lending technology company established in 2016 that supports the digitisation of lending products for businesses, discusses the potential of OpenAI's large language model to address the insights SMEs can gain from their data. These firms often lack the resources or in-house expertise to access and comprehend information needed to optimise the cash-to-cash cycle.

At the same time, data fragmentation is a perennial issue in workingcapital management, especially for businesses with multiple trading partners and banking relationships.

McCann outlined how Trade Ledger's Working Capital Copilot, built on Azure OpenAI Service, tackles these challenges. It uses advanced large language models to interpret conversational language queries about cash management, generates algorithmic queries and returns meaningful, actionable information for CFOs in SMEs.

He claimed that Trade Ledger already standardises and normalises corporate data, consolidating it into a single repository to drive automation across all front- and middle-office activities for both bank teams and customers.

By elevating the same data model structure and making it available to corporate customers via a bank interface enhanced by AI it is bringing corporate cash management capabilities to smaller businesses.
For banks, McCann emphasised that the technology offers the opportunity to integrate Generative AI in a compliant and secure manner, resulting in up to 60% reduction in the overall costs of acquisition and processing.


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