Moody’s to utilise AI to accelerate analytical report writing

Moody's Corp. is stepping into the world of artificial intelligence, leveraging generative AI technology to expedite the process of analysing extensive data and generating analytical reports.

According to a report by Bloomberg, the ratings agency is introducing large language models from Google Cloud to its employees, with the aim of streamlining the analysis of public documents and the company's internal database, ultimately making the report-writing process more efficient. This initiative is set to empower a broader range of staff members to work on projects that previously necessitated expertise in fields like coding, finance, and accountancy.

Nick Reed, Moody's Chief Product Officer, spoke about the time-saving capabilities of this technology. Bloomberg quoted him as saying, "What could potentially have been a day's long process is now literally five minutes."

Add WION as a Preferred Source

He emphasised that there are numerous potential applications for the AI-driven technology, and the company's approach is to introduce it to employees, allowing them to identify areas where workflow improvements can be made.

Recently, PricewaterhouseCoopers LLP announced a collaboration with OpenAI, the owner of ChatGPT, to provide AI-generated advice to clients. Similarly, KPMG revealed a substantial $2 billion investment in Microsoft's generative AI efforts and cloud services.

To ensure transparency and reliability in the reporting process, the AI software will maintain a digital record of the sources from which information is derived. According to Philip Moyer, Vice President of Artificial Intelligence and Business Solutions at Google Cloud, this feature will prevent the technology from "hallucinating," a term used to describe instances where large language models generate answers that appear convincing but are not accurate.

In addition to enhancing their own operations, Moody's will extend the availability of these large language models to other financial institutions. These firms can employ the technology to streamline routine tasks, allowing for faster, data-driven decision-making. For instance, a bank employee could promptly assess whether to onboard a small business client by instructing the large language model to identify key risks in the client's financial disclosures, summarise earnings calls, or identify peers with similar environmental footprints. This process, which previously consumed hours, could now yield results within minutes.

Philip Moyer highlighted the goal of democratising access to critical information, allowing individuals who lack in-depth expertise in specific fields to effectively engage with and utilise this technology for more efficient decision-making.

(With inputs from Boomberg)

WATCH WION LIVE HERE