Cohere’s LLM Solutions Advance Knowledge Work in Financial Services

The financial services industry is poised to reap significant productivity gains from the adoption of large language models (LLMs). According to Accenture, banks could see a 30% boost in productivity, topping the more than 20 industries evaluated. This is because the strengths of LLMs align perfectly with both a top differentiator among competing banks - customer service - and a function that sits at the heart of the industry: knowledge work.

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An intensely competitive environment has historically motivated financial services to adopt technologies earlier than many other industries, tech sector aside. With LLMs, all indications are that FS will again stand at the forefront of innovation.

McKinsey’s latest State of AI survey shows 41% of respondents from the industry investing anywhere from 6% to 20% of digital budgets in GenAI, behind only the tech sector (and on par with energy). A steady cadence of press releases from top FS firms touting their GenAI pilots, along with what can be seen in Cohere’s work, corroborates the high interest.

Making Knowledge Work More Efficient

Knowledge work is at the core of financial services. Investment bankers, financial planners, credit analysts, wealth advisors, equity analysts, risk managers - the list of industry roles that deal with vast amounts of information daily could fill the rest of this article. LLM applications can support them, both in finding information and analyzing it.

Faster Knowledge Search and Synthesis

These workers spend significant chunks of their days sifting through and extracting insights from troves of financial data, product specs, procedural documentation, and spreadsheets. Numerous studies have quantified the substantial amount of time workers spend hunting for information, with estimates ranging from 2 hours to 3.6 hours per day. The search, retrieval, and summarization capabilities of LLMs offer a significant source of time savings.

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Homing in on one role, wealth advisors must monitor capital markets closely to manage risks and help clients make wise investment choices. While traditional machine learning models have enabled institutions to amass vast amounts of data, advisors still must sift through a myriad of documents - from regulatory filings to economic reports - to find the right data.

AI knowledge assistants are changing this by distilling the latest financial statements and market analysis into concise summaries within seconds, and then answering follow-up questions to provide sources, clarifications, comparisons, and contextualization.

Improving Data Analysis

LLM solutions can do more than just find knowledge - they can also help analyze it. Enterprises can automate financial, operational, and tabular data analysis by leveraging LLMs to examine various factors and generate reports. Equipping an LLM with a Python console, for example, enables organizations to start analyzing spreadsheets and financial data automatically.

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Financial AI assistants are also capable of executing sophisticated prompts that can scan across documents, extract common information, and organize it in a standardized format that makes it easier to spot trends. In one case, the managing partners of an investment firm use Command R+ as part of a solution that helps them assess performance across their portfolio.

Better Customer Service Through Quick, Accurate Responses

Customer service quality is a top factor influencing where people choose to do their banking and other financial activities. In retail banking, for example, a 2024 J.D. Power survey found that poor customer service is almost as important as unexpected fees when it comes to why customers might switch banks. And banks with high customer satisfaction often see an increase in customer deposits.

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While many factors can impact customer service, one significant challenge has been the speed and efficiency with which banking staff can respond to customer questions or issues. Bank workers often need to navigate many and varied documents, from policy manuals to product specs, to find relevant information, which can be time-consuming and cumbersome.

Building AI assistants with advanced retrieval capabilities and semantic search offers a massive performance upgrade over typical keyword searches. With turbocharged search, any role involved in customer service - from support agents to wealth advisors - can quickly surface more relevant answers to customer queries from voluminous knowledge stores and across databases.

Managing the Risks

Understandably, financial services firms want to ensure that they can manage risks associated with the use of LLMs, just as they have done with the cloud and other innovative technologies. It’s not only sound practice to protect and serve clients, but it’s also necessary for compliance with strict regulations that the industry faces around data usage, model-driven decision-making, and other rules pertinent to the use of LLMs.

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There are three primary concerns Cohere hears most often in conversations with customers: protecting sensitive data, avoiding intellectual property rights violations, and minimizing inaccuracies. Cohere takes these and other risks seriously given that the Company focuses solely on enterprise AI solutions.