Welcome to a new blog series on the rise of the digital co-worker (DCW), which represents the new age of AI in financial services. In recent years, the emergence of large language models (LLMs) has fascinated the business world, with their ability to speak human language.
That said, in 2026, the novelty of AI-enabled chats is slowly wearing off and being replaced by the “work” imperative. For long, financial due diligence services have been in an ongoing “battle” against rising volumes. Modern credit analysts feel their job is more about excavation rather than analysis. For instance, human analysts have to dig through mountains of the company’s financial data, including earnings, transcripts, 10-K’s, and 10-Qs, to get a complete picture of its financial health.
The sheer volume of financial information can create a bottleneck, which can impact either the speed or the accuracy. With the emergence of AI agents in finance, this is beginning to change for the better. Let’s discuss how.
Beyond chats – the role of AI in financial research
In 2023, chatbots (powered by Generative AI) dominated business headlines. These chatbots could practically summarize any uploaded document or email. In 2026, the digital co-worker can perform the workflow of financial research.
Powered by AI agents, the credit research analyst doesn’t simply “read” but “investigates” the available information. As an example, this AI agent can autonomously navigate databases of compliance-related regulations, cross-reference financial metrics, and flag any financial risk (without any human prompting).
How it works – the power of parallel processing
At its core, an AI-powered credit research analyst is enabled by multi-agent collaboration and parallel processing. Typically, a human analyst can sequentially tackle tasks – for instance, read the 10-K document, check the earnings, and then the latest news updates.
A digital co-worker can simultaneously execute the roles of the following 4 AI agents for research:
- Financial metric analyst
This AI agent can extract and calculate key ratios, such as EBITDA and liquidity. - Risk assessor
This AI agent scans for any business risks, litigations, and market threats. - Regulatory auditor
This agent reviews the SEC filings for any compliance-related flags and disclosures. - Alternative indicator analyst
This agent can search for and analyze non-financial indicators for health or financial distress.
Through parallel processing, these AI agents can complete financial due diligence in a fraction of the time taken by human teams. Next, let’s look at some of the key financial metrics impacted by the use of AI agents in finance.
3 key metrics impacted by AI agents
The use of AI agents can have an immediate impact on the following 3 key metrics in financial services:
- Speed
With AI agents, financial service companies can complete a comprehensive credit analysis in under 2 minutes. This allows them to screen potential opportunities or risks in real-time. - Traceability
Traditional AI models can cause hallucinations in the financial services domain. With Agentic AI, companies can ensure 100% source transparency. Every AI-enabled insight or red flag is directly mapped to a specific section in the SEC filing or source document. Additionally, human agents can verify the source instantly. - Precision
By automating the ingestion of 10-K and 10-Q filings, AI agents can practically eliminate human errors associated with manually comparing data.
Conclusion – the future of credit analysts
In conclusion, does this mean that the AI-enabled digital co-worker will replace human credit analysts? Not really. By taking up the manual tasks of data extraction and synthesis, Agentic AI can free up the human analyst to perform strategic functions. This means they don’t need to spend 80% of their working time searching for relevant information, but instead on making informed decisions.
Credit analysts can focus on strategic roles such as improving client relationships and making investment-related decisions. With the AI-enabled credit research analyst, human analysts have a smarter tool at their disposal.
Here’s a short video of how the credit research analyst works.
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