Data accuracy is arguably the No. 1 requirement for financial services firms using GenAI and Large Language Models. Inaccurate, low-quality, or disconnected data has a cascading effect with implications for strategy, operations, risk management, and compliance.
This article explores causes of data inaccuracy and the role of retrieval augmented generation (RAG) to help mitigate the issue.
Main Causes of Inaccurate Data
In financial services, data inaccuracies typically result from one or more of these main circumstances. As firms are ingesting higher volumes of data types from a growing number of vendors as well as incorporating proprietary and third-party sources, awareness of the causes is increasingly important.
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Data entry and validation errors: Over time, manual entry of financial data and insufficient quality checks can lead to transcription errors, incomplete entries, incorrect formatting, or missing data.
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Outdated information: Data can decay over time if not regularly managed, leading to inaccurate analysis and misguided decisions.
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Integration issues: Disparate legacy systems within a firm may not connect well with new technologies, causing mismatches or lost information.
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Inconsistent data standards: Different departments may use varying governance standards or formats for data, making it difficult to aggregate or compare information.
Hallucinations
Hallucinations are another cause of inaccurate data and are one of the key challenges around GenAI to resolve in the financial sector.
Hallucinations are basically errors in fact and logic; instances when a model generates coherent, plausible-sounding text that is actually inaccurate, misleading, or completely fabricated.
The phenomenon happens because Large Language Models predict words based on patterns learned from training data. Models do not possess an understanding or a knowledge base that ensures factual accuracy based on research.
Why RAG is Essential in AI Systems
Perhaps the most important strategy to vastly reduce hallucinations and enhance GenAI accuracy is retrieval augmented generation (RAG), which is available to engineers building software products on top of an LLM. RAG is the programmatic version of providing context in a prompt and helps ground the LLM responses in factual information.
Augmenting responses from an LLM with RAG provides a number of key benefits:
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No need to re-train or fine-tune the LLM
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Better accuracy and fewer hallucinations since answers are derived from proprietary data
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Improved auditability with the source of an answer
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Enablement of up-to-date knowledge and user-based security
RAG combines the generative capabilities of LLMs with an effective data retrieval system. For example, think about asking an LLM to identify the key risks of an investment in a specific company stock. A LLM without RAG and without current training on that company would likely reply that no specific context about the investment risks of the company has been provided. Or worse yet, it may provide a response relying on out-of-date information used when training the LLM. The best it could do, with additional prompting, is summarize the typical risks to consider when investing in equities.
A LLM using RAG within a governed, regularly updated data source will summarize the specific risks to an investment in that company stock and link to each risk’s specific source, such as a 10-Q. It could also provide a view of that company’s financial highlights and keys stats on trading, valuation, and estimates.
Additional Ways RAG is Beneficial
Earlier we highlighted differing data formats as one cause of inaccurate data. Although RAG doesn’t standardize your data formats, it does enable more effective use of your data across formats.
For example, the retrieval aspect of RAG can process and represent data from unstructured and structured data sources in a unified approach. In addition, RAG is valuable because it connects a firm’s existing legacy systems and data silos as knowledge sources. There is no need for time-consuming system migrations or expensive LLM re-training.
Learn More About Retrieval Augmented Generation
Although RAG is not a magic catch-all solution for solving all data-accuracy issues, it does help address some of the main causes of inaccurate data we’ve discussed.
Learn more about retrieval augmented generation in our popular e-book and podcast episode.
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