Unlocking Deterministic AI Accuracy in Banking and Financial Services

Let's face it, Generative AI can be a bit of a wild card. Ask it the same question twice, and there’s a chance that you’ll get two very different answers. Sometimes, that’s fine, or even desired – getting a variety of explanations on a complex technical topic can be beneficial. But when it comes to high-stakes, numbers-driven business decisions, this unpredictability, which is inherent in the technology, can be a liability. Scenarios like these demand deterministic AI accuracy. 

Imagine relying on AI to extract critical financial data, only to find it's inconsistent and unreliable. The consequences could be disastrous, leading to compliance violations, inaccurate portfolio recommendations, or damaged client trust.

This uncertainty is a problem that can't be ignored. But what if there was a solution? In this article, we delve into the root causes of this uncertainty, examine why it's prevalent across GenAI implementations, and explore its impact on GenAI users in their daily work. We'll then demonstrate how pairing a semantic knowledge graph and retrieval augmented generation (RAG) into a graphRAG enterprise AI system provides an unprecedented degree of deterministic accuracy, showcasing this capability through an interactive demo.

GenAI’s Achilles Heel: Non-Deterministic Responses

Large language models (LLMs) rely on probabilistic models to generate responses based on patterns from extensive datasets. When given a prompt, they don’t "know" the answer in a traditional sense – instead, they predict the most likely next word based on their training. Obviously, there’s a lot of guesswork involved. 

Retrieval augmented generation, a technique used in enterprise settings, reduces the uncertainty by grounding responses in an organization’s proprietary data. How does RAG improve the accuracy of ai responses? It does so by augmenting a user’s query with the relevant information retrieved from the organization’s knowledge bases using advanced search algorithms.

Get a deep dive on RAG and how to overcome RAG's limitations in our recent white paper: Advancing GenAI beyond RAG.

But even with RAG, this non-determinism – while dramatically improved – persists, as it relies on retrieving what it deems to be contextually relevant information from a knowledge base. Precisely which information ends up being retrieved can differ based on the data available and how it is interpreted by the system. 

RAG’s information retrieval stack may well identify and pass on the right “chunks” of corporate data to the LLM. But it’s still up to the LLM to correctly interpret the information it was provided. If a single chunk includes multiple similar financial figures, as outlined in a recent blog, the model may not pick the right one, despite constant improvements in large language model accuracy. It has no way to know which number aligns with which entity unless the context is obvious. 

Put more plainly, if you ask the GenAI system to give you the 3-year performance metrics of a specific bond fund, which is mentioned within a long financial report, you might want to think twice before trusting the output it provides. 

How Enterprise Knowledge Graphs Overcome AI’s Non-Determinism

Humans have a leg up over LLMs and RAG systems, in several ways. First, unlike GenAI systems, our brains seamlessly process (or "chunk") information flexibly across scales – phrases, sentences, paragraphs, and even ideas spanning entire documents. We are wired to pick up on key details and connect concepts intuitively, allowing us to understand and precisely extract information from documents regardless of whether the information is densely packed or spread out. 

Secondly, we carry within our minds a model of the world that lets us understand how concepts connect to each other. In keeping with the example used earlier, a person asked about a bond fund would immediately associate it with concepts like yield, duration, credit risk, interest rates, and income – structured mental links that resemble the nodes and edges of a knowledge graph.

And that is precisely how knowledge graphs transform the performance of GenAI systems. Enterprise knowledge graphs structurally represent relationships between entities like companies, people, financial figures, dates in a relational network, converting human-readable knowledge into a machine-readable format that adds much needed deterministic accuracy to GenAI outputs. To deliver deterministic outcomes, they take advantage of various factors:

Explicit relationships: A knowledge graph can directly connect related information "Company A → reported revenue → $10 million in Q1 2023" as a triple, removing ambiguity between similar numbers.

Query precision: Instead of searching based on raw text matching, users can query the graph semantically (e.g., “What was Company A’s Q1 revenue?”) and get an exact answer with deterministic accuracy.

Scale-free representation: Knowledge graphs operate at the level of ideas and entities, not chunks. Put plainly, they don't care if the information was originally buried in a paragraph or spread over several pages.

Beyond Accuracy: The Business Imperative for Deterministic AI in Finance

While the technical advantages of knowledge graphs in achieving deterministic accuracy are clear, the real value lies in the tangible business benefits they unlock. In industries like finance, where precision and reliability are paramount, deterministic AI – and the knowledge graphs acting behind the scenes – are moving from a ‘nice-to-have’ to an indispensable business imperative.

Navigate Regulatory Scrutiny: Deterministic AI, fueled by knowledge graphs, is becoming increasingly essential to meet regulatory demands and ensure robust compliance in finance.

Mitigate Financial Risk: Knowledge graphs provide the structured foundation to effectively audit, demonstrate compliance, and proactively reduce financial risk exposure.

Drive Confident, Data-Driven Decisions: Deterministic accuracy stands out against the uncertainty of probabilistic AI, delivering consistent, reliable insights for strategic decision-making you can trust.

Unlock Competitive Advantage: In today's complex financial landscape, deterministic AI provides the actionable insights needed to outperform, drive profitability, and secure sustainable growth.

Supporting Client Advisors with Deterministic Accuracy

We've put together an interactive demo showcasing how knowledge graphs acting behind the scenes in GraphRAG applications drive up the value that enterprise GenAI systems deliver in quantitative use cases that require deterministic accuracy. 

Explore our interactive demo of a financial advisor AI application and see how enterprise knowledge graphs empower client advisors with insights they can confidently stand behind. 

In the demo, our client advisor, Mark, is tasked with planning an investment strategy for their retirement, focusing on long-term growth, tax efficiency, and sustainability – a complex task with high stakes, both for Mark and his client. 

To offer sound investment advice, Mark needs to be able to reliably compare various investment options at a granular level, something that non-deterministic systems such as LLMs or even RAG GenAI systems struggle with. 

• Accurately retrieve financial figures: Using Squirro’s chat functionality, Mark can extract financial figures from documents and have them presented in a structured table. 

• Achieve deterministic accuracy: Because the data is stored in a semantic knowledge graph, the system returns the exact values connected to the right entities and time period. 

• Traverse the graph to explore related concepts: Mark can traverse the knowledge graph, which operates behind the scenes, to find financial products that are conceptually related. 

• Compare the performance of financial products: Mark can use chat functionality to compare the performance of different financial products, which are accurately presented side by side thanks to the information stored in the knowledge graph.

• Generate bullets for a client presentation:  In seconds, Mark can instruct the system to generate a summary of the findings for his next client meeting – all formatted for easy integration into a PowerPoint presentation.

Lead with our Guide for GenAI in Financial Services

It’s time to stop reacting to the GenAI revolution and start leading it. We’ve created a technical guide to help you get the most value from GenAI for a decisive competitive advantage in financial services. Learn how to transform data chaos into a unified intelligence engine, driving radical operational improvements.

Explore concrete use cases that deliver immediate impact, from process automation to smarter client engagement and superior insights. Get the definitive breakdown of what it takes to build robust, reliable enterprise GenAI and equip yourself to thrive in the next era of finance. Download your tech guide today and gain the edge: Transforming Banking and Financial Services With Enterprise GenAI..