Monolithic Objects to Cognitive Graphs

In the high-stakes world of investment banking, the ability to price complex derivatives and manage firm-wide risk in real-time is the ultimate competitive advantage. For decades, three proprietary platforms—Goldman Sachs’ SecDB, JPMorgan’s Athena, and Bank of America’s Quartz—have defined the gold standard of financial engineering. While these Holy Trinity systems share a common lineage, they represent a technological evolution that is now reaching its architectural limit, paving the way for a new era of AI-driven intelligence.

The story began with SecDB (Securities Database) at Goldman Sachs. Developed in the early 1990s, SecDB was revolutionary because it unified pricing models and risk data into a single, globally distributed object database. It utilized a proprietary functional language called Slang, allowing quants to write code that instantly updated risk across the entire firm. This "single version of the truth" famously allowed Goldman to navigate the 2008 financial crisis by identifying subprime exposure faster than its peers.

JPMorgan’s Athena and Bank of America’s Quartz followed as spiritual successors, spearheaded by former Goldman engineers. Athena was designed to modernize the SecDB concept using Python instead of a proprietary language, emphasizing developer productivity and a glass box approach where code was transparent across the front and middle offices. Quartz similarly adopted Python, aiming to consolidate Bank of America’s fragmented legacy systems into a unified cross-asset platform.

While successful, these systems are monolithic in spirit. They rely on hard-coded dependencies and massive, centralized codebases that can be difficult to adapt to the non-linear, unstructured data demands of modern markets.

To move beyond the limitations of SecDB-style architectures, we propose a Cognitive Risk Architecture—a system that replaces static object hierarchies with a dynamic, AI-powered Knowledge Graph.

Traditional systems struggle with semantic drift—where different desks define risk or counterparty differently. By using SKOS (Simple Knowledge Organization System), we can create a standardized taxonomy of financial concepts. This feeds into a Knowledge Graph (KG), where assets, entities, and global events are represented as interconnected nodes rather than isolated database rows.

While SecDB calculates Greeks (Delta, Gamma, Theta) through numerical methods, a Graph Neural Network (GNN) can learn hidden relational patterns across the market, such as how a liquidity squeeze in one sector propagates through a network of counterparties.

• GraphRAG: By combining Retrieval-Augmented Generation with the KG, an LLM can provide explainable risk reports. Instead of just seeing a VaR (Value at Risk) spike, a trader can ask, "Why is my exposure increasing?" and the system will trace the path through the graph to show a specific geopolitical event's impact on a supplier.

The greatest weakness of legacy systems is that they are correlative, not causal. Integrating Causal Models allows the system to run Interventional and Counterfactual simulations.

• Intervention: "If the Fed raises rates by 50bps, what actually causes my portfolio to bleed?"

• Counterfactual: "What would have happened to our hedging strategy if we had moved to cash two days earlier?"

By marrying the rigor of SecDB’s quantitative roots with the fluid reasoning of LLMs and Graph-based AI, the next generation of banking tech will move from simply measuring risk to truly understanding it.