Bank Taps Semantic Intelligence to Unify Risk Analytics
A global investment bank recently faced confusion due to differing interpretations of data by its fraud detection and boa teams. This issue, stemming from traditional risk analytics lacking shared meaning and context, highlights a common challenge in modern finance. Gartner estimates that poor data quality, often due to misaligned meaning, costs organisations USD12.9 million per year on average.
To tackle this, the bank turned to semantic intelligence. This innovative approach introduces shared context into data, enabling teams to understand and interpret information consistently. By implementing a semantic layer, the bank unified definitions, metrics, and business logic across its systems, creating a single contextual foundation for risk analytics.
In a significant feat, the bank analysed 500 billion transactions in mere seconds using semantic intelligence. This enhanced risk predictions and response times, demonstrating the power of this new technology. Notably, Goldman Sachs has also adopted semantic intelligence to create better risk predictors. The future of risk management, it seems, will be determined by context, with semantic intelligence transforming data into meaning and risk into opportunities.
Semantic intelligence not only improves risk management but also weaves governance into the process. It ensures compliance and accountability without slowing down business operations. Vodafone Portugal's switch to a universal semantic layer platform on GCP exemplifies this, enabling real-time data access and self-service tools for agile decision-making. Moreover, semantic intelligence provides a strong and consistent database, laying the groundwork for successful AI implementation in risk handling. As such, it appears that semantic intelligence is set to revolutionise the way global investment banks manage risk.
