Ed Chalopin, Chief Product Officer at Likezero.
The collapse of Silicon Valley Bank was not just an isolated incident; it was a clear indicator of the vulnerabilities and deep cracks that run through the financial sector.
Data forms the foundation of every decision a bank makes, and events such as these have made it abundantly clear that not every bank’s infrastructure is robust enough to weather crises they come up against. To avoid becoming the next cautionary tale and effectively navigate the complexities of political and market shifts, banks must embrace artificial intelligence, not just as a tool for efficiency, but as a transformative approach to enhancing data quality, governance and resilience.
Turning data quality into strategic insight
Data quality has emerged as a key concern within the industry. At a recent Risk Live Europe panel, 38% of participants acknowledged data quality as a significant challenge in their AI initiatives. AI and machine learning (ML) systems do more than just analyse data — they can help correct it. New technology automates data capture from trading agreements at scale, producing complete structured data sets, providing a full picture of their counterparty risk. In addition to highlighting inconsistencies or outliers. It stores the structured dataset in a way that gives companies the full picture, where previously they’d only be able to see specific data points they were manually searching for. This means that AI can then leverage the data and transform it into actionable insights, allowing banks to make more strategic decisions. Financial institutions that make decisions based on outdated or fragmented data are like Formula 1 cars running on low-grade fuel: they might make it to the finish line, but they won’t win the race. AI changes the game by not just processing data but actively improving data operational processes.
Breaking down data silos
One of the most significant barriers to effective data governance in banks, particularly around capital markets trading agreements, is the existence of data silos — isolated pockets of information, on numerous systems, that prevent a comprehensive view of the firm’s position. These siloes not only limit the effectiveness of AI but also create blind spots in risk management and strategic planning. By breaking down these internal barriers, building a unified data environment and bringing in AI capabilities, one creates transparency and effective counterparty risk management across the organisation. As AI processes trading agreements data from various departments, it can also identify patterns and trends that might not be visible when data is fragmented. This integration is crucial for staying ahead.
Automating manual processes
AI is already proving invaluable in automating complex, time-consuming processes, particularly in areas like the trading agreement management lifecycle. Traditionally reliant on manual processes, this area is ripe for transformation. AI and ML technologies can now automate the extraction and analysis of data from legal agreements, such as collateral agreements, significantly reducing the time and error involved in manual processing.
By leveraging AI-based question-and-answer techniques and advanced natural language processing (NLP) methods, banks can convert unstructured data into structured formats tailored to specific business needs. This not only speeds up decision-making but also improves accuracy in critical areas like credit risk and compliance.
Conclusion
We’re living in a world where data is power. Banks that continue to rely on manual processes and fragmented data are not just hindering their growth—they are courting disaster. The key to avoiding the next crisis lies in AI’s ability to turn data into a strategic asset. The organisations that fully leverage AI’s capabilities to integrate and optimise data will stay ahead of the tide, rather than just keeping afloat.
