Part 1: What the Infrastructure Needs to do
Trades and Products
Where do Trades Come From?
The Purpose of the Infrastructure
Part 2: The Problems with Trade Processing Infrastructure
The Evolution of Technical Complexity
The Regulatory Challenges
The Complexity Cycle
Part 3: Historic Approaches to Transformation
Functionalisation, aka “Factories”
The Golden Middle
Part 4: New Approaches to Infrastructure
Cloud and Utilities
Artificial Intelligence and Robotics
Big Data and Analytics
Blockchain/Distributed Ledger Technology
Distributed Ledger Technology: Hybrid Approach
We are going to get our data scientists to use their data goggles to look into our data lake for insights.
– Project manager of a data program at a major global bank
One of the many things that has become painfully obvious to the senior management of many financial firms, as well governments and even politicians, is the poor quality data about capital markets transactions. As the great financial crisis hit, management in many firms became aware that they lacked an accurate view of their risk in specific markets or against specific counterparties. In many cases, they also found unexpected trades and risks on their books. The view of central banks and regulators of what was going on was typically much worse.
Latterly, many firms in the financial sector have shown a great deal of enthusiasm for the potential of big data and analytics to solve their problems. This has manifested itself in spending on technologies such as Hadoop, the creation of chief data officer roles and the hiring of data scientists, who presumably know the correct way to wear a pair of data goggles. The odd thing about this enthusiasm is that capital markets firms have known for decades that they could