Enthusiasm for blockchain, or the broader family of distributed ledger technologies (DLT), within capital markets is now into its third year. The enthusiasm has manifested itself in numerous pilots, proofs of concept, fintech startups and industry collaborations.
Within investment banks, large broker-dealers, and many “buy-side” firms, the enthusiasm has in large part been driven by a combination of heavy demands on IT departments and considerable pressures to cut costs.
This paper argues that there is a key factor that has prevented the delivery of any significant working systems within these enterprises; a general tendency to try to fit a solution (blockchain and its derivatives) to problems, rather than trying to understand problems and find the appropriate solutions.
Relative analysis of firm infrastructure suggests that the root causes of most problems are not technical but are human in nature; being related to incentives, culture and organizational structure. The analysis demonstrates that trade processing data and business logic are highly distributed but frequently highly inconsistent.
A model is proposed (drawing on many of the concepts of DLT) to create both transparency of issues and mechanisms for the propagation of consistent business logic and consistent data models. This aims to use DLT based techniques to deal with fundamentally human problems by the introduction of the appropriate feedback loops for management decision making.
Understanding the nature of problems and the effectiveness of changes would allow genuinely evidence-based management decision making. A technologically driven, human transformation that could act as a lever for unravelling organizational complexity.