The 2007–08 financial crisis highlighted the interconnectivity of the global financial system. To prevent such an event from occurring again, central banks and regulators are turning to massive amounts of new supervisory data to gain insights on how to better model and stress‑test complex financial systems.
Financial Network Analytics’ (FNA’s) mission is to make the financial system safer and more efficient, while playing a pivotal role in the growing use of artificial intelligence and machine-learning tools in financial supervision and oversight. The company has developed software that allows business analysts to map and monitor complex financial networks and simulate operational and financial risks, winning it Central Banking’s FinTech & RegTech Global Award for Best Analytics Solution Provider.
Hosted on FNA’s secure cloud environment or on site, the FNA Platform uses an intuitive scripting language and more than 300 out‑of‑the‑box graph and machine-learning algorithms to allow users to analyse and visualise supervisory datasets. This would usually take months; with FNA’s platform, the process can take just hours. Users can also configure the platform to monitor their systems in real time, and to carry out predictive modelling through simulations and stress tests.
Central banks and regulators already use the FNA Platform in a number of ways in banking supervision, financial market infrastructure design, oversight and operations, systemic risk surveillance, and financial crime surveillance. FNA undertakes extensive research and offers a number of solutions that address these areas. In Latin America, one central bank is using the platform to analyse interbank payments and has been able to monitor its banking system in near real time. Automatic alerts notify the regulator of anomalies in its network, and automated stress tests help it understand the risk inherent in the system and comply with international standards.
The system has also highlighted where cost savings could be made. Payments Canada is currently undertaking a multiyear project to develop a new real‑time payment system. Initial simulations with FNA revealed that moving to a real‑time gross settlement arrangement could increase participants’ liquidity needs by more than 40%. It also found that advanced liquidity‑saving mechanisms could bring this down substantially. FNA’s platform revealed a potential liquidity saving of $4 billion.
In Asia, a regulator wanted to identify risk concentrations in its derivatives markets, improving transparency in the process. Using derivatives data collected through its trade repository, the regulator has been able to use the FNA platform to design and implement an initial framework for analysing the financial stability of the market and its potential risks. Its users also say it could fundamentally transform how money laundering and fraud is averted at global banks.
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