15 Jul 19
A visualization based on real stock market data from Nasdaq
Solutions
Risk Management
Run multiple what-if scenarios to proactively revise your strategy, mitigate potential risk and optimize bad debt provisioning in order to make better informed, forward-looking decisions.
Challanges
Historical data
Current approaches rely on historical data and an assumption that our future looks like the past.
Regulatory requirements
Increasing regulatory requirements mean banks require a new set of tools to better assess and classify a range of exposures.
Complexity
Traditional measures of counterparty credit risk and liquidity risk fail to account for the fact that banks are incredibly complex.
Our solutions
01
Mortgage analytics
Recreate complex dynamics by using a bottom-up approach to model markets and customer behaviours.
02
Stress testing
Transition from regulatory to strategic stress testing and better explore how macroscopic outcomes and emergent behavior are generated from low-level interactions.
03
Contagion risk
Use simulation to link agents together through causal relationships to capture network risks and better understand contagion risk.
04
Credit risk
Run large numbers of scenarios, model systematic risk, and run reverse stress tests to inform lending criteria – all in a massively scalable framework.
Latest risk management resources
“
We are delighted to be using Simudyne’s toolkit as part of our ongoing efforts to improve the ways in which Barclays identifies and manages risk. By building agent-based models we hope to spot and prepare for risks arising from dynamic and large, direct and contingent, counter-party exposures. This helps create a more robust and stable bank for our customers, clients and shareholders ”
CS Venkatakrishnan
Group Chief Risk Officer at Barclays Bank.
Group Chief Risk Officer at Barclays Bank.