Agent-based simulation offers a way forward for banks seeking to get an holistic perspective on the interest rate risk on their books. The Basel Commission on Banking Standards (BCBS) has placed increased emphasis on asking banks to study how movements in interest rates affect banks’ profits and capital. This has proved challenging for even the largest banks.
Critical business issues are being automated and solved by AI. Companies are extracting more value than ever before from the data they collect. And these trends look set to continue. But AI is still struggling to help business leaders in industries like finance, transportation and healthcare make better decisions in the complex systems they operate in.
Failing to know-your-customers is expensive. The push for banks to develop stronger know-your-customer (KYC) procedures continues from authorities, and they are prepared to hit banks hard in the pocket. By way of example, Deutsche Bank’s $630 million fine for failing to prevent $10bn of Russian money laundering shows just how crippling the punishments can be. On top of the threat of fines, banks face the direct losses from financial crime: Bangladesh Bank took an $81 million hit in 2016 when fraudulent transactions were made via the Swift network.
X-Valuation Adjustment is a catch-all term for various adjustments made to derivative instruments. These kinds of calculations are computationally intensive: they require a modeller to calculate a larger number of default scenarios and the potential loss (Potential Future Exposure, or PFE) in each case. Agent-based simulation provides a framework to compute more accurate valuation adjustments for better risk management; let’s see why.
Although wisdom sits at the apex of the knowledge hierarchy, it is often the aspect that is least discussed. Wisdom should be the ultimate goal of any analytical endeavour. Scrutinising data lets us explore structures, spot patterns, and understand relationships. However, insight from data alone is insufficient to make wise decisions when we are confronted by new and unseen scenarios. Why is this the case? And how do we gear our analysis towards obtaining wisdom?
Changes in accounting standards tend to be pretty dull, but this one is seismic. IFRS 9 introduces a new era for expected loss provisioning: banks now need to model expected losses throughout the lifetime of a financial instrument. In practice, this means simulating millions of realistic future scenarios in order to accurately compute probability-weighted losses. Advances in complexity science and computational economics have made this type of risk management possible, allowing financial institutions more than just simple compliance, but also true business value and competitive advantage.
Contagion risk is the risk that a shock to one financial institution spills over to others. In this way, small shocks can have significant effects. Contagion is one of the key dynamics that gives rise to systemic risk in a complex adaptive system. The complex web of interconnections between the constituent parts of the financial system can act as pathways along which instability spreads and amplifies.
Traditional modelling approaches are ill-suited to the type of scenario-based risk management that stress testing is concerned with. Agent-based modelling (or ABM), in contrast, provides a natural framework for exploring challenging scenarios faced by complex adaptive systems — as I shall illustrate later in this post with a simple mortgage model.