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Traditional credit rating models from the Big three typically have not proven themselves very well, especially during the 2008-2009 financial crisis, where their ratings of structured products and financial institutions were either too static, and backward-looking or could not respond to the point-in-time behavior of the credit and financial markets under stress, often with no early warning indicators of the potential stress.
They were not forward-looking and failed to incorporate dynamic information to make their models more robust and responsive to dynamic changes in the environment and business cycles.
Enter KRIS, the dynamic and adaptable credit risk information services from Kamakura, built up from ab initio principles of credit analytical and bond pricing models based on rigorously tested and validated academic studies, all of which have been published in world-renowned refereed financial journals and adopted by financial institutions globally, like the HJM model. Unlike the legacy models above, KRIS incorporates information from various sources, like the macroeconomic factors, company-level data and financial ratios, business cycles, management and staff turnover information, existing public ratings and other real-time information, to yield a unique dynamic ratings model, which adapts to the changing business environment, with an automated cross-validation process to bond prices and credit spread information to ensure proper benchmarking and reflection of the current state, including the Kamakura Troubled Company Index which provides a daily measure of global credit quality based on the aggregate level of default probabilities in the KRIS coverage universe, doubling up as an early warning indicators by region, sector and name level.
This leading edge approach to credit risk modelling, harnesses all the available data and information and stands out on its own as a thought leader providing an alternative to the ratings business that is more forward-looking and grounded more in empirical data to yield an evidence-based model, providing transparency and also attribution to the drivers causing changes in the credit-worthiness of the counterparties.
In addition, when integrated with the market information about the assets and liabilities (products, companies and SMEs, etc.) typically owned by the ultra high net worth or high net worth individuals in a private bank’s or wealth management unit’s portfolio, a one-by-one personal ecosystem profile of these individuals and their associated companies can be built up using the KRIS platform to provide the banks with the advanced credit analytical tools to monitor rigorously the “personal balance sheet” and credit profiles of these individuals, bringing the wealth management business to another level with this risk-based approach.
KRIS is also embedded with portfolio risk management capability. Users could upload the portfolios and run Valuation, Stress Test Scenarios, Value at Risk simulations, Multi-Period Credit simulations as well as Credit Value Adjustment calculations based upon the relationship between PDs and Macro Factors. Most financial products can be modeled in KRIS or easily added. The easy to use interface provides the user with the ability to access the power of an enterprise-wide risk engine without the need for a costly implementation process or the time-consuming setup. A user can run a portfolio simulation and generate report output in a matter of minutes on an ad hoc basis and also do “what-if” type portfolio analysis.