To provide daily, automated risk assessment of the financial products of various investment categories, taking into account the key risk factors of liquidity, market and credit risk. To enable presentation of product risk in the form of a comprehensible overall risk indicator (product risk classification or PRC), which can be used for both client consultations and any other commonly applied investment processes. To support the development of a collateral value model (or loan-to-value or LTV model) based on a PRC that calculates the collateral value of the investment portfolios of the bank’s clients.
The implemented solution provides the bank with a tool to help its investment business and loan business departments with the task of risk assessment. Daily assessment of the overall investment universe and existing stocks not only enables risk reduction in the acquisition of any new business, but also constant monitoring of the risk of existing stocks and investment portfolios. Thanks to the use of this SaaS solution, the bank incurs no infrastructure costs associated with a high-performing IT solution, no procurement costs for obtaining the additional data required for adequate risk assessment of investment products and no additional costs for operating the solution or market data management.
UnRiskOmega PRC (SaaS solution)
For years our client, a Swiss bank, has had a reputation as one of the most proficient investment managers in the German-speaking region. It is synonymous in the private banking sector with competence, security, customer focus, product neutrality and sustainability. To aid with risk assessment of financial products and calculation of the collateral value of client portfolios, the bank and a management consultancy firm mandated by them collectively went in search of a suitable provider of key risk indicators that would be capable of covering the entire spectrum of its own investment universe. Under evaluation, the PRC solution as service (SaaS) won over both the bank and management consultancy firm, for the intelligent quantitative algorithms are able to keep the cost of data procurement low, making it also an attractive prospect for small to medium-sized finance institutes.
In addition, a major driver of the decision to go with the UnRiskOmega offer was the high degree of flexibility afforded with integration of the service and configuration of the various models, plus the practical assistance with implementation and testing of the collateral value model.
The PRC model of UnRiskOmega AG takes into account the key risk factors of liquidity, market and credit risk and presents these risk factors in the form of an overall risk indicator (PRC) as a figure ranging from 1 (low risk) to 5 (high risk). The internal collateral value model (LTV) of the bank then uses that product risk classification as the basis of its own assessments. In order to do justice to the special requirements, additional key indicators in terms of liquidity risk were also calculated. These were defined by the Quant Group of UnRiskOmega AG in conjunction with the client’s specialists and the management consultancy firm and tested on a broad range of financial products. The model parameters were calibrated on the basis of real portfolios.
After only a three-month integration period, daily risk assessment of the bank’s financial products as well as calculation and monitoring of the collateral value of client portfolios were able to be used by the bank to productive effect. Within that time frame, as well as integrating the service within the core banking system, the PRC models were able to be extended, calibrated and tested with the aid of UnRiskOmega AG. Thanks to this SaaS solution, the bank has benefited ever since from a comprehensive service that reduces the operating and maintenance costs to a bare minimum.