Monitor banks' risk in real-time. Benchmark their public Risk, Liquidity and Capital disclosures. Stay ahead of the public sentiment and react quickly to negative news.
An index used to gauge the overall sentiment of bank's risk in the media.
Sentiment analysis scans worldwide news sources to identify articles related to each bank. Subsequently, we assess the sentiment of these articles across various aspects such as solvency, liquidity, reputation, and political risk, assigning a score ranging from 0 to 100. This score reflects the percentage of instances in which the bank was depicted in a negative light.
Risk sentiment analysis of media channels can be a valuable tool. Banks can perform sentiment analysis by monitoring various media platforms and sources. Here are some of the different types of media platforms and sources to consider:
Early Warning System
Banks can use sentiment analysis to monitor media channels for early signs of emerging risks. By analyzing news articles, social media posts, and other media sources, banks can identify trends and sentiment shifts related to economic conditions, industries, or specific companies. Early detection of negative sentiment can provide a warning of potential financial risks.
Customer Sentiment Analysis
Banks can use sentiment analysis to understand how their customers perceive their services. By monitoring customer feedback on social media and other platforms, banks can identify areas of concern and make improvements to enhance customer satisfaction and loyalty.
For publicly traded banks, sentiment analysis can be valuable in managing investor relations. Banks can use sentiment analysis to understand how their financial performance and corporate announcements are being received by the investment community.
A comparison of Risk KPIs across major Banks
Pillar 3 is one of the three pillars of the Basel III framework, which is a set of international banking regulations developed by the Basel Committee on Banking Supervision. These regulations were created to strengthen the stability and soundness of the global banking system. It encourages banks to provide information about their risk profiles and capital adequacy to enable market participants to make informed decisions and contribute to the stability of the financial system. We use the public disclosure of banks under Pillar 3 to benchmark bank's risk KPI and Stress Test bank's resilience to changing market conditions.
Benchmarking how a bank's risk metrics compare to the industry offers several benefits, which can aid in enhancing risk management, decision-making, and overall performance.
Optimize Liquidity Portfolio
Financial regulators require banks to maintain a minimum level of liquidity to ensure their ability to meet depositor and creditor demands. A diverse range of liquid assets in the HQLA portfolio can spread risk across various asset classes, reducing concentration risk and enhancing overall portfolio resilience. Optimizing its composition can yield additional returns whilst remaining within the risk appetite.
Capital and Liquidity calculations involve intricate models, comprising components stipulated by regulators and others built in-house. With our expertise in model development, we can assist banks in identifying areas for enhancement in both their models and overall financial positions. Additionally, we can collaborate with local teams to implement these improvements effectively
Reverse Stress Test
We can assist in identifying market conditions that might lead the bank to exceed its risk appetite and regulatory thresholds. Furthermore, we can compare how comparable scenarios would impact other banks within the peer group. This analysis can be instrumental in pinpointing vulnerabilities in the bank's risk position, as well as in its liquidity and capital management practices.
Create Market Consistent Scenarios to Identify Weakness in the bank's Balance Sheet, Capital and Liquidity KPIs
Interact with the charts by dragging any 2 points on the charts to create a new scenario (for example, drag the 2y and the 5y Government Bond points down). This will create a consistent scenario based on historic market correlations. You can then tweak the generated scenario by adjusting the suggested shocks. The severity indicator at the bottom suggests how different the scenario is from historic correlations. The algorithm is based on a multivariate distribution calibrated on at least 10 years of historic data.
Creating severe yet plausible stress scenarios is essential for stress testing a bank's portfolio for several important reasons.
Generate severe yet plausible stress scenarios by introducing shocks to two, or more variables (e.g. the 2-year and 5-year German government bond rates). The models will automatically propagate these shocks to all other relevant variables. Additional inputs can also be provided to tweak the scenario. These scenarios can then be employed within Risk and ALM (Asset and Liability Management) systems to project the changes in the balance sheet, earnings, and other key performance indicators (KPIs) of the bank.
Risk Factor Expansion
Expand the scenarios provided by the regulators to market factors that are required by bank's internal models. The EBA, or the Bank of England stress tests typical provide 100s of variables in the definition of their Stress Scenarios. These then need to be expanded to 1,000's of variables for the bank's internal stress testing systems. We can help with the expansion using our model to ensure consistent scenario across variables.
Fast Stress Tests
By generating stress scenarios in real-time and applying to publicly disclosed risk information we can execute top-down stress tests in real-time. The accuracy of the stress test result can we further enhanced by working with a bank's internal data while maintaining the speed and immediacy of real-time execution.
RiskView is maintained by three experts in the field of Risk, Data and Economics who spent decades in senior roles at Investment Banks, Asset Manager, Sovereign Agencies and Large Corporations across Europe
Dmitry spent 15 year in Investment Banking between London and Amsterdam. Most recently as Head of Stress Testing at NatWest Markets in Amsterdam, and prior to that held a number of senior roles in Risk, Treasury and Front-Office.LinkedIn
Paulo has 15 years working in Data at major financial institutions . He is one of the original founders of the AI&ML community at M&G Prudential one of the first AI teams in the city of London. More recently he has been the Head of Indices & Data Science at Skytra-Airbus.LinkedIn
Ricardo is an economist with 15+ years' experience, worked for EIB, ESM, BNP Paribas, Portuguese Budget Office, and Caixa Geral de Depositos. Focuses on banking, macro/sovereign, and climate risk monitoring, with models assessing risks in 150+ countries.LinkedIn