Case Study: Dealing with Risk and UncertaintyOverviewIn this case study assignment, you will select a well known companyor organizationof your choice that has been dealing with risk and uncertainty...

Case Study: Dealing with Risk and Uncertainty among Banking sector
    Case Study: Dealing with Risk and Uncertainty among Banking sector
DELL    [Pick the date]    
Over the last year, the macroeconomic climate has been marked by rising uncertainty, excessive volatility, and elevated event risk. These reasons, combined with an uneven economic recovery, have driven many financial institutions to employ new analytical skills in a variety of business activities. At the same time, the business landscape is evolving, with digitalization speeding and a wave of acquisitions expanding model stock in the US and Europe.
Threats and risks are antecedents of business crises. Some hazards are universal and constitute a threat to any firm, whilst others are relative and rely on the nature of a company's business or sector. Regardless of the risk or danger that pertains to the organization, having an up-to-date crisis management strategy in place to respond to a crisis when the risk becomes a reality is critical. An informal poll of business leaders showed what they believe are the most significant dangers and challenges that firms face today or will face in the future.
McKinsey has asked groups of risk managers to meet over the last year to explore the most sophisticated technologies in risk modeling and risk management modeling (MRM). We received ideas from companies in the United States and Europe on a variety of modeling issues and possibilities during our roundtable talks and through our global MRM survey.
The sessions' outcomes shed light on the cutting edge of banking MRM and show a number of issues that are likely to affect institutional approaches in the coming year. They specifically indicated three crucial transformations: Automation of the model lifecycle, increased emphasis on efficiency, digitalization, and; increasing MRM's reach to new areas such as climate, networking, sales and marketing, or human resources; and a focus on reducing risk and harnessing the possibilities of artificial intelligence and big data. All of this has signaled the start of a series of strategic and tactical changes that will shape the parameters of MRM in the next year.
The Three crucial transformations:
Many banks have lately increased their model inventories in response to the more complicated business and business environment, as well as the significant economic impact of the COVID-19 epidemic. Since 2019, model numbers in US banks have increased by 25%, while European institutions have increased by 13%.However, the procedure remains difficult. In Europe, Tier 1 models require an average of 20 weeks to validate, whereas Tier 2 and 3 models take 13 and 9 weeks, respectively. The length for repeating confirmations is 11 weeks, 6 weeks, and 4 weeks, respectively. Validation timeframes in the United States are usually shorter across all banks, with initial validation for Tier 1 models lasting 12 weeks and Tier 2 and 3 models taking 6 and 4 weeks, respectively. The average execution time for recurring validation is seven weeks, five weeks, and four weeks, respectively.
Banks say that when activity increases, expenditures in areas such as inventory management, reporting, and risk management have grown. As a result, several people have made initiatives to increase the MRM function's performance. Team leaders went above and above to guarantee that duplication and redundancy were reduced, procedures were streamlined, and risk-based methods were implemented throughout the business.
With rising capacity pressures, automation has become a more pressing necessity, supported by more standardized procedures. Increased efficiency (more uniformity and rigor across activities) and higher efficiency are two commonly claimed benefits of automation (e.g., freeing up the ability to validate models).
The most automated procedure in Europe, according to study respondents, is continual monitoring and testing, especially for models that undergo frequent testing, followed by validation testing. period. There is no distinction between model kinds. The biggest goal for the future is to automate MRM operations, followed by validation, testing, and documentation automation. US institutions are likewise concentrating on automating MRM procedures and controlling...