Creating a risk management framework with QlikView
Our client is a large international financial institution with multiple divisions, several brands and millions of customers. Alongside all organisations within the banking industry, our client is tackling a fundamental challenge: Achieving desired growth and revenue objectives while maintaining stability and operating efficiencies.
Both profit and loss (PnL) analysis and control and sound risk management practices are of paramount importance. These two critical pillars were the main focus of our engagement, and we managed to successfully address several issues by developing a solid BI solution based on the QlikView platform.
With constant market volatility and increasingly stringent regulations, all financial institutions need to better manage their exposure to fluctuating security prices and market conditions by establishing a robust risk management framework.
Any such framework should include such elements as:
- Effective liquidity risk management to ensure a bank’s ability to meet cash flow obligations.
- Gauging risk against the defined limits both in the short and long term in order to create balanced effective portfolio.
- Intraday profit and loss assessment in conjunction with risks through different portfolios, currencies time frame pillars and risk curves.
- Active management of collateral positions.
- Mandatory risks and liquidity reports ensuring consistency and transparency while meeting regulatory requirements.
The creation of such a framework for our client was complicated by several factors:
- The bank generated large volumes of risk data in a variety of different source systems which spanned multiple geographies, risk classes and lines of businesses. This meant that a significant effort was required to integrate all required data in one place.
- There was no single rule of risk breakdown by curves and pillars. Each system had its own set of rules, moreover, each line of business and department tended to have its own view and risk representation according to unique financial instrument features and these views constantly evolved.
- Due to the complexity of the risk calculation algorithms, a significant amount of time was required to calculate and display risk figures, whereas the business needed to be able to generate a fast response on risk changes because of new trades and market rates shifts which have not been taken into consideration by the calculations in the source systems. In addition, it’s often happened that algorithms in the source systems were not 100% accurate and required a lot of manual tweaks.
As a result of the above issues, departments were struggling with manual extraction, consolidation, and analysis of large volumes of risk data with uncontrollable error prone usage of Excel which took hours and did not provide desired insights and decision making speed. The problem multiplied when consolidating many spreadsheets into one leading to Excel anarchy
The QlikView Solution
To address the challenges, we built several integrated apps to provide intelligence and insights into risks and profit and loss figures. We ultimately creating an aggregated, up-to-date ‘single version of truth’ for transactional financial trade data across multiple and disparate sources. This single version of truth could then be shared throughout the organisation quickly and securely based on roles, as opposed to a reliance on complex manual Excel spreadsheets reports which the organisation could now retire.
The solution included the following key components:
- Liquidity risk limit management
- Risk and exposure analysis
- A risk limits control
- Intra-day PnL figures assessment based on risk curves
- Analysis of portfolio risks and PnL figures
- Analysis and control of collateral positions
- Automated regulatory reports generated from the system
The solution we developed on the QlikView platform delivered the following benefits:
- More effective internal reporting and compliance with external evolving regulations.
- Reduced time to data visibility, enabled faster decision making and an increase in productivity of analysis.
- Facilitated a more proactive approach to portfolio management, delivering a better diversified and more profitable portfolio according to existing risk tolerance policy.
- Reduced operational risk by allowing investigations to dig down to transaction level, uncovering underlying drivers and maintaining stability.
- Facilitated faster and easier ad hoc investigation and reporting.
- Eliminated the significant amount of effort previously required for manual reporting.
The project improved overall awareness and insight generation through dashboards sourced from large volumes of integrated transactional data. It also empowered business users to drive their own data discoveries instead of spending hours assembling data.