Financial risk management constitutions a crucial element of banks’ activity. Properly led, it allows for taking proactive steps to ensure more effective control over possible future events. Lack of such an approach, on the other hand, is oftentimes a source of a bank’s future crisis – to which they can only respond post factum, which not only is a more demanding challenge, but also jeopardises the bank’s reputation and operations, and is associated with high costs.
Banks are unique institutions characterised by a high level of leveraging and financed primarily by the funds entrusted to them by their clients. Their unique position is particularly visible with respect to the risk level, which is significantly higher than for enterprises operating in other sectors. High leverage as well as a transformation of maturity and due dates, resulting from an asset-liability mismatch, are the most recognisable features of banks’ balance sheets. Financed by the funds borrowed from their clients, banks are also obliged to return such funds to the clients if requested. For the reasons described above, banks are particularly exposed to multiple risks such as:
The fact that banks collect deposits as well as their impact on the stability of the financial system mean that they are subject to a complex system of regulations and control by supervisory bodies.
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Successful risk management is only more important in the contemporary environment of low interest rates and, following the crisis of 2008-2009, increased financial regulation (such as Basel III, IV, BRRD), whereby banks generate lower profits, have higher capital and liquidity requirements and act under high pressure of lower costs and downsizing. At the same time, they are under more increased supervision and need to meet more requirements than in the past.
We believe that working with our clients we help them not only to meet the prudential requirements, but also maintain the competitive advantage – for instance through introducing new cost-effective estimation methods and risk monitoring, fast and effective IT tools, as well as collaboration on completing regular tasks and activities.
In PwC we combine years of experience in the financial risk area with technological competences. This allows us to provide tools suitable for our client needs, having both strong business foundations and user-friendly interface.
DataValidatoR is an easy and extendable R tool for automation data quality analysis process. It contains predefined set of analyses and statistics for various types of data and it creates .docx document with the analyses results.
Challenges solution is addressing:
Banks are required to periodical data quality verification for the data used in the modelling process. Therefore automation of the process allows to save time and ensures comparability of the data quality monitoring results. The same applies to model validation or development, where data quality tests are a significant step of the model validation / development.
Result:
Data quality report in .docx format or in Shiny
Benefits:
Tool allowing for modelling of the Balance Sheet and Profit and Loss Statement along with key metrics (both regulatory (capital, liquidity) and profitability / performance (RoA, RoE, C/I, etc.)
Challenges solution is addressing:
Shortening and easing the lengthy and complicated process of making financial projections
Allowing for easy modelling of user-defined aspects
Allowing for easy development of varied modelling scenarios
Modelling of more than 1 scenario simultaneously
Allowing for easy iterations of the scenario / projection
Results & benefits:
Balance Sheet, Profit and Loss and key regulatory and profitability metrics projected according to predefined scenario
Easy analysis of results thanks to customizable views (charts, graphs, tables) of most important aspects of projected results (BS composition, RWA distribution, capital requirement levels vs. regulatory minimum, etc.)
Standalone dashboard for regulatory thresholds with easy to read breaches in projected periods
On the spot customization of scenario parameters allowing for further analysis / simulation
Easy comparison of selected scenarios in all modelling layers (Balance Sheet, Profit and Loss, Capital Requirements, Liquidity measures, Profitability indicators, thresholds)
Model Validation Engine is an innovative application that automates and facilitates the process of model validation. Navigating in a user friendly interface, you can conduct a thorough analysis of the quality of the model in a very short time, overview the results in real time and generate a report in the form of a structured editable document.
Model Validation Engine is built with cutting-edge technology. The heart of the application is a backend written in R and the server that connects all parts of the application and runs the process written in GO – modern programming language created by Google developers. The combination of these two technologies provides for the two greatest advantages of the Model Validation Engine: flexibility and high performance. Thanks to R codes new functionalities, tests, reports can be added in a matter of days, and are easily understandable by risk team members using R in their everyday work. Thanks to GO, the calculations can happen in multiple sessions, allowing for user access control, auditability, and very high performance.
Challenges solution is addressing:
Validation activities are mandatory for all credit risk models, required by the regulator and auditors. At the same time they are highly time-consuming and client's teams are spending long hours on preparing the analysis and then manually creating documentation. All of this can be done with one click using the Model Validation Engine allowing validation and monitoring teams to focus on more analytical tasks such as research on new ways of model validation or new modelling techniques. Additionally, this tool helps to structure the Monitoring/Validation process, providing user roles, access rights, version control, auditability of all previously processed validations which limits the operational risk.
Results & benefits:
Model Validation Engine gives you an opportunity to automate and standardize a very important part of the risk assessment processes. Thanks to our application, monitoring and validation activities will be less time consuming and your team could dedicate saved energy on exploring new areas of risk modelling. Moreover, you will be always ready for any regulatory inspection since your reports, input data and calculation parametrizations will be backed-up in the Model Validation Engine. Putting it in a nutshell, the benefits from having Model Validation Engine on-board are:
BacktesteR is an application that focuses on backtesting of the risk models. The user can choose from multiple backtesting methods (e.g. test for equality of means, Monte Carlo, bootstrap), in order to investigate the quality of the model from various angles.
Results of the analysis can be reviewed in real time on the basis of charts and detailed summary tables generated by the application. The assessment of the output is based on the Traffic Light Approach. BacktesteR provides also a reporting feature. The results can be printed to a Word or PDF document which contains details and summary of the performed analyses. All of the above is packed in a user friendly interface which smoothly guides the user through each stage of the backtesting procedure. In the case of need for adding a particular backtesting method – it is not a problem at all. Flexibility of the application enables extending its functionality very quickly.
BacktesteR can also be integrated with Model Validation Engine – a very powerful application for model validation and monitoring.
Challenges solution is addressing:
Backtesting is a very important part of model validation and monitoring. Both activities are highly time-consuming, thus automation can be very beneficial for financial institutions. BacktesteR is an application which addresses these challenges giving the user comfort of a standardized approach to backtesting and providing easy to interpret indicators of model performance.
Results & benefits:
The main benefit of having BacktesteR on-board is that it saves time of your team simultaneously standardizing your approach to backtesting of risk models. Thanks to this, you can easily and timely capture the change of model performance eliminating the impact of backtesting methodology change. Moreover, all of your work is automatically documented in the form of Word or PDF outputs (other formats are also available). Flexibility of the design enables you also to extend the tool with other backtesting methodologies.
Saving time of your team simultaneously standardizing your approach to backtesting of risk models.
Easy and timely capture the change of model performance eliminating the impact of backtesting methodology change.
Automatically documented reports in .docx or PDF format (other formats are also available).
Flexibility of the design which enables you to extend the tool with other backtesting methodologies.
Result:
Benefits:
Our team comprises over 70 people – including both analysts fluent in multiple programming languages and big data, as well as experts in regulations and bank processes. We work in the following areas:
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