Ahead of the 2nd Annual Liquidity Risk Management Congress 2017, we interviewed Volker Liermann, Partner, Global Sales Team & Funds Transfer Pricing Expert, ifb Group.
Volker, can you please tell the Risk Insights readers a little bit about yourself, your experiences and what your current professional focus is?
I have been working in the Banking Industry for more than two decades, mainly focusing on financial risk management. Throughout my career, I have focused on developing integrated and comprehensive frameworks to help organizations correctly allocate and forecast profitability at a strategic and tactical line of business and departmental level while managing risk. In recent years, my focus has been on developing frameworks to integrate stress testing, liquidity risk management, and Funds Transfer Pricing as it relates to both organizational profitability and meeting regulatory requirements.
At the Liquidity Risk Management Congress, you will be speaking on your insight regarding – Integrating liquidity risk management into Funds Transfer Pricing frameworks. Why do you believe this is a key talking point in the industry right now?
In today’s banking environment, a key question is: Where can banks generate profit without taking any risk? Perhaps the place no longer exists “the shop with the free lunch has closed”, but banks can aim to make higher profits while taking lower risk in a cost effective manner. Prior to the 2008 financial crisis, profits were accepted without seeing or wanting to see the attached risk within the transactions. This is to some extent still true for liquidity driven risks and profits.
In your experience, how can financial institutions best manage integrating liquidity aspects into a funds transfer pricing framework?
To integrate liquidity risk into FTP you need a clear picture of which drivers contribute to profits and risks. Inside an FTP framework the drivers are interest rate movement, liquidity and the uncertainty in the transaction.
What in your opinion, is the best practice is for dealing with uncertainty in the funds transfer pricing?
The first step is for organizational leadership to acknowledge that there is uncertainty in this context, next you develop metrics or KPIs to quantify this uncertainty. This step itself is two-folded: 1) ex-post analysis: how can I quantify profits & losses when the transaction has ended (profit-view) and 2) ex-ante prognosis of the distribution of the future profits & losses (risk-view). The last step is the hardest: incorporating the metrics into FTP and risk management landscape, including communication and reporting of the metrics.
Can you give an overview on profit and risk, an integrated view on liquidity?
When you are using FTP with liquidity integrated you can see the contribution made by the client related divisions (asset and liability side) distinguishing between interest rates and liquidity. The same distinction in interest rates and liquidity can be made for the contribution of the treasury department. The integrated view develops by adding the separated risk view for interest rates and liquidity. This view can oppose the profits from the recent period and the risks taken in this period. The most value arises when you add the planning into the framework, allowing you to make target-performance compression on the aforementioned drivers.
What, in your opinion does the future hold for liquidity risk professionals, and how can they keep up the increasing change?
Liquidity risk will become more quantitative in some areas, like the question on how to estimate the movement or flow in retail deposits. In the first step time-series analysis and classic machine learning will improve the prediction accuracy and reduce uncertainty or at least will help to handle the uncertainty better. If AI will contribute to more accuracy then time-series analysis and classic machine learning has to be proven. The handling of the uncertainty will become – one or the other way – more quantitative.
To bring this value to the whole organization you need excellent communication skills. So the combination of complex methods and the ability to explain them is the key to a successful implementation.