Infosys Labs Briefings
2008 financial crisis has shown that the current liquidity risk management (LRM) approach is highly unreliable in a changing and difficult macroeconomic atmosphere. The need of the hour is to improve operational liquidity management on a priority basis.
THE CURRENT LRM APPROACH AND ITS PAIN POINTS
Compliance/Regulation
Across global regulators, LRM principles have become stricter and complex in nature. The regulatory focus is mainly on areas like risk governance, measurement, monitoring and disclosure. Hence, the biggest challenge for the financial institutions worldwide is to react to these regulatory measures in an appropriate and timely manner. Current systems are not equipped enough to handle these changes. For example, LRM protocols for stress testing and contingency funding planning (CFP) focus more on the inputs to the scenario analysis and new stress testing scenarios. These complex inputs need to be very clearly selected and hence it poses a great challenge for the financial institution.
Siloed Approach to Data Management Many banks use a spreadsheet-based LRM approach that gets data from different sources which are neither uniform nor comparable.
This leads to a great amount of risk in manual processes and data quality issues. In such a scenario, it becomes impossible to collate enterprise wide liquidity position and the risk remains undetectable.
Lack of Robust LRM Infrastructure
There is a clear lack of a robust system which can incorporate real-time data and generate
necessary actions in time. The various liquidity parameters can be changing funding costs, counterparty risks, balance sheet obligations, and quality of liquidity in capital markets.
THE NEED OF A READY-MADE SOLUTION In a recent Swift survey, 91% respondents indicated that there is a lack of ready-made liquidity risk analytics and business intelligence applications to complement risk integration processes. Since we can see that the regulation around the globe in form of Basel III, Solvency II, CRD IV, etc., are shaping up hence there is an opportunity to standardize the liquidity reporting process. A solution that can do this can be of great help to banks as it would save them both effort and time, as well as increase the efficiency of reporting. Banks can focus solely on the more complex aspects like inputs to the stress testing process and on business and strategy to control liquidity risk. Even though there can be differences in approach of various banks in managing liquidity, these changes can be incorporated in the solution as per the requirements.
CHALLENGES/SCOPE OF REQUIREMENTS FOR LRM
The scope of requirements for LRM ranges from concentration analysis of liquidity exposures, calculation of average daily peak of liquidity usage, historical and future view of liquidity flows on both contractual and behavioral in nature, collateral management, stress testing and scenario analysis, generate regulatory reports, liquidity gap across buckets, contingency fund planning, net interest income analysis, fund transfer pricing, to capital allocation. All these liquidity measures are monitored and alerts generated in case of thresholds breached.
Concentration analysis of liquidity exposures shows some important points on whether the assets or liabilities of the institution are dependent on a certain customer, or a product like asset or mortgage backed securities. It also tries to see if the concentration is region wise country wise, or by any other parameter that can be used to detect a concentration for the overall funding and liquidity situation.
Calculation of average daily peak of liquidity usage gives a fair idea of the maximum intraday liquidity demand and the firm can keep necessary steps to manage the liquidity in ideal way. The idea is to detect patterns and in times of high, low or medium liquidity scenarios utilize the available liquidity buffer in the most optimized way.
Collateral management is very important as the need for collateral and its value after applying the required haircuts has to be monitored on a daily basis. In case of unfavorable margin calls the amount of collateral needs to be adjusted to avoid default in various outstanding positions.
Stress testing and scenario analysis is like a self-evaluation for the banks, in which they need to see how bad things can go in case of high stress events. Internal stress testing is very important to see the amount of loss in case of unfavorable events. For the systematically important institutions, regulators have devised some stress scenarios based on the past crisis events. These scenarios need to be given as an input to the stress tests and the results have to be given to the regulators. A proper stress testing ensures that the institution is aware of what risk it is taking and what can be the consequences of the same.
Regulatory liquidity reports have Basel III liquidity ratios like liquidity coverage ratio (LCR), net stable funding ratio (NSFR), FSA and Fed 4G guidelines, early warning indicators, funding concentration, liquidity asset/
collateral, and stress testing analysis. Timely completion of these reports in the prescribed format is important for financial institutions to remain complaint with the norms.
Net interest income analysis (NIIA), FTP and capital allocation are performance indicators for an institution that raises money from deposits or other avenues and lends it to customers, or performs an investment to achieve a rate of return. The NII is the difference between the cost of funds to the interest rate achieved by lending or investing the same. The implementation of FTP links the liquidity risk/
market risk to the performance management of the business units. The NII analysis helps in predicting the future state of the P/L statement and balance sheet of the bank.
Contingency fund planning contains of wholesale, retail and other funding reports in areas of both secured and unsecured funds, so that in case of these funding avenues drying up banks can look for other alternatives. It states the reserve funding avenues like use of credit lines, repro transactions, unsecured loans, etc., that can be accessed timely and at a reasonable cost in liquidity crisis situation.
Intra-group borrowing and lending reports show the liquidity position across group companies. Derivatives reports related to market value, collateral and cash flows are very important to an efficient derivatives portfolio management. Bucket-wise and cumulative liquidity gap under business as usual and stress
scenario situations give a fair idea of varying liquidity across time buckets. Both contractual and behavioral cash flows are tracked to get the final inflow and outflow scenario. This is done over different time periods, like 30 days to 3 years to get a long term as well as short term view of liquidity. Historic cash flows are tracked as they help in modeling the future behavioral cash flows. Historical assumptions plus current market scenarios are very important in dynamic analysis of behavioral cash flows. Other important reports are related to available pool of unencumbered assets and non-marketable assets.
All the scoped requirements can only be satisfied when the firm has a framework in place to take necessary decisions related to liquidity risk. Hence, next we would have a look into a LRM framework and as well as a data governance framework for managing liquidity risk data.
LRM FRAMEWORK
Separate group for LRM that is a constituted of members from the asset liability committee, risk committee and top management needs to be formed. This group must function independent of the other groups in the firm
and must have the autonomy to take liquidity decisions. Strategic level planning helps in defining the liquidity risk policy in a clear manner related to the overall business strategy of the firm.
The risk appetite of the firm needs to be mentioned in measurable terms and the same has to be communicated to all the stakeholders in the firm. Liquidity risks across the business need to be identified and the key risk indicators and metrics are to be decided. Risk indicators are to be monitored on a regular basis, so that in the case of an upcoming stress scenario preemptive steps can be taken. Monitoring and reporting is to be done for internal control as well as for the regulatory compliance.
Finally there has to be a periodic analysis of the whole system in order to identify possible gaps in it and the frequency of review has to be at least once in a year and in case of extreme markets scenarios more frequently.
To satisfy the scoped out requirements we can see that the data from various sources is used to form liquidity data warehouse and datamart which acts as an input to the analytical engines.
The engines contain business rules and logic based on which the key liquidity parameters are calculated. All the analysis is presented in report and dashboards form for both regulatory compliance and internal risk management as well as for decision making purposes.
Some Uses of Big data Application in LRM 1. Staging Area Creation for Data
Warehouse: Big data application can store huge volumes of data and perform some analysis on it along with aggregating data for further analysis.
Due to its fast processing for large amount of data it can be used as loader to
Corporate Governance Strategic Level Planning Identify & Assess Liquidity Risk
Monitor & Report PeriodicAnalysis for Possible Gaps
Take CorrectiveMeasures
Figure 1: Iterative Framework for effective liquidity risk management
Source: Infosys Research
load data into the data warehouse along with facilitating the extract-transform-load (ETL) processes.
2. Preliminary Data Analysis: Data can be moved in from various sources and then using a visual analytics tool to create a picture of what data is available and how it can be used.
3. Making Full enterprise Data Available for High performance Analytics: Analytics at large firms were often limited to the sample set of records on which the analytical engines would run and provide certain results, but as a Big data application provides distributed parallel processing capacity the limitation of number of records is non-existent now.
Billions of records can now be processed at increasingly amazing speeds.
HOW BIG DATA CAN HELP IN LRM ANALYTICS AND BI
■ Operational efficiency and swiftness is a point where high performance analytics can help to achieve faster decision making because all the required analysis is obtained much faster.
■ Liquidity risk is a killer in today’s financial world and is most difficult to tracks as for large banks have diverse instruments and a large number of scenarios need to be analyzed like changes in interest rates, exchange rates, liquidity and depth in the markets
Big Data FTP & liquidity costs Funding
Figure 2: LRM data governance framework for Analytics
and BI with Big data capabilities Source: Infosys Research
worldwide, and for such dynamic analysis Big data analytics is a must.
■ Stress testing and scenario analysis, both require intensive computing as lot of data is involved hence faster scenario analysis means quick action in case of stressed market conditions. With Big data capabilities scenarios that would takes hours to otherwise run can now be run in minutes and hence aid in quick decision making and action.
■ Efficient product pricing can be achieved by implementing real time fund transfer pricing system and profitability calculations. This ensures the best possible pricing of market risks along with adjustments like liquidity premium across the business units.
CONCLUSION
The LRM system is the key for a financial institution to survive in competitive and highly unpredictable financial markets. The whole idea of managing liquidity risk is to know the truth, and be ready for the worst market scenarios.
This predictability is what is needed, and can save a bank in times like the 2008 crisis. Even at the business level a proper LRM system can help in better product pricing using FTP, and hence pricing can be logical and transparent.
Traditionally data has been a headache for banks and is seen more as compliance and regulation requirement, but going forward there are going to be even more stringent regulations and reporting standards across the globe. After the crisis of 2008 new Basel III liquidity reporting standards, newer scenarios for stress testing have been issued that requires extensive data analysis and can only be timely
possible with Big data applications. All in the banking industry know that the future is uncertain and high margins will always be a challenge, so an efficient data management along with Big data capabilities needs to be in place. This will add value to the banks profile by clear focus on the new opportunities for banks and bring predictability to their overall businesses.
Successful banks in future would be the ones who take LRM initiatives seriously and implement the system successfully. Banks with an efficient LRM system would definitely build a strong brand and reputation in the eyes of investors, customers, and regulators around the world.
REFERENCES
1. Banking on Analytics: How High-Performance Analytics Tackle Big data Challenges in Banking (2012), SAS white paper. Available at http://www.sas.com/
resources/whitepaper/wp_42594.pdf.
2. New regime, rules and requirements — welcome to the new liquidity, Basel lll:
implementing liquidity requirements, ERNST & YOUNG (2011).
3. Leveraging Technology to Shape the future of Liquidity Risk Management, Sybase Aite. Group study, July, 2010.
4. Managing liquidity risk, Collaborative solutions to improve position management and analytics (2011), SWIFT white paper.
5. Principles for Sound Liquidity Risk Management and Supervision, BIS Document, (2008).
6. Technology Economics: The Cost of Data, Howard Rubin, Wall Street and Technology Website, Available at http://
www.wallstreetandtech.com/data-management/231500503.
VOL 11 NO 1 2013