Building an ALM system for the Insurance Industry

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1 Building an ALM system for the Insurance Industry Marc Hansenne, Koen Van Huffel SOLID FINANCE Solutions (A division of SOLID Partners NV) Abstract ALM systems are having a breakthrough in the insurance sector. This is a response to factors as an increased variability of financial markets coupled with new insurance products and a fierce competition with the banking sector. In contrast with the banking sector, where ALM is since years a known technique, insurance companies have to build this know how. Moreover, the system and the organisation to make an ALM system operational must be implemented. We will give a brief discussion of what ALM is, and which components are needed, in order to guarantee a flexible and open system. We will see that ALM fits in a RISK management approach that monitors issues as performance, risk, solvability and liquidity. Given the major components of an ALM system (data input from a data warehouse, front-end, calculation box, report generator) we demonstrate why the SAS System offers an ideal solution as a platform to build an ALM system for your organisation. Introduction In today s more and more aggressive markets it is of primary importance to know whether the products sold still give a positive contribution to the total value of the enterprise. The economical impact of an insurance contract is by its nature however only visible after years. The long term character of insurance contracts demands a thorough follow up of profit and losses for each individual product. Asset and Liability management is the management of the composition of the balance sheet with the aim to realise the company goals within given risk boundaries. The insurance industry, in contrast with the banking sector where, ALM is since years an accepted technique, starts to implement ALM systems for mid and long term use. Many insurers opt to build a tailor made system, since there are almost no integrated software packages available on the market that offer solutions for insurance products. Building such a complex system as an ALM system offers challenges and opportunities for the entire company. We will briefly give an overview of what ALM is and why an insurance company needs it. We will see that ALM manages interest rate risk. An overview of the application components of an ALM system and a number of practical issues that one encounters while developing such a system will end this paper.

2 What is ALM An Asset and Liability Management system is an element of a risk management approach with the purpose of managing assets and liabilities influenced by the combination of several risk factors, and issues as rentability, liquidity and solvability. It simulates cash flows, profit &loss and balance sheets under multiple interest rate and exchange rate scenarios. Some systems enable to model strategic decisions. Stress testing with extreme scenarios can deliver information about the companies liquidity management system. An ALM system offers the possibility to evaluate multiple risk/return trade offs and the effect of strategies on the result of the company. Per contract (or per group of contracts when working with aggregates) one can calculate the different cash flows in time. All these cash flows of the different contracts can be summed up per period and the effects on the P&L and balance sheet of each individual contract can be simulated. Figure 1 describes ALM as the projection of future earnings under assumed market scenarios, where earnings are defined as earnings reported in the financial statements. In many organisations are the bulk of activities reported on a accrual basis. Transactions are booked at historical cost +/- accruals, and only a limited number of trading items are constantly marked-to-market. Inventory of financial transactions Accrual items Trading items Income Simulation Scenarios Projected Income Statement Figure 1: ALM as the projection of future earnings An ALM system requires the data from almost every transactional and accounting system of the company. These data however have to be completed in order to calculate future cash flows for the items on both sides of the balance sheet and for the interest rate sensitive non balance sheet items. Why do we need an ALM system? It is of primary importance in today s fast moving markets to respond promptly to new business opportunities and challenges. As for every other market it is of the outermost importance to adapt the product portfolio as close as possible to the fast changing demand. An ALM system gives better insight in operation and results of the

3 different profit centers. It gives insight in the understanding, the measurement and the management of interest rate risks. The economical impact of an insurance contract is only visible after years. The long term character of insurance contracts causes the standard accountancy performance measuring methods, based on performance indicators, to give a good but not complete view of the evolution of an insurance portfolio value. Lacking this view means lacking an accurate view on the insurance company s value. Alternative techniques as profit testing and embedded value calculation add also the necessary pieces of the jigsaw. Besides the already mentioned competition between insurers and the fierce competition with the banking sector, one can generally see the increased variability of the financial markets, new insurance products, and the need to manage the solvability risk, as the most important issues to decide to buy or build an asset and liability management system. Risk elements The insurance industry faces several typical risk factors as the increased degree of sophistication of services (insurance technical risk), risks related to reglementation (regulatory risk), financial risks and operational risks. The financial risk elements can be devided in interest rate risk, country risk, inflation risk, credit risk, risk on asset investments, inflation risk and liquidity risk. ALM focuses mainly on the measurement and control of the interest rate risk in order to maximise company result within given risk limits, desired growth and rentability. Multiple interest rate scenarios can be used to simulate possible future market trends. Reusing experience In recent years insurance companies invested in product pricing models for their different products. The concept of profit testing can be seen as analogous to the base calculation routines of an ALM system since it models the cash flows of each policy. Even more, Embedded Value techniques (calculation of the net worth of a insurance portfolio) have many aspects in common with an ALM system since they combine profit testing with a number of hypotheses as lapse rates, interest rates and mortality. Having a profit testing system in place will thus shorten the development cycle significantly. Analysis of Interest rate risk An ALM system analyses the interest rate sensitivity of the balance sheet. Many of the factors that are important in the banking industry are also important in the insurance industry: Embedded and contractual options Contracts with a start date well in the future Interest rate guarantee Interest rate sensitive behaviour of the client Three techniques are used for measurement of the interest rate risk: interest rate gap analysis, duration analysis and simulation.

4 1. Interest rate gap analysis Time buckets are defined and the components of both sides of the balance sheet are classified according to the time until maturity. The accumulated capital difference of the sum for each time bucket of the active side and the sum of each time bucket of the passive side of the balance sheet is defined as the cumulative gap. The interest rate sensitivity is then given by the cumulative gap times the rate change. It is a simple technique, with some drawbacks (some products can not be divided in time buckets, options in products are not taken into account, book values not market values) 2. Duration analysis A duration is the weigth average of the times until maturity, with the present value of the cash flows as weigth factors. It demonstrates the change in value of the different balance items when an interest rate change occurs. For the Macaulay duration are present value interest factors calculated with the yield until maturity as interest rate. The Fisher-Weil duration is however closer to reality since it uses the spot rate to calculate the PVIF with. The most important advantages of the technique are: interest rate risk monitored by one figure, market values instead of book values. The most important drawback is that this technique is not suitable for shares and other products without fixed rate periods. 3. Simulation analysis Profit & loss simulation under multiple interest rate, exchange rate and portfolio scenarios with the aim to define the ideal balance structure of the company. The advantages of this technique are that it is directed towards the future and it is dynamic. When products lapse, reinvestment can be modelled or new business can enter the system. Systems can be made multi currency or multi company. Disadvantages are the risk for information overkill. The reporting component of the application is very important for visualisation of the results. Application components An ALM system should consist of following components: Product data load module for input product and accounting data: data would be ideally obtained from the company data warehouse with all the implicit properties (clean, validated). Batch job scheduling system Input parameters load module (interest rate scenarios, exchange rate scenarios,..): can be imported from the data warehouse and from simulation programs. Front end (on client): input and control parameters and batch system. Calculation /Simulation routines (testing on client, executing in batch on server) Interest rate scenario independent and dependent New business Report generator (HTML, SAS/EIS - MDDB) for Scenarios Parameters Cash flows Market values Durations

5 Gap data Historical comparisons Comparison with balance sheet and P&L Figure 2 describes the main system components: scenarios Product data (warehouse) front end economica R&D strategies simulations scenarios reporting Figure 2: ALM appliction components Practical issues Data Consistency is critical: The company data warehouse should be in place as product data source (clean, validated, ). It avoids having to spent valuable time on cleaning and validating input data. Availability of product data is critical. High level sponsorship of the ALM project ensures that time limits are met by other departments. Data Modelling : Normalisation of the parameter data base is essential to build a metadata driven system without hardcoding. Start small think big : implement gradually product by product and test thorough for each new component or product Hardware :Do not underestimate performance issues for the batch system (unix server/mainframe) and for the client. Standardisation (environment, directories, working practice,..) will save time in the future. It should be easy to switch between complete product files, aggregates and very little test data. Testing is an important time consuming factor. Being able to switch at all times reduces testing times significantly.

6 Human Resources: Take debugging into account (the more products you model, the more complex it gets) User requirements should be properly defined. Having to add additional functions afterwards causes important delays System management takes time and should be taken into account in the project planning Product knowledge is essential. The necessary staff with thorough product knowledge should be readily available. ALM is all about modelling cash flows in the different life phases of a contract. If the modelling is not correct, the results will be worse. Some necessary data fields are not available in the transaction systems or in the data warehouse. Modelling exeptions take a lot of time. Each case should be evaluated whether Software Requirements SAS Software is the best of breed software package that is necessary to build a complex ALM system. Building such a system requires a lot of properties that are available in The SAS System : - The large number of build in functions shorten the development cycle. - The multi platform aspect enables testing on the PC and executing on powerful UNIX boxes or mainframes. - Outstanding data import and export facilities. - Extended reporting capabilities (from PROC PRINT to EIS/MDDB and Hybrid OLAP). Conclusion The ALM theory is briefly discussed after an introduction about the reason to custom build an ALM system in an insurance company. Why an ALM system is needed and how it can integrate with existing profit testing or embedded value systems is pointed out. It is stressed that ALM is all about the analysis of interest rate risk. Some factors relevant for the insurance industry are discussed, together with ALM techniques as interest rate gap analysis, duration analysis and simulation. More practically are the different components of an ALM system and some practical issues described. Last but certainly not least we discussed the advantages of building an ALM system with SAS software. Bibliography - Dirk Van Berlaer (Prof.Dr) : rentability of insurance products (course notes Free University Brussels 1993) - Marc Hansenne : How Data Warehousing and EIS techniques can contribute in company wide consistent performance measuring of an insurance company (SAS Blues proceedings 1996)

7 - Marc Hansenne : Waardemeting van een levensverzekeringsportefeuille (Thesis Lic Actuarial Sciences - Free University Brussels -1994) - Jacques Longetstaey (JP Morgan, New York): Var, RiskMetrics and market risk methodology (Bank- en Financiewezen Jan Belgische Vereniging van banken (Belgium Banking Association) Authors Marc Hansenne (actuary) and Koen Van Huffel (MBA) SOLID Finance Solutions (A division of SOLID Partners NV) Universitair Bedrijvencentrum Antwerpen Drie Eikenstraat 661 Edegem B-2650 Belgium tel +00/32/3/ fax +00/32/3/ marc.hansenne solidpartners.be About SOLID FINANCE Solutions (A SOLID Partners division ) SOLID Finance Solutions is the Financial Business and Management Information consultancy branch of SOLID Partners NV. Where the mother company focuses on data warehousing and Information Delivery solutions in SAS, SOLID Finance Solutions offers Business Consultancy services in the following areas: Management Information Systems (Financial and Non Financial) Performance Measurement Risk Management Activity Based Costing Activity Based Management Treasury Activities