Today, with approximately 17,000 employees, 700 branches across Italy, an international presence to support its corporate clients, and a ranking within the top 50 of European banks in terms of market capitalization, Banca Nazionale del Lavoro (BNL) is well ahead of the majority of its competitors when it comes to risk management.
BNL's risk management practice took a first leap forward in the mid-1990s when the bank decided to organize its operations into strategic business areas supported by common risk management tools, processes and platforms with the ultimate goal of optimizing capital consumption and maximizing returns.
More recently, BNL's risk management practice has become an independent entity within the organization, with a strategic value of its own. Its mandate is to improve the quality of the bank's assets and manage the strict control of value and volatility in the bank's financial results. Additionally, it is responsible for establishing risk-adjusted pricing methodologies and involved in supporting BNL's implementation of new IASB standards in 2005.
Although BNL counts itself as one of the earlier banks to champion integrated, enterprise-wide risk management, it purposefully held back before deciding whether to adopt a "buy" or "build" strategy when it came to a software system. It watched some competitors stumble over technological hurdles, building systems in-house only to find them inadequate in addressing their needs going forward, and having to start again from scratch. "We wanted to maximize our investment without making mistakes. We chose to buy from a third party rather than build our own system as we had seen some important banks achieve poor results when they did that," says Serenella Bianchi, who is in charge of market risk, counterparty risk, operational risk, ALM and capital monitoring at BNL.
The decision paid off. With risk management software experts Algorithmics, BNL found a partner that was able to transform its vision into a reality. The most important step was to implement an integrated system capable of measuring all categories of risk: market, credit, asset liability and operational. In other words, to develop a single platform to measure, assess and mitigate risk, and ultimately to support the capital management process. Using a single platform with consistent analytics, data and assumptions, BNL would be able to improve risk control and, at the same time, take advantage of the integration of different categories of risk to achieve capital savings.
The implementation began with market risk in 1997. At the time, financial markets were reeling from the Asian crisis and the Mexican peso disaster. In addition, Russia was heading towards a financial crisis of its own that would see it default on its debt. Improving the management of market risk was considered a priority in order to prevent an undesired impact on the balance sheet. Moreover, risk management theory was much more advanced for market risk than for other types of risk. "Market risk as a discipline was more mature; it was too early to phase in credit risk and operational risk management at that time," says Bianchi.
The bank carried out a detailed assessment of the different software products for market risk available at the time. A working group made up of risk managers, IT professionals and finance managers investigated the leading systems and settled on Algo Market from Algorithmics. "We had a clear vision in our minds that our ideal risk management partner was supposed to have a strong leaning towards research and development," says Bianchi.
BNL wanted a software partner that was able to offer a single, unified platform and scalable architecture that the bank would be able to use over the long haul, and that would allow it to either add or integrate different or new types of risk when required.
The bank is sure it made the right choice. With Algo Market, BNL has, since 2000, been able to measure all its market risks for every asset class and product type quickly, efficiently and accurately, and to use the results of its risk measurement procedures in the control and risk mitigation processes. More specifically, the bank uses Algo Market to calculate Value-at-Risk (VaR) using Monte Carlo simulation involving 5,000 scenarios for around 100,000 positions on a daily basis. It runs Monte Carlo scenarios for approximately 520 risk factors to calculate various types of VaR, including net, interest rate, forex, equity and implied volatility.
Through the intranet, Algo Market also supplies the organization with a set of stress tests involving sensitivity analysis, historical scenarios and conditional scenarios, and other attributes-such as marginal VaR and best hedge position-that allow better market risk management and decision-making overall. In addition, since 2002, BNL has defined and implemented a framework of VaR-based operating limits approved by the bank's board.
What is more, Algo Market's flexibility means that BNL can customize the system to suit its individual needs as the bank grows and expands into new products, and as markets become more sophisticated. For example, BNL has recently begun to model exotic options embedded in equity-linked notes and has created more innovative pricing solutions for interest rate derivatives, where it has introduced a new pricing methodology based on volatility skew.
BNL also now has the ability to experiment with different pricing methodologies in order to find the best pricing solutions for a wide range of products, enabling the bank to become more competitive in the market.
In 2000, BNL found that off-balance sheet instruments, especially derivatives, were one of its fastest growing areas of activity, and the issue of counterparty risk had become imperative. With market risk now under control, BNL decided that counterparty risk would be one of the first priorities in the broad arena of credit risk management, and began implementing Algo Credit the following year. The solution leverages a common architecture and platform with Algo Market, so implementation was speedy and uncomplicated.
Not only could the bank immediately start to measure exposures accurately, it also began to take advantage of the synergies between the different categories of risk.
BNL runs Monte Carlo simulations involving 1,000 scenarios for each of six steps and scenario analyses to compute expected and unexpected loss for each counterparty. It has now integrated the processes of measuring and controlling counterparty risk by product and business unit, and is using these results to mitigate its credit risks through netting and collateral management procedures.
By 2005, BNL will be able to set operating limits for its trading book based on Algo Credit's 4.0 architecture. Once Algo Credit was in place, BNL quickly discovered that its existing limits were inaccurate in a number of cases. "We found that for our short-term derivatives, for example, the old system was too conservative, so we gained more commercial space when we transferred to the new way of measuring risk," says Bianchi. In other instances, the old system underestimated risk, meaning that the bank was potentially leaving itself open to credit losses. Now with a fully back-tested counterparty risk model in use, BNL is confident that its limits are more sound and accurate.
BNL has also tackled credit risk in the banking book. Algo Credit's portfolio management functionality enables the bank to measure and control credit VaR, expected and unexpected losses, as well as shortfalls. The results are mainly used for risk adjusted pricing policy to protect margins as the bank has targeted an important reduction of the expected default frequency. "In the future," Bianchi says, "there will also be benefits from portfolio diversification, from the introduction of the limits system and in the integration with the trading book."
BNL is also currently implementing Algorithmics' turnkey market and trading system to calculate specific risk. Once this solution goes live in the spring of 2005, it will complete the risk integration project for the whole of BNL's trading book. All market and credit risks will be measured by portfolio, enabling the bank to allocate and sub-allocate capital, achieve optimal portfolio diversification, and realize better returns. "I am not aware of other Italian banks that have managed to completely integrate credit risk and market risk in the trading book-it would be a first! Step by step, we are getting closer to the end goal of being able to diversify the whole bank," says Bianchi.
Though already well ahead of most of its peers in terms of integrating risk, BNL is not resting on its laurels and plans to overhaul its asset and liability management (ALM) operations. The bank already has an ALM system in place, but BNL wants to adopt Algo ALM's more modern framework, one that complies with mark-to-market hedge accounting after the introduction of IAS 39 and one that can cope with modern commercial products containing embedded optionality. Bianchi also wants to take advantage of a shared common platform with BNL's market risk system. Algo ALM can take clean, tested market risk data quickly and efficiently, and eliminate operational risk caused by input errors. The solution also allows BNL to manage the trade-off between earnings-at-risk and VaR on the same basis, using the common framework. BNL expects the ALM implementation to be complete by the end of 2005.
BNL has introduced its risk management systems and processes across its three risk-taking centres: Rome, London and New York. Around 100 staff use the online market risk management reports, both on the trading desks and in the control function, while at least another 200 users are planned for counterparty risk management reports. The implementation of all these solutions can be considered a success story. "It has helped that we have had the right people around us," says Bianchi.
In so doing, BNL has nurtured a new culture of risk awareness, from top management through to the actual daily users of the risk systems. This culture is advanced by monthly global risk reporting to the risk committee and by weekly financial reporting, including stress test reports, to the finance committee. Risk management processes and procedures have the blessing of the board, and the staff has become more aware and trained to measure and manage risk at the business unit level. In BNL's wholesale banking business, for example, which uses Algo Market for trading and compliance with operating limits, the flexibility of the system's drill-down function means that it is very easy for the chief dealers to switch positions to markets that are performing better or carry less risk. "They are more aware of the risk/return ratios of their portfolios, so they can perform better," says Bianchi.
Integrating its risk systems and building a risk culture across the enterprise has also assisted BNL's preparations for Basel II. The bank plans to use its internal models for market and credit risk to measure its capital requirements within Basel II. In fact, the complete, integrated firm-wide risk management system that BNL is developing takes the bank beyond the requirements of Basel II to a point where the bank is in a position to realize additional capital benefits by correlating different types of risk.
BNL's risk management systems and procedures are competitive with those of most of its peers. The next stage will be to deal with operational risk management. The bank already has robust procedures in place for VaR calculation and has collected roughly four years of loss data. For now, though, BNL is satisfied with the results it has achieved so far. It has seen substantial cost savings and an overall reduction in risk.
Bianchi concludes, "All of our managers and risk professionals involved in this 'odyssey' are happy because they have all seen concrete results. The conceptual design has been clear from the beginning. Although strategic issues in the bank can change, it has generated a 'competence centre' that BNL can use for many years to come."
The BNL Case Study is one in a series of Algorithmics' case studies. For information about Algorithmics and to view other client case studies, visit www.algorithmics.com.Algorithmics
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