应用内部模型方法处理实时交易对手风险敞口
公司规模
Large Corporate
地区
- Europe
国家
- Italy
产品
- IBM Algo One
技术栈
- Real-time credit engine
实施规模
- Enterprise-wide Deployment
影响指标
- Cost Savings
- Productivity Improvements
技术
- 分析与建模 - 实时分析
适用行业
- 金融与保险
适用功能
- 商业运营
用例
- 监管合规监控
服务
- 系统集成
关于客户
Intesa Sanpaolo 是一家意大利银行集团，市值为 487 亿欧元（2017 年 7 月），在零售银行、企业银行和财富管理领域处于领先地位。其投资银行部门 Banca IMI 在米兰总部、罗马和意大利办事处以及纽约子公司拥有 800 多名员工，与 70 多个市场的 350 个客户进行股票、债券和衍生品交易。与许多银行一样，Intesa Sanpaolo 正在改造其风险管理基础设施，以提供按需风险分析，从而支持其投资银行部门并提高风险管理流程的效率。
挑战
意大利银行集团 Intesa Sanpaolo 的风险管理基础设施面临挑战。该银行对场外 (OTC) 衍生品的交易决策基于多种建模技术。交易员要么必须运行过于保守的附加产品，要么致电风险团队获取内部模型结果。这个过程既耗时又耗资源。该银行希望通过让交易部门访问其风险管理团队已经在使用的相同经批准的交易对手风险内部模型，帮助前台和中台团队更有效地合作。这将使交易员能够更快地深入了解给定交易对手的总风险敞口，帮助他们在进行交易之前了解限额，并避免超出这些限额的风险。
解决方案
Intesa Sanpaolo 和 IBM 构建了一个解决方案，让交易员可以实时对交易对手风险进行假设分析，从而提供与风险团队模型一致的更准确的风险敞口指标。该银行认为，最好的策略是让其交易部门访问风险管理部门现有的 IBM Algo One 平台。Algo 解决方案已经获得当地监管机构对市场风险和交易对手信用风险的内部模型批准。因此，Algo 的风险指标被视为银行内部优化监管资本要求的“官方数字”。此外，该银行已经实施了 Algo 实时信用引擎，以实时提供交易对手风险指标。为了帮助前台用户采用该工具，项目团队设计了一个直观的界面，不仅可以对潜在交易进行假设分析，还可以提供有用的指标，例如每个交易对手和银行前十大交易对手的当前风险敞口、预期正风险敞口 (EPE) 和潜在未来风险敞口 (PFE)。
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