Squashing Financial Fraud Faster with the Power of Predictive Analytics
公司规模
Large Corporate
地区
- America
国家
- United States
产品
- Zementis Predictive Analytics
技术栈
- Predictive Model Markup Language (PMML)
- Machine Learning
- Artificial Intelligence
实施规模
- Enterprise-wide Deployment
影响指标
- Cost Savings
- Productivity Improvements
- Customer Satisfaction
技术
- 分析与建模 - 预测分析
适用行业
- 金融与保险
适用功能
- 商业运营
- 销售与市场营销
用例
- 欺诈识别
- 质量预测分析
- 补货预测
服务
- 数据科学服务
关于客户
The customer is a leading financial services company with operations spanning commercial and investment banking, private wealth management, consumer finance, and insurance. The company is one of the largest financial institutions in the United States, with additional operations globally. The company's annual revenue exceeds $45 billion, with operating income over $25 billion. The company offers its customers a diverse portfolio of services and management options, which has led to extreme complexity as data volumes have multiplied exponentially.
挑战
The financial services company was facing challenges due to the extreme complexity of data volumes as a result of product flexibility. The company's data science teams were hitting capacity ceilings, leading to external risk from financial fraud such as money laundering and corruption. The company was using a dedicated team of data scientists to create hand-coded fraud models, but with millions of customer accounts, a large service portfolio, and geographically dispersed operations, manual coding became a major liability. The process of converting algorithmic fraud models to operational form dramatically slowed the process of “operationalization.”
解决方案
The company implemented Zementis Predictive Analytics, part of the Software AG Digital Business Platform, to automate the process of operationalizing fraud management models without the need to manually write custom code. This solution combined machine learning, artificial intelligence technologies, and next-generation Internet of Things-type streaming data analytics to provide better risk scoring and fraud detection for mission-critical applications. The initial implementation focused on detecting anomalies in financial transfers, with the goal of identifying money laundering. With strong results, the company adopted Zementis Predictive Analytics more broadly in its cross-channel fraud detection efforts.
运营影响
数量效益
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