Squashing Financial Fraud Faster with the Power of Predictive Analytics
Customer Company Size
Large Corporate
Region
- America
Country
- United States
Product
- Zementis Predictive Analytics
Tech Stack
- Predictive Model Markup Language (PMML)
- Machine Learning
- Artificial Intelligence
Implementation Scale
- Enterprise-wide Deployment
Impact Metrics
- Cost Savings
- Productivity Improvements
- Customer Satisfaction
Technology Category
- Analytics & Modeling - Predictive Analytics
Applicable Industries
- Finance & Insurance
Applicable Functions
- Business Operation
- Sales & Marketing
Use Cases
- Fraud Detection
- Predictive Quality Analytics
- Predictive Replenishment
Services
- Data Science Services
About The Customer
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.
The Challenge
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.”
The Solution
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.
Operational Impact
Quantitative Benefit
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