Software AG Case Studies Squashing Financial Fraud Faster with the Power of Predictive Analytics

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	Squashing Financial Fraud Faster with the Power of Predictive Analytics Software AG

Squashing Financial Fraud Faster with the Power of Predictive Analytics

Software AG

Technology Category	Analytics & Modeling - Predictive Analytics
Applicable Industries	Finance & Insurance
Applicable Functions	Business Operation
Use Cases	Fraud Detection Predictive Quality Analytics
Services	Data Science Services
Challenge	The customer, a leading global financial services company, was facing challenges due to its diverse portfolio of services and management options. The complexity of analytic models increased as the company grew, and the internal data science and IT teams were reaching their capacity. This increased the underlying risk of the company’s business model and threatened to remove a competitive advantage. The company was using a dedicated team of data scientists creating hand-coded fraud models. However, with millions of customer accounts, a large service portfolio, new product launches, and geographically dispersed operations, manual coding became a major liability. The process of converting algorithmic fraud models to “production ready” dramatically slowed the process of integrating them into the operational business processes. Read More
About Customer	The customer is a leading global financial services company providing data and analytics solutions to businesses, consumers, and governments. Its solutions range from all aspects of credit modeling and scoring, to fraud detection and prevention, to identity management and verification services. The company offers customers a diverse portfolio of services and management options. With millions of customer accounts, a growing and large service portfolio, new product launches, and geographically dispersed operations, the company was facing challenges in managing the complexity of its operations. The company's annual revenue exceeds $3 billion a year with an operating income of more than $2 billion. Read More
Solution	The company implemented Zementis Predictive Analytics, part of the Software AG Digital Business Platform. This solution combined machine learning, artificial intelligence technologies, and next-generation Internet of Things-type streaming data analytics to provide automated predictive models for better risk scoring and fraud detection. 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. The solution provided its functionality as a plug-in tool for other leading analytics and data warehouse platforms, using the Predictive Model Markup Language (PMML) industry standard. Read More Log in to view content
Contents

Technology Category

Analytics & Modeling - Predictive Analytics

Applicable Industries

Finance & Insurance

Applicable Functions

Business Operation

Use Cases

Fraud Detection

Predictive Quality Analytics

Services

Data Science Services

Challenge

The customer, a leading global financial services company, was facing challenges due to its diverse portfolio of services and management options. The complexity of analytic models increased as the company grew, and the internal data science and IT teams were reaching their capacity. This increased the underlying risk of the company’s business model and threatened to remove a competitive advantage. The company was using a dedicated team of data scientists creating hand-coded fraud models. However, with millions of customer accounts, a large service portfolio, new product launches, and geographically dispersed operations, manual coding became a major liability. The process of converting algorithmic fraud models to “production ready” dramatically slowed the process of integrating them into the operational business processes.

About Customer

The customer is a leading global financial services company providing data and analytics solutions to businesses, consumers, and governments. Its solutions range from all aspects of credit modeling and scoring, to fraud detection and prevention, to identity management and verification services. The company offers customers a diverse portfolio of services and management options. With millions of customer accounts, a growing and large service portfolio, new product launches, and geographically dispersed operations, the company was facing challenges in managing the complexity of its operations. The company's annual revenue exceeds $3 billion a year with an operating income of more than $2 billion.

Solution

The company implemented Zementis Predictive Analytics, part of the Software AG Digital Business Platform. This solution combined machine learning, artificial intelligence technologies, and next-generation Internet of Things-type streaming data analytics to provide automated predictive models for better risk scoring and fraud detection. 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. The solution provided its functionality as a plug-in tool for other leading analytics and data warehouse platforms, using the Predictive Model Markup Language (PMML) industry standard.

Impact #1	The company was able to automate the process of operationalizing fraud management models without the need to manually write custom code.
Impact #2	The company's data scientists were able to focus on modeling fraud rather than fixing mistakes.
Impact #3	The company was able to use advanced predictive models to detect and analyze customer behavior, market dynamics, security risks, and other variables necessary for quick and precise fraud detection.

Impact #1

The company was able to automate the process of operationalizing fraud management models without the need to manually write custom code.

Impact #2

The company's data scientists were able to focus on modeling fraud rather than fixing mistakes.

Impact #3

The company was able to use advanced predictive models to detect and analyze customer behavior, market dynamics, security risks, and other variables necessary for quick and precise fraud detection.

Benefit #1	Analytics-based decisions that previously required months of preliminary analysis now required only days, sometimes even hours, enabling near real-time decision making.

Benefit #1

Analytics-based decisions that previously required months of preliminary analysis now required only days, sometimes even hours, enabling near real-time decision making.

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Overview

Squashing Financial Fraud Faster with the Power of Predictive Analytics

Operational Impact

Quantitative Benefit