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 - Machine Learning Analytics & Modeling - Predictive Analytics Analytics & Modeling - Real Time Analytics
Applicable Industries	Finance & Insurance
Applicable Functions	Business Operation Sales & Marketing
Use Cases	Fraud Detection Predictive Quality Analytics Predictive Replenishment
Services	Data Science Services
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.” Read More
About 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. Read More
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. Read More Log in to view content
Contents

Technology Category

Analytics & Modeling - Machine Learning

Analytics & Modeling - Predictive Analytics

Analytics & Modeling - Real Time Analytics

Applicable Industries

Finance & Insurance

Applicable Functions

Business Operation

Sales & Marketing

Use Cases

Fraud Detection

Predictive Quality Analytics

Predictive Replenishment

Services

Data Science Services

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.”

About 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.

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.

Impact #1	The company was able to reduce decision-making time from months to days.
Impact #2	The company was able to lower costs.
Impact #3	The company was able to cut its overall risk profile.

Impact #1

The company was able to reduce decision-making time from months to days.

Impact #2

The company was able to lower costs.

Impact #3

The company was able to cut its overall risk profile.

Benefit #1	Reduced decision-making time from months to days.
Benefit #2	Lowered costs.
Benefit #3	Cut overall risk profile.

Benefit #1

Reduced decision-making time from months to days.

Benefit #2

Lowered costs.

Benefit #3

Cut overall risk profile.

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Overview

Squashing Financial Fraud Faster with the Power of Predictive Analytics

Operational Impact

Quantitative Benefit