Guide to Using Data Analytics to Prevent Financial Fraud

Risk assessment is crucial for any enterprise that extends credit to customers. Commonly known as credit scoring, the process helps lenders make confident, informed decisions on whether prospective customers will honor their debt. Credit scoring is typically associated with the banking and financial service sectors, but is required across a wide array of businesses, including telecoms, retail, and insurance. In most cases, credit scoring isn’t just a business tool, it’s a regulatory necessity. And credit scoring is a vast industry. In the U.S. alone, recent consumer debt valuations hover over $14 trillion. Credit scoring is a complex task that involves wrangling a diverse range and large volume of data. Based on predictive modeling, the use of artificial intelligence (AI) and machine learning (ML) is well established and widespread. And because of the data burden, credit risk firms were some of the earliest organizations to adopt the technology. As such, the credit risk sector can claim to be a pioneer in AI and ML utilization.

Data drives vital elements of our society, and the ability to capture, interpret, and leverage critical data is one of Altair’s core differentiators. While Altair’s data analytics tools are applied to complex problems involving manufacturing efficiency, product design, process automation, and securities trading, they’re also useful in a variety of more common business intelligence applications, too. Explore how machine learning drives EV adoption insights - click here. An Altair team undertook a project utilizing Altair Knowledge Studio® machine learning (ML) software and Altair Panopticon™ data visualization tools to investigate a newsworthy topic of interest today: the adoption level of electric vehicles, including both BEVs and PHEVs, in the United States at the county level. This guide explains the team’s findings and the process they used to arrive at their conclusions.

Any industry that produces bulk quantities of goods such as pharmaceuticals, food, chemicals, or cosmetics, is seeking to produce these products consistently while reducing cost factors like waste and down time. Due to the nature of process manufacturing, multiple ingredients are combined to be mixed, coated, or sorted, so understanding the behavior of these processes is of paramount importance for manufacturers. Through the use of simulation modeling and Smart Manufacturing principals, manufacturers are now able to optimize these processes, leading to greater productivity and profitability.

By extracting real value from their data, manufacturers can make accurate predictions about component life, replacement requirements, energy efficiency, utilization, and other factors that have direct impacts on production capacity, throughput, quality, sales, customer acceptance, and overall efficiency.

Whether you call it data preparation, mining, extracting, cleaning, joining, blending, or masking, it’s all data transformation. This guide will help you assess your requirements and develop an implementation plan to increase efficiency and reduce errors in your data transformation processes. Experience no-code, automated data transformation: Try Altair Monarch today, for free.