Data analytics and data science have grown in importance in many aspects of business in recent years. One of the most important is fraud detection.

Consider how much organizations spend on data analytics technologies. A report from Credence Research says the big data analytics market was valued at $37 billion in 2018 and is expected to reach $105 billion by 2027 at a compound annual growth rate of 12 percent throughout the forecast period.

The rapidly increasing volume and complexity of data are due to growing mobile data traffic, cloud computing traffic, and burgeoning development and adoption of technologies such as artificial intelligence.

The data analytics function involves inspecting, cleansing and modeling data with the aim of finding useful information and trends. These  insights can help inform and support decision-making. Not only does data analysis play a key role in helping executives and others make decisions, but it also helps organizations operate more effectively.

Data science uses scientific methods, processes, algorithms and systems to extract insights from many sources of structured and unstructured data. It employs a variety of methods to understand and analyze actual phenomena with data, using techniques and theories  from many fields, such as mathematics, statistics, science and computer science.

Risk managers should strive to work with people such as the vice president of data analytics, vice president of data science or director of data science, as well as fraud analysts, data analysts and data scientists.

The challenges data analysts and scientists face with regard to fraud mitigation have to do with technical issues rather than fraudulent activity itself.

Among these are a lack of reporting and BI, disparate fraud management tools and a lack of transparency from machine learning models for fraud detection, according to a January 2020 report by research firm Gartner, “How to Create a Payment Fraud Detection Strategy at the Organizational Level.”

SOLUTIONS TO CONSIDER
Data analysts and data scientists can work with risk managers to address challenges and to enhance fraud analysis in a number of ways:

• Leverage consolidated data warehouse and reporting capabilities in a centralized decision platform to ingest outputs from disparate tools and machine learning models.
• Use quality fraud prevention tools that provide a well-balanced approach to combating fraud.
• Ensure the analytics team doesn’t create negative experiences for customers when analyzing transactions for possible fraud.
• Work with data that’s captured every day as part of the normal course of conducting online transactions. This is critical to prevent imposing new requirements on customers and to allow for easy ordering from a web
• Leverage data in new ways to correlate low-risk and trustworthy behaviors, which in many ways is more important than fraud detection in today’s environment.
• Deploy anti-fraud products that enable seeing all the data attributes of the customer together. This allows analysts to ensure they see the correct user over and over, across different transactions.

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