A deep dive in risk management intel

Data, method, and software is lacking

Every day, many e-commerce services struggle when managing risk. They have to figure out ways to prevent risk themselves. They lack data, methods, and software to make the right decisions. This wastes so much time and leads to bad decisions.

Processes are slow and ineffective

Too often, risk operation analysts have to resort to intuition because they don’t have access to the information they need. Frequently, executives have to explicitly demand audit reports by verbalizing their need, waiting for the reports to materialize, and making decisions. This is slow and ineffective.

The big picture is being missed

Analysts spend a lot of time making audit reports and showing information, but what is the basis of these reports? How much data are they based on? What can we conclude? Are the conclusions justified? Often, reports are developed inconsistently over time and based on limited data samples. They miss the big picture and make it worse.

Who is to blame?

Should we blame the analysts who juggle different IT systems and need to justify their work to upper management? Maybe the wrong metrics are used to measure productivity.
We also blame the fraud detection software, but that is just one component. Usually, it's possible to build something on top of what exists.

Risk management under-valued

The expected benefits of risk management are often underestimated, probably because it’s new for most businesses—usually, it's reserved for financial institutions or large companies.
To add to the problem, although everyone knows what a chargeback or fraud is, how to call a customer to confirm an order, or what a blacklist is, few know what a risk is, how it's calculated, and how to translate it back to the e-commerce service—this book provides answers to these questions.

Who is the book for?

The process components and methods are relevant to all levels of seniority in the business. They’re not just for those who carry out payment verifications or make audit reports.
They're also relevant to those who need to understand the terms being used and a vision of the whole process, such as executives, lawyers, or investors.

What's in the book

Outline of the Book

Part 4. Quantitative methods

Big data and statistical learning to prevent fraud

Here we focus on the application of big data, artificial intelligence (AI), and machine learning (ML) to boost the prevention, detection, and management of payment fraud. We review the preliminary conditions to carry out data analyses for fraud prevention. Second, we describe the essential methods used to measure risk and test hypotheses. Finally, we discuss how it is possible to teach machines to predict a concept like payment fraud.