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Risk Management Intelligence

A global picture, a deep dive, and the bits and pieces of risk management intelligence (available on Amazon)

Why is risk poorly managed?

There're at least three reasons why the risk of payment fraud is poorly managed at e-commerce services.

  • Because data, methods, and software is lacking
  • Because of slow and ineffective decisions
  • And because of limited and inconsistent reports

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 1. Online payments

When things just work

Given the diversity of the regulations, payment methods, intermediaries, and payment scenarios available, how are payments engineered and orchestrated? In this part we review some of the payment methods used to pay for items online. We investigate the different entities that carry out payments online. And we describe the three steps required for online payments.

Part 2. Payment fraud

When things go wrong

Although the online payment process goes right most of the time, in some scenarios it fails. Some reasons why a payment may fail are legitimate, but others are not. In this part, we describe what happens when things go wrong, particularly when payment fraud occurs. We look at why things may go wrong when processing online payments. We discuss how things go wrong (the processes used to commit payment fraud). And we identify the factors influencing payment fraud.

Part 3. Preventing fraud

How to make it work

Here we focus on how to detect, manage, and prevent payment fraud when processing payments online, and we also look at the existing solutions. We first describe the landscape of fraud prevention solutions. Then we provide a bird’s-eye view of the payment fraud prevention process. We dive deep into the bits and pieces of fraud detection. And we describe the feedback loop, i.e., fraud prevention process auditing.

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.

Part 5. The next steps

Towards risk management intelligence

We review the application of corporate financial risk management methods to prevent, detect, and manage fraud prevention for e-commerce services. We review ideal fraud detection systems for e-commerce services. We describe risk classification, the concept of escalation time, and risk ratings. We suggest to use value at risk (VAR) to pilot risk in e-commerce services.