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Using machine learning to reduce frauds in digital payments

False positives are becoming a bigger problem for banks and businesses. According to KPMG’s 2019 global fraud study, 51% of respondents cited a high number of false positives as a result of current technology solutions, as well as decreased fraud detection efficiencies. We’re seeing an even higher spike in both fraudulent activity and false positives as clients accelerate their migration to digital channels.

There’s no denying that a high number of false positives is a major source of frustration for many banks and businesses. The decision to approve or refuse a transaction is frequently the defining component of a positive customer experience and a profitable card portfolio. This is why it’s critical to identify, assess, and classify your high-risk transactions as accurately and rapidly as possible.

 

How Machine Learning Improves False Positives Detection Accuracy

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Traditional automated fraud detection systems might quickly become overburdened. That’s because most weren’t designed to handle massive amounts of data from various sources in the first place.

The data can then be segregated, fragmented, and incompletely housed across multiple sites, making it difficult to investigate and monitor. Finally, such detection systems are based on antiquated rules and standards that are out of step with today’s payment landscape, which includes omnichannel commerce, mobile payments, contactless payments, and, shortly, IoT payments.

Machine learning and other predictive technologies are frequently used to assess multiple data points inside a single transaction, producing a value that is used to score the transaction depending on its risk level. It’s vital to remember, though, that the quality and speed of your machine learning will be limited by the data you feed it. More accuracy can be used if data is delivered in a more comprehensive and timely manner.

 

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Multiple linkages, networks, and transition points make up a payment transaction. The majority of end-to-end transaction routes have at least four to eight linkages. We’re essentially providing a new spot for a transaction to be hijacked every time we add an API, service, or device. As payment-related fraud assaults get more sophisticated, it’s more critical than ever to take a holistic approach.

Attacks are increasingly being identified by combining various data points – for example, a payment made on a different device, from a different location, and at a different frequency than usual. Part of the secret to success is the capacity to screen every end-to-end transaction, across every link and hop.

 

Let’s Consider an Example

Consider this scenario: you and your partner have a joint bank account. One is a spender, and the other is a saver, like in many happy families. You decide to swap cards for a week to see how things go.

When a spender uses a ‘savers’ card to go on a frequent shopping spree, a rules-based fraud detection system will most likely flag this as an anomaly and deny some large-item in-store transactions. Simultaneously, the saver prefers to shop online because the discounts and rewards are better. Because this is an unusual occurrence (according to the rules-based system), their online transactions may also be incorrectly refused.

On both occasions, a trusted financial institution put the customer in an unpleasant situation. They’ll most likely look for an alternative the following time because they have a variety of other cards and payment options.

 

Key Advantages of using Machine Learning in Fraud Detection

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Machine Learning-based fraud detection is achievable thanks to ML algorithms’ capacity to learn from previous fraud trends and spot them in future transactions. When it comes to the speed with which information is processed, machine learning algorithms appear to be more effective than people. In addition, machine learning algorithms can detect sophisticated fraud qualities that a person cannot.

 

Works Faster

Rule-based fraud prevention systems entail writing precise written rules that “tell” the algorithm which types of actions appear regular and should be allowed, and which should not because they appear suspect. Writing rules, on the other hand, takes a long time. Furthermore, in the business world, manual interaction is so dynamic that things can change dramatically in a matter of days. Machine Learning fraud detection approaches will be useful in learning new patterns in this case.

 

Scalability

The increase of the dataset to which ML algorithms are fitted improves their performance – that is, the more samples of fraudulent actions they are trained on, the better they spot fraud. Rule-based systems are exempt from this principle as long as they do not change. A data science team should also be cognizant of the hazards associated with rapid model scaling; if the model fails to detect fraud or erroneously marks it, it will result in false negatives in the future.

 

Efficient

Machines will be able to take over basic chores and the tedious effort of manual fraud investigation, allowing specialists to focus on higher-level choices.

 

What Type of Data do you need to Reduce False Payments?

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Customer data from cross channels is essential for enhancing fraud analytics and detection systems. Additionally, by pooling data from throughout the cardholder portfolio and connecting it to analytics, you can effectively connect the entire payment ecosystem, rather than just isolated payment channels or client groups.

On that note, the following data types help in identifying false positives in payments –

 

General Customer Data

As part of the KYC procedure, general client information is collected. You should also consider the customer’s account age, existing economic links with other account holders (family) and businesses (employer), as well as the frequency of direct debits.

 

Transactional Data

Transactional data includes all of the usual information about quantities, frequency, and transaction types (CNP, ACH, direct debit, and so on), as well as information about the location.

 

Non-Transactional Data

When combined with other elements, non-transactional client activities may indicate suspicious activity. An updated email or password, for example, along with higher-than-usual expenditure, can indicate account takeover.

 

Location Data

Location data can potentially be used as a proxy for account fraud. Cash withdrawals in large amounts from several locations should set off alarm bells on your end.

 

Behavioural Data

When compared to information provided during KYC, overall behavioral data will help you to assess how one cardholder compares to lookalike prospects in their segment and more effectively discover variations in their behavior.

 

Social & Mobile Data

Both add context to the customer’s daily activities and monetary transactions with persons they know.

 

The Bottomline

Innovations usually take time and effort to adopt and produce results that meet the client’s expectations. While fraud prevention strategies may necessitate infrastructure modifications in the way data is stored, structured, cleansed, and readied for use, it is well worth the investment. The first steps toward using machine learning techniques for fraud detection will be difficult, but their use will grow year after year, resulting in fewer user complaints and more loyalty.

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