March 4, 2021
80% of European organizations are affected by payment fraud. Merchants are the main victims of the problem, they face losses of around 1.8% of their revenues, and expenses of up to 23% of their operating budget - all spent trying to stop payment fraud.
In an increasingly competitive marketplace (and with rapidly developing ecommerce marketplaces), the rising tide of fraud continues. Licenses to operate can be lost if fraud ratios become too high. Banks can be fined by card providers and regulators, if they don’t control and actively mitigate the prevalence of fraud. Meanwhile, payment Service Providers (PSPs) are in a race to zero on transaction processing fees, and subsequently need to offer their customers auxiliary services. Therefore, the demand for effective fraud prevention tools are high.
Additionally, money laundering by micro-merchants and instant onboarding results in $350bn laundered per year. With the prevalence of easy digital onboarding, merchant fraud and scams are increasing – all at the expense of acquirers. Issuing banks and their customers also face increasingly sophisticated fraud attacks and scams. Money Laundering is also a priority for EU regulators, and to comply, banks have extensive and expensive teams working on these issues – often using outdated techniques and technology.
Payment fraud is insidious, difficult to stop, and can inflict financial harm on any business in minutes. The battle against payment fraud needs to be sophisticated. This starts with pre-emptive strategies to thwart fraud attempts by training AI machine learning models to quickly spot and act on threats. Then, build out this strategy across every selling and service channel a digital business relies on. AI and machine learning can help with that.
The emergence and development of a digital economy has given organizations many new tools that streamline customer relationships. On the one hand, this has helped improve operational overhead, and decrease friction for customers, with corresponding time and money savings. On the other hand, it has opened the door for digital criminals to develop new and more aggressive attacks that exploit these automated processes and consequently compromise the business of companies operating online.
Some of these threats are basic. Simple spam messages or phishing emails that encourage users to share personal information remain a top concern for organizations, but the threat landscape is more complex.
There is a convergence between offline and online fraud, especially in financial services institutions. An example of this is when hackers steal data online, which is then used by criminals on the ground to fraudulently withdraw money from physical bank counters.
Still in this area, the misuse of ATMs (unrecognized movements), identity theft, credit card cloning, copying of magnetic strips of cards and, in particular, cyber fraud with stolen cards, use of malicious programs, and information subtracted from customers (data provided by them in marketing and credit granting actions) on online platforms are the main areas of concern for banks and e-commerce platforms.
Detecting these scams is a game of balance. Customers want their data and transactions to be safe, but they also don't want to be bothered by unnecessary declines of payments (false positives). What decision should a bank or an e-commerce platform make if its fraud detection system flags groups of similar transactions as fraud based on simplistic rules? Do you block all transactions and risk “losing” the customer? Do you not block any at all?
Despite the decrease in false positives (legitimate transactions that are flagged as fraudulent), organizations have to live with the risk of their existence. The solution is to reach an acceptable, as well as manageable proportion of false positives.
Fraud prevention is not, however, a static process. On the contrary, fraud prevention is a continuous cycle that involves monitoring, detection, decisions, occurrence management and learning.
To identify and stop a series of fraudulent attacks quickly and accurately, companies capture and aggregate all types of data available between channels and incorporate it into the analytical process. Likewise, the continuous monitoring of transactions and the application of behavioral analysis that allows decision making in real time and the incorporation of layered security techniques are means that allow payment fraud prevention and transaction fraud detection.
Imagine a human brain and its neural network. It is in this intricate web of neurons and synapses that what we know as ‘intelligence’ was born. What AI does is exactly the same, but instead of synapses, it operates based on a combination of processes and patterns that simulate human intelligence. This includes behavioral patterns such as planning, learning, logic, problem solving, self-correction and appropriate use of available data and information.
Based on three cognitive skills - learning, reasoning, and self-correction, AI programs and algorithms work in cohesion, using data, to create actionable information to perform tasks, solve problems and make forecasts.
“Artificialintelligence would be the ultimate version of Google. The ultimatesearch engine that would understand everything on the web. It wouldunderstand exactly what you wanted, and it would give you the rightthing”.
These words belong to Larry Page (co-founder of Google) and help us understand the role of AI in today's world, and its potential importance in detecting and preventing fraud.
However, there is no single AI system. AI is, in essence, a broad set of principles similar to the process of acquisition, storage, analysis and decision making of a human brain. Principles which are later applied in the development of a multitude of systems, among which, the alarms of fraud via machine learning.
The latter system is based on algorithms that have the ability to process large datasets, continually self-learning, and gaining experience over the passage of time.
Over the past few years, this approach has seen rapid growth and advancement due to the fact that it requires less human involvement and is more cost-effective. These algorithms study and analyze correlations between millions of patterns and behavior over the course of time, and based on that, decisions are made that assist anti-fraud companies and businesses worldwide.
This type of ML is done by using labeled datasets from the past and applying those patterns to new data sets.
This type of ML involves the training of datasets that haven’t been labeled or defined. Instead, the system itself looks for variations and similarities in order to make calculations.
Reinforcement Learning
This can be described as a hybrid of Supervised and Unsupervised machine learning, as it involves using unlabeled datasets that are given automated feedback and revisits once the action has been performed. There might be several trial and error instances using this learning method, as the whole point is to train computer systems using known and unknown past patterns to achieve the desired results.
Online transaction fraud is not a problem exclusive to banks. All products and services purchased through digital systems can be the target of fraud, which creates losses for companies and consumers alike. Therefore, it is no wonder that payment fraud prevention and payment fraud detection systems are evolving rapidly with the help of machine learning techniques.
Systems based on machine learning have several applications. In addition to detecting fraud, they can be used to recognize profiles and make offers to customers, as well as optimizing processes such as the logistics of deliveries or even make predictive analysis based on the patterns already identified. The McKinsey Global Institute's “The Age of Analytics: Competing in a Data-Driven World” study, published in December 2016, identified 120 areas from 12 different industries with great potential to increase productivity with machine learning systems.
Solutions that use machine learning analyze the data provided by each client to create and identify patterns, and to detect profiles that are outside the rule. In the event of fraud, the software will relearn and adjust itself automatically. This type of solution manages to perceive “hidden” relationships between data - i.e. those that would not be identified by people or by systems powered only by definitions created by humans.
Another advantage of this type of technology in comparison to traditional models is its greater assertiveness. When you have a machine that learns from its own evaluations, the refinement is much greater than when you simply define generic rules based on the target audience's profiles. In addition, these solutions are able to adapt more quickly to new fraudulent techniques.
It is also possible to start a system with machine learning by using previously defined data and rules, and then let the software make adjustments from there. Combined with other tools, fraud detection systems with machine learning can even dispense of human analysts to solve suspicious cases, as certain situations can be solved with an SMS confirmation, for example.
Always at risk of payment fraud, banking institutions are among the organizations most interested in this fight.
Generally speaking, bank fraud encompasses credit card fraud, money laundering and mortgage fraud.
In order to prevent and detect these threats, most banks use rule-based systems combined with manual evaluation. Despite the reasonable effectiveness of these systems, they have become increasingly inconsistent in recent years. This is because systems based on manual analysis and evaluation cannot keep up with the sophistication, complexity, and speed of new fraud patterns.
Some banks also use systems built on RDBMS, but these often deliver worse results than rule-based systems.
The need for top financial fraud detection software products led to the development of new anti-fraud systems, based on AI (artificial intelligence) and machine learning.
By emulating the architecture of a human brain, the best AI with machine learning software is not only able to prevent and detect irregularities, but also learn (literally) throughout the process and modify its actions, according to the complexity of the attack.
In this game of cat and mouse between authorities and cybercriminals, the key lies in developing solutions that are as comprehensive as possible and reduce human involvement.
This is what happens with Fraudio and its disruptive patent-pending technology, which can connect every merchant, payment service provider, merchant acquirer and card issuer - of all sizes - to the same centralized artificial intelligence brain, trained with billions of transactions.
All of this happens through an application programming interface (API), fully hosted in the cloud and remotely managed by the Fraudio AI brain. Unlike most similar systems, this payment detection system is connected and ready to provide fraud detection in a few days and has no need for complex integration.
In practical terms, their AI system works in real-time and evaluates a transaction according to the degree of threat: green for a reliable transaction; yellow when it is dubious; and red when it is marked as fraudulent.
For example, after receiving an order from a customer, an e-commerce platform sends the data to the API which, in real time, analyzes it. After a series of data cross matchings (past transactions or customer history, for example) through supervised and unsupervised machine learning techniques, the Fraudio system evaluates the order and assigns a risk rating. After the evaluation, the API sends that fraud risk score to the merchant, who will then decide whether or not to accept the transaction.
When it is the merchant who carries out fraud (using data from stolen cards or not delivering a good or service after payment, for example), Fraudio searches for normal activity and anomalies on the spot and can identify underlying trends that suggest risky behavior merchant - subsequently sending real-time alerts and reporting, when necessary, directly to payment companies.
As mentioned before, money laundering is one of the main problems global financial institutions face. Therefore, besides helping merchants and payment companies to detect payment fraud in commerce, this AI technology with machine learning is also ideal for anti-money-laundering detection.
By making use of algorithms focused on “too good to be true” patterns, this machine learning technology detects complex laundering patterns that are extremely useful in prioritizing investigations to be carried out, saving time and money. In addition to the analytics process, this machine learning technology also sends alerts and reports when it detects suspicious behavior.
Bottom line – payment fraud detection via AI machine learning assists in regulatory compliance and alleviates operational pain points.
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