Fraud has been glamorized in the many blockbuster movies and series available today. However, the stories of con artists and their complicated, high-action crimes are inspired by a troubling reality.
Though ideal for use cases, for machine learning and artificial intelligence (AI), fraud is still creating negative waves throughout all commerce industries. This constant and underlying danger for commercial businesses can cost millions of dollars in processing scams, as well as causing inconvenience for customers and significant damage a company’s reputation.
Present-day Fraud
Computers have been attached to external networks for a long time, and since then, businesses have sought after effective fraud solutions. Each attack is identified, and a solution is integrated to stop it from happening again.
As technology has drastically evolved over the past decade, so have the tactics to exploit it, such as those used in Card-Not-Present (CNP) fraud. Though it may seem good that technology evolves quickly to be more robust and hard to hack, this also applies to fraud tactics. And so, the never-ending race of fraudsters versus network firewalls ensues.
Using techniques such as SQL injection, hackers exploit all weaknesses within a webserver and the website codebase. To protect websites from these attacks, WAFs were created to block access to request patterns that match already known attacks.
To summarize: the traditional approaches to stopping fraud (which involved rule-based decision-making engines) no longer offer the level of protection necessary for effective payment fraud prevention. Today’s tactics for committing fraud continue to adapt, requiring AI and machine learning algorithms to identify and stop them.
In this blog, we have delved into how Custom Business Logic and AI can be utilized to prevent payment processing fraud.
Understanding Payment Fraud Prevention
Cybercriminals are always looking for weak spots in a business’s security, and there are many different ways that a fraudster can attack a business:
- The more recent efforts are to hone in on soft attacks to a system.
- Business logic attacks involve finding ways to steal information or cause damage.
- Social engineering attacks involve targeting human operators to access the system by using personal information gleaned from social media.
Combatting these newer attacks continues to be a challenge. Many cybersecurity strategies involve rule-based access and protection. – A system that rejects transactions that do not follow a set of protocols. However, these rules do not prepare the system to handle new challenges.
Setting up rules for an enterprise-level financial institution becomes complicated. The security plan breaks customers into different groups, and each group may need a unique set of rules. This requires a new type of attack that requires spending time examining and revising the rules for each group.
A rule-based approach can also be frustrating for customers. Rules that are too stringent lead to false positives and rejected transactions. Or, if the system fails and customers fall victim to a fraudulent transaction, they will lose faith in the business.
This is all starting to sound quite glum. But don’t let the fear of fraud swallow you whole just yet. Because now, it is time to start talking about the benefits of using machine learning. (Which, in this instance, is how its analytical tactics ‘learn’ fraud patterns in databases without being constantly guided by human analysts.)
Think of machine learning as the most effective way of building analytical models and AI as the way of using these machine learning models. The AI within this refers to a broader application of specific analytics to accomplish tasks such as fraudulent tactics.
With machine learning, data analysts can efficiently and more accurately determine which transactions are more likely to be fraudulent—allowing for automated discovery of what fraudulent and non-fraudulent patterns (across large volumes of streaming transactions) look like and how they differ.
To prevent this technique from becoming outdated and inefficient, machine learning can adapt over time to new, previously unseen fraud tactics. Such a complex process involves cross-referencing patterns in its collected data, to continually improve its ability to distinguish between normal and fraudulent behaviors.
This process requires finesse and a professional understanding. If the domain is not understood or fraud-specific data sciences are not implemented correctly, then a machine can easily be employed with machine learning that learns the wrong things. This is a costly mistake that is hard to right once made.
Just as people can learn bad habits, so can machines. This is why utilizing a custom developer is crucial in making sure your business starts on the right foot from the get-go.
Custom Business Logic, AI Technology, and Fraud Prevention
Most business transactions follow a predictable pattern. An order originates from a customer’s location and, most of the time, from the same IP address. As a customer places more orders, the pattern becomes more detailed. Information about the size and cost of the person’s average order is noted as part of their normal behavior.
Business logic protection involves teaching machines to learn the fraud patterns and recognize their unusual actions. Some of the signs that might trigger suspicion are:
- Logging in from a new location
- Several accounts logging in from the same IP address
- Searching through the site faster than normal
- Unexpected high-cost purchases
- Shipping to an unrelated address
AI fraud prevention will respond according to its programmed logic. Unusual activity may trigger an alert, ask for secondary verification, block a transaction, or throw a suspected unauthorized user off the site. Incorporating a custom business logic solution will help enable real-time responses to attacks and minimize the impact of fraud on customer experiences (UX).
The Benefits of Paying for AI Fraud Detection
New fraudulent schemes are constantly in the works. Organizations that want to maintain trust with their customers should examine their current strategy and see if they benefit from artificial intelligence fraud prevention integration.
The top 4 features that can be utilized from machine learning, and AI, solutions are:
- Learning Patterns and Detecting Anomalies
Developers have two ways of training an artificial intelligence fraud prevention resource: supervised and unsupervised:
The supervised procedure involves showing the AI system both normal and fraud-related transaction patterns. This model works well for stopping common fraud tactics.
Whereas the unsupervised procedure requires training the AI to look for unusual activities to build its own understanding of unusual fraudulent behavior and usual customer behavior. Remember that AI integrations with an unsupervised model have a better chance of catching novel fraud strategies.
The most significant benefit of utilizing AI integration is its ability to uncover behavior anomalies on a platform quickly. For instance, the average user will log into their account, browse, and slowly add things to their cart.
But a user account that has been compromised will log in and immediately start adding multiple things to the cart. Or they may try to ship items to an address not previously used by the actual user’s account. They may even attempt to implement a custom payment method that has never been used before.
With AI, these differences in behavior will be monitored and picked up on. Being able to discern which types of behavior are suspicious, the AI fraud prevention feature will save on a lot of time, money, and customer distress.
- Custom Settings for Risk Thresholds
Financial institutions and e-commerce sites bear a certain amount of fraud risk by working in the digital world. A development team can design intelligent payment fraud prevention tools that use logic and probability to keep transactions within a risk threshold. An institution may also apply several risk management levels for different transactions. The risk tolerance for an inexpensive purchase will be higher than for high-end purchases, but it is a risk nonetheless.
A business should work carefully with its development team to create a security solution that makes sense, as low-risk tolerance may lead to false positives and blocked transactions. But if the risk tolerance is too high, the organization may deal with additional fraudulent purchases and payments.
Every business has different levels of risk tolerance and slightly varying ways that they are exposed. But suppose a business is unwilling to commit to a custom business logic and AI protection for their security. In that case, any legitimate customer that comes their way may find their platform hard to use and maybe even untrustworthy.
These legitimate customers may experience a hold on their payment even though they are the owner of their user account and the credit details being used.
Although it may not be immediately apparent, using AI with a zero-tolerance for risk can lead to a sharp interruption in sales performance. To combat this, it should be customized to a business’s specific level of risk tolerance, generating a high number of sales while also minimizing the potential of fraudulent activity.
In other words, an effective fraud risk threshold is dependent on creating a balance between business priorities and the business weak points. Easy to tweak, these custom risk thresholds can be adjusted to current concerns and current trends in fraud tactics.
By understanding how vital setting fraud risk thresholds are, a business can assess its model accuracy and invest in improving it to be as up-to-date and effective as possible.
- Real-time Fraud Detection
A business often realizes too late when something is amiss in rule-based fraud protection. It may take days for them to realize the fraud and weeks to resolve the issue for the consumer. Some businesses won’t even know that fraud has occurred until they receive a chargeback claim from a customer’s card company. This is made worse by the lengthy dispute process, which could take up to eight weeks.
Compared to this, business logic fraud protection uses AI integration technology to watch transactions in real-time.
The AI integration will track and log problematic transactions as they happen. The system will block most fraud attempts before they can cause damage, meaning fewer successful breaches in need of investigation. Such efficiency is achieved through thousands of accurately performed computations in milliseconds.
Combating fraud used to be a complex process that involved manual transaction reviews. But with artificial intelligence fraud prevention, real-time detection immediately makes an alert at suspicious behavior, saving a business a lot of time and money as well as preventing additional complications.
Real-time features are based on categorical data such as country, ASN card digits, email domains, etc. These noted features aid businesses in expanding into new markets without their machine learning software suffering any adverse effects, such as bias, by monitoring the real-time traffic.
- Improving Customer Care by Minimizing False Declines
A transaction that does not follow a standard pattern does not necessarily mean fraudulent activity. Someone on vacation will naturally use credit cards from a new location.
A significant benefit of AI payment fraud detection is its ability to respond to minor anomalies. Though the vacationer may need to verify their identity during the first transaction at the resort, once the AI registers the new location, purchases can happen without interruption.
False declines are frustrating for consumers. Not only are they inconvenient, but they are also worrisome. Someone who is far from home will get nervous when their payments are denied. They will question if someone is tampering with their accounts, and these stressful feelings can damage business-customer relations.
A survey found that businesses lose an estimated 3% of their revenue every year, and this is because of false declines.
However, by utilizing custom AI solutions, the accuracy of payment fraud detection and prevention will improve the number of legitimate transactions being approved and the number of fraudulent ones being rejected.
AI today is notable for its efficient ability to minimize the total number of false declines by precisely identifying transaction anomalies.
But if a business is adamant, then there is always the less-than-great alternative of using a rule-based decision engine that will often reject even legitimate customer orders, which will only incur more unhappy customers as well as a loss in revenue.
The Future of Fraud Prevention Needs both AI and Human Input
When customized and implemented correctly, AI integration will significantly improve a business’s ability to detect and prevent fraud attacks. No longer can businesses expect rule-based fraud solutions to prevent ever-evolving and developing fraud tactics.
Especially when machine learning can do so much more than merely detecting a fraud attack, its deep learning ability can also flag spam emails, fraudulent image recognition, and product recommendations.
Machine learning, however great, cannot replace a fraud analyst team. Though it does aid in removing the heavy burden of vigilance. Allowing a business’s team to invest more time and energy into urgent cases, investigations, insights, and reporting. Integrating such a solution makes the role of fraud analyst that much more efficient.
Analysts can then improve and optimize machine learning fraud detection systems through fine-tuning rules and reviewing and labeling customers. Because though the machine is unbeatable at doing the heavy lifting of data analysis, number crunching, and output, it still needs the assistance of humans to fine-tune and truly optimize its contributions to payment fraud protection.
Human insight is crucial in picking up something a machine may miss; manual review allows for a further fine-tuning of a business’s fraud prevention. Human input won’t simply be sitting there and occasionally approving a transaction. It is there to analyze after events and label all data so that rapid feedback to the machine is seamless. Thus creating accurate results for a precise defense against fraud.
Cybercriminals will continue to look for ways to exploit weaknesses in the security of e-commerce businesses and their sites. These businesses will need vendors that can create custom and cutting-edge security solutions.
Instead of merely responding after attacks, tools are needed to predict, catch, and block them. Incorporating machine logic and AI technology into a security plan offers the best chance of preventing fraud. Custom fraud protection solutions, built by an experienced development team, will help businesses and their customers stay safe in the ever-expanding digital marketplace.
Bio
William Dawsey
Vice President of Sales for Finance and Payments
William Dawsey is Vice President of Sales for Finance and Payments Technologies at Chetu, a global provider of world-class custom software development solutions. William has nearly a decade of experience in the financial technology space and has helped numerous brands and institutions develop and integrate the latest in POS and FinTech solutions. William Dawsey facilitates programmers with expertise in utilizing cloud-based, web, and mobile solutions for financial service modules such as payment gateways and POS software, accounting & tax preparation software, plus banking and finance service modules.