After rising fraud attempts in the 2010s, JP Morgan implemented an AI detection system. The bank created a machine learning algorithm to track live transactions and identify anomalies (even at scale). Since it was developed, JP Morgan has reported ‘lower levels of fraud, better customer experience and a reduction in false positives’. This complete guide breaks down the use cases for AI in fraud prevention. Learn how to take advantage of platforms like Trustpair to access proprietary machine-learning algorithms that detect fraudulent activities from data. Request a demo to learn more!
What is AI in fraud detection?
Being widely accessible, but without a set guide-book has caused the perfect storm of misuse in AI. Bad use cases have tarnished the reputation of artificial intelligence, and many won’t know that there are actually plenty of AI applications for good. For example, AI algorithms can be applied to detect bank account changes, classify how suspicious these changes are, and detect threats of fraud. It’s one of the key finance and accounting trends for 2024!
AI in fraud detection typically refers to a range of machine learning technologies. Machine learning works by applying a set of rules (known as an algorithm) to a scenario to make a decision. In this case, decide whether there is a threat of fraud, or not.
But the great advantage is that these rules evolve over time, as the technology is fed more information. AI “learns” from previous scenarios, and gets more and more accurate over time.
Some examples of machine learning technology include:
- Natural language processing (NLP) models: enrich and categorize data into granular groups
- Captcha / reCaptcha: automated test to separate humans from computers
- Graph neural networks (GNNs): a data processing system which maps out the relationship between different pieces of information to better understand an overall
How does AI work to detect fraud?
There are several steps for fraud detection that AI is involved with:
- Data collection: continuous data collection is at the core of fraud detection. It enables businesses to set their ‘normal’ range of data. Some of the data collected by AI platforms includes transactional data like amount or account details, and behavioral data, like time spent purchasing.
- Anomaly detection: after setting the precedent for ‘normal’, AI models are relied upon to flag ‘out of range’ data in real-time. This data may be deemed suspicious, and relies on statistical AI algorithms to be detected.
- Continuous accuracy improvements: since AI models learn from themselves, they are less likely to make the same mistakes over and over. This helps to reduce the occurrence of false positives.
- Alerting and reporting: when fraudulent threats are suspected, it’s imperative to move to the next stage of fraud prevention: response. AI can alert humans in real-time, but it can also release a set of actions to protect the business, such as blocking outgoing payments, or removing email attachments.
What are the benefits of AI in fraud detection?
A recent study by PWC and the Bank of England found that despite all the potential cases for misuse, AI is more effective at fraud detection than manual controls. It’s obvious, then, that compared to no controls at all, AI provides a significant advantage to organizations in their fight against fraud.
There are several key benefits, including:
- Dynamic vs static rules
- Real-time vs lag
- Cost of prevention vs reaction
Dynamic rules
Before AI, fraud detection strategies relied on applying static rules to data in order to find the anomalies. But this could cause inaccuracies and false positives, since the rules never evolved unless the entire algorithm was changed.
Instead, AI learns from itself, setting dynamic rules that change with the circumstances. For example, an email sorting platform might evolve its rule for categorizing junk emails once it discovers ‘off-brand’ domains. After learning that an email from googlee.com has a high likelihood of spam, the AI platform can start categorizing all typo’d domain names into the junk folder. And this personalization can be scaled up to thousands of customers and vendors each month.
By applying dynamic over pre-defined rules, companies using AI can benefit from higher accuracy. With fewer risks of false positive results, they’ll also keep user experience intact, generating lower friction within the sales process.
Real-time
Prior to the adoption of AI for fraud detection, companies were faced with employing a full time statistician to continuously analyze for threats. Not only was this a costly venture, but it relied on this individual not making manual errors, and working quickly. This left companies at a significant disadvantage – they couldn’t scale up and maintain the same levels of oversight.
But with AI algorithms working in real-time, organizations can benefit from scale without the same costs. This leads to further benefits when suspicious activities are detected – since AI algorithms can instantly block, freeze or protect their accounts, and report back to team members instantaneously. Having this all completed while continuing to assess more data in the background makes AI the multi-tasker of the times.
Long term cost optimization
It’s no secret that employing AI to prevent and detect fraud can be a costly investment. But with its heightened levels of accuracy, speed, and ultimate success, AI might ultimately provide a long-term saving. That’s due to the lower likelihood of realized fraud events.
By effectively preventing cases of fraud, companies face fewer reimbursements, fewer embarrassing reputational problems, and reduce their risk of fines for non-compliance.
It can be compared to insurance – since you never really know if you’ll need it. But with 96% of US companies targeted at least once in 2023, it’s a question of when, not if. Wouldn’t you rather invest in order to be better protected against fraud?
Download our latest fraud report for more details about B2B fraud!
What are the types of fraud AI can detect?
One of the best use cases for AI in fraud detection is to prevent payment fraud, which Sade Telecom, a construction company, experienced first hand.
It occurred when the company’s accounting department received a letter requesting to change the payment details of one of their well-known suppliers. They had already received the accompanying invoice, so simply input the new account number and routing code, and off the payment went.
Except for the fact that three weeks later, Sade Telecom received another notice from their supplier: a warning that their most recent invoice had become overdue. After some investigation, they realized that the letter was a fake, and that they had in fact paid the latest invoice to fraudsters.
Having suffered significant financial losses, Sade Telecom turned to Trustpair. We use a proprietary hello@thesearchcure.com algorithm as part of our payment fraud prevention platform, which continuously checks account details with external databases to ensure they are correct. And when suspicious activity is detected, we automatically block payments to ensure your organization has all the time they need to investigate.
Some of the other types of fraud that AI can detect include:
- Chargeback fraud: shopify is one platform that automatically assesses the risk of chargeback fraud for an online payment, based on the historical behavior of the customer
- Fake account creation: a dedicated bot detection platform can validate the identity of website’s your account holders and block the attempted creation of fake accounts
- Credit card fraud: AI systems are employed by banks to accurately track user behavior, and flag changes that could suggest that card details have been stolen
What are the challenges of using AI in fraud detection?
Being open about the key challenges of using AI in fraud detection will support companies in applying the right systems, practices and procedures to make the investment of their AI technologies effective.
The two top challenges are:
- Black boxes
- Ineffectiveness against non-digital threats
Black box
Many AI algorithms are created in something known as a black box – which effectively hides its decision-making process. This means that AI lacks transparency, can cause huge trust problems when errors occur, and might be hard to effectively control. Black boxes are also at the crux of many of the morality arguments around AI; after all, if you can’t monitor how it’s being developed, how ethical can AI be?
Getting around this challenge isn’t easy, especially if you’re relying on third parties to build your AI platforms. Remember to be transparent and check for manageable interfaces, ensuring your team members can navigate the platform with confidence.
Ineffectiveness against non-digital threats
The other key challenge of using AI is that it’s far less effective against offline fraud threats.
For example, in a classic case of stolen identity, imagine that a fraudster heads to the ATM to make a withdrawal of their victim’s funds. Once the perpetrator knows the PIN number, they can access the entire account, withdrawing as much as they need (even changing the PIN on the spot to lock out their victim).
In examples like these, AI fraud detection and prevention controls are useless, since the fraudster is working in physical capacity, rather than digital. Organizations can employ extra KYC controls and measures like two-factor authentication to prevent unauthorized physical access to accounts.
For most companies though, this is less of an issue since it’s unlikely that you’ll encounter many physical fraud threats.
AI is the best path for fraud prevention
Technologies like NLP, Captcha and GNNs enable businesses to use AI for fraud prevention. Dynamic rules and real-time reporting benefits help companies to detect fraud threats before they happen, and prevent the disastrous financial consequences. Trustpair’s proprietary machine learning algorithm is one way that organizations can prevent payment fraud.
