After experiencing rising fraud attempts, increasing digital transactions, and skyrocketing false positives in their anti-money laundering program, JP Morgan implemented an artificial intelligence (AI) detection system as early as 2021. The bank created an 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.
Take advantage of platforms like Trustpair to access proprietary algorithms that detect fraudulent transactions from data changes. Request a demo to learn more!
What is AI in fraud detection?
AI in fraud detection typically refers to machine learning models able to spot patterns. It 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 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.
AI models 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 financial fraud? A few examples
Accordingly to our latest fraud study, 90% of US companies reported being targeted by cyber fraud in 2024. 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 and identify patterns.
- 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 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 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
Various finance professions are thus impacted; for example, treasury departments must prepare for the AI shift.
Dynamic rules
Before AI, fraud detection systems 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 fraud detection
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 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 90% of US companies targeted at least once in 2024, 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 powered solutions can detect?
One of the best use cases for AI in fraud detection is to prevent payment fraud. 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 and identity theft: AI systems are employed by financial institutions to accurately track user behavior, and flag changes that could suggest that card details have been stolen
And many others (account takeover, deepfakes, etc.)!
Fraud can have significant consequences on organizations – and not only financial losses. Experienced treasurer Lee-Ann Perkins answered our questions about the financial toll of payment fraud in our latest Fraud Flash video!
What are the challenges of using AI to detect fraud?
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.
At Trustpair, we use AI for specific high-risk cases but our fraud prevention and detection solution relies on proprietary algorithms built and improved by human experts. Our threat detection solution detects anomalies in transactional data – new account number, company domiciliation in risky countries, etc -, signaling potential fraud. A real-time warning is sent to teams in charge to avoid fraudulent activities and make sure businesses are only processing legitimate transactions.
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. In examples like these, AI fraud detection and prevention controls are useless, since the fraudster is working in physical capacity, rather than digital.
AI technology, one of the 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.
