Implement financial fraud detection using machine learning in your business

financial fraud detection using machine learning
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Previously, food and beverage company Danone couldn’t rely on their vendor data which left them vulnerable to fraud. So, they decided to do something about it. To protect the business, Danone implemented a platform using machine learning that could audit vendors continuously and guarantee payment security. Read on to learn more about financial fraud detection using machine learning!

Trustpair has a unique methodology combining machine learning, risk scoring, and human expertise to detect and prevent fraud. Request a demo to learn more!

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What is Machine Learning?

Machine learning, a definition

Machine learning is a form of artificial intelligence (AI) that can learn from data patterns, saving the need for it to be specifically told what to do. Learn more about AI in finance in this article!

Machine learning vs AI

Machine learning is a branch of AI. Whereas, AI is the group of technologies replicating human intelligence using machines. Imagine a business group is AI and machine learning is one of the companies it owns.

How can ML be used in fraud detection?

Machine learning is trained on historical data involving different types of fraud. So, it can analyze data without the need for specific instructions in the future. That way, it can autonomously pick up on fraud by detecting odd patterns.

Train it on fraud and non-fraud

For machine learning models to be effective, it must be trained on fraudulent and non-fraudulent transactional data usually through a specific engineering program. That is so that the risk of false positives (blocked non-fraudulent transactions), is lessened as the system gets used to differentiating between fraud and not.

Fraud prevention software

Trustpair’s fraud prevention software involves machine learning, risk scoring, and human expertise to ensure zero fraud. In risk scoring, fraud risk scores are generated for each payment and transaction. A low score should mean that the payment is accepted and a high score leads to the transaction being reviewed by the bank or buyer. Trustpair brings this all together to create a risk status.

This can be:

  • Favorable
  • Unfavorable
  • Unconfirmed
  • Anomaly

Software companies may not always provide scores as it can be tough for organizations to know how to act from their findings.

What kind of financial fraud can be detected with ML?

There are several types of financial fraud that a machine-learning model helps with including:

  • Chargeback fraud
  • Credit card fraud
  • Identity verification
  • Invoice fraud
  • Internal fraud

Chargeback fraud

Chargeback fraud, sometimes known as friendly fraud, can be prevented with machine learning as it assesses patterns and trends.

E-commerce vendors and merchants are targeted after a customer buys an item and may then say that the item didn’t arrive. They may also say that they didn’t cancel a subscription in time to avoid paying a new fee.

Machine learning algorithms are useful for the detection of fraudulent activity as they look at the behavioral analytics such as buyer’s order and purchase history, how often they make chargeback claims, and how often they return items too.

Chargeback fraud incidents soon amount up. In 2024, it is projected that global chargebacks will cost merchants $54.5 billion.

Credit card fraud

Machine learning models can help to detect fraud techniques such as credit card fraud by recognizing suspect patterns in the transaction data.

For example, if a transaction is made in one country, and the billing address or mobile phone number is registered in another, this could raise suspicions. If it is paired with an automated email address, this transaction could receive a negative risk status.

Also, a rule could be created in machine learning algorithms so that if the above occurs, it would prevent a transaction from going through without some form of card verification between the customer and the bank.

Other factors that could signal suspicious activity include:

  • If several accounts are using the same credit card for transactions
  • Multiple costly transactions are made within a short space of time

Invoice fraud

A machine learning model can assess invoices and other documents. It should be able to pick up on vendor details that are associated with red flags and suspicious activity.

For example, the 3-way matching process, which can use machine learning in automation, will compare an invoice with a purchase order (PO) and order receipt, commonly referred to as a goods receipt note (GRN).

This should increase the likelihood of detecting invoice fraud before it happens.

Internal fraud

This is sometimes regarded as occupational fraud. Machine learning systems are good for identifying potential fraud patterns such as a worker approving regular invoices to a new vendor.

That could be a sign of employee embezzlement, where the worker is sending money to an account controlled by themselves. However, in an attempt to get away with it, they hide the transaction in payments made to a new customer or new vendor.

For example, a worker at Aberdeen City Council in Scotland admitted to embezzling more than $1.3 million from the council over 17 years. The fraudster was the council tax and recovery team leader. It was said in court that he could issue council tax refunds of nearly $4,000 (£3,000) without anyone else overseeing the process. He could also solely change the payee account details and alter these details to an account controlled by himself to receive the money.

This just highlights the importance of the segregation of duties, where more than one person completes each task. Therefore, no one should be able to commit internal fraud.

What are the benefits of ML in fraud detection?

Time and resources are saved

Rather than directing members of staff to manually verify the identity of potential suppliers and look at customers’ purchase histories to assess patterns of chargeback fraud, machine learning models can do this for you.

Therefore, employees’ time can be better spent on other pressing tasks rather than a large manual task.

Efficient fraud detection

Machine learning is not 100% foolproof. As we have mentioned, it can raise false positives and false negatives (undetected fraud).

However, on the whole, it is efficient at detecting financial fraud and red flags as examples in chargeback fraud and credit card fraud display.

And, the more data you feed to machine learning, the better trained it is to find fraud.

Fast solution

Machine learning can provide timely responses to quickly assess the identity and reliability of your vendors to make swift decisions. If a human was manually checking the validity of a vendor, it could take a while, but not with machine learning.

For example, Trustpair’s software that uses machine learning has the result of an account validation in 30 seconds.

Recap

Financial fraud can be detected using machine learning systems. They can pick up on the likes of chargeback fraud, credit card fraud, identity verification, invoice fraud, and internal fraud. Trustpair’s unique system brings together machine learning, risk scoring, and human expertise to detect and prevent vendor fraud as well as ensure payment security.

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FAQ
Frequently asked questions
Browse through our different sections and find the answer to your question.

Decision trees, logistic regression, and neural networks are commonly used in fraud detection due to their ability to handle large volumes of data and their effectiveness in recognizing complex patterns that deviate from normal transaction behaviors.

These models are trained on historical banking transaction data and learn to discern between legitimate and fraudulent transactions based on features like transaction frequency, amount, and merchant type.

The choice of model can depend on the specific requirements and the nature of the data available.

Yes, banks extensively use AI to detect fraud.

AI systems are used to monitor banking transactions in real-time, identifying suspicious activities that could indicate fraud. These systems leverage machine learning models, including neural networks and anomaly detection algorithms trained on vast amounts of transaction data. This training enables them to detect patterns and anomalies that deviate from normal behavior, enhancing the accuracy and efficiency of fraud prevention efforts.

By using AI, banks can rapidly respond to potential security threats, safeguard customer accounts, and adapt to new fraudulent methods as they evolve.

Detecting financial fraud involves using sophisticated systems that analyze transaction patterns and behaviors to identify anomalies.

Banks and financial institutions typically employ a combination of rule-based systems and machine-learning models. Rule-based systems flag transactions based on predefined criteria, such as unusually large amounts or rapid frequency of transactions. Machine learning models, on the other hand, learn from historical transaction data to recognize complex patterns and subtle irregularities indicative of fraudulent activities. This dual approach enhances the accuracy of fraud detection, allowing for real-time prevention and the ability to adapt to evolving fraud techniques.

These systems are crucial in maintaining the security and integrity of financial transactions.