The projected increase in fraudulent transactions is estimated to be
$6.5 billion between 2021 and 2027, and its impact on the finance industry is expected to be significant. Financial institutions are prioritizing proactive fraud prevention as they seek effective solutions to minimize fraud and enhance the overall security of their clients.

Machine learning (ML) is a process that involves teaching machines to identify patterns in large volumes of data, often associated with artificial intelligence (AI). Presently, millions of individuals interact with ML through various popular applications. For instance, Uber and Google Maps employ ML to estimate travel times, while Siri, Alexa, and Google Assistant utilize ML to provide users with personalized information based on their preferences.

How can financial institutions take advantage of the incredible efficiency of ML to help protect their client’s transactions and reduce overall fraud? To answer this question, I’ll provide evidence of how ML has been successful thus far in preventing fraud in the finance industry. 

Why Human Labor Alone Isn’t Enough to Combat Fraud Technology

The rapid advancement of technology is significantly improving the convenience of people’s lives. In the banking sector, this translates to heightened consumer expectations for digital and mobile options.

Financial institutions are faced with the challenge of accurately processing billions of transactions as more customers opt for online banking. Unfortunately, the abundance of data also presents an opportunity for increased fraudulent activities.

Scammers have become increasingly innovative, successfully executing thousands of fraudulent transactions every second, which poses a formidable challenge for financial institutions in detecting deceitful behavior.

According to estimates by
Cybersecurity Ventures, global cybercrime is projected to cost the economy approximately $10.5 trillion annually by 2025, nearly doubling the reported figures from the previous year. To put this into perspective, this amount exceeds the combined cost of all natural disasters in the United States in 2021, which totaled a staggering $145 billion.

Given the immense scale of fraud and the limitations of human labor in combatting it, businesses have turned to machines as a crucial defense. Automated and rule-based fraud detection systems have been implemented by financial institutions, as manual systems are unable to effectively handle real-time data streams.

However, there is a far superior solution: machine learning (ML) and artificial intelligence (AI). The remarkable ability of machines to learn from historical data patterns and identify anomalies is both remarkable and indispensable.

Where Financial Fraud Happens & How ML Tackles Them

Financial detection software proves particularly valuable in specific areas for effectively identifying and flagging

fraudulent activities. These areas include:

1. Credit card fraud, which is the most prevalent type of payment fraud due to the digital storage of card data, providing criminals with more opportunities to commit crimes. ML solutions primarily focus on detecting transactions that deviate from clients’ regular spending patterns.

2. ATMs, which are susceptible to various forms of fraud, such as the theft of debit card numbers and PINs. Fraudsters often employ false doorway card readers to obtain and save card information. By acquiring card data, they can create counterfeit cards for unauthorized transactions or cash withdrawals.

ML solutions for ATM fraud include anomaly detection to identify unusual transaction patterns, behavior analysis to compare current transactions with a cardholder’s historical spending habits, detection of card cloning through analysis of duplicate card usage, network monitoring to detect tampering or skimming devices, and real-time risk scoring to assign risk levels to transactions for further scrutiny.

3. Point-of-Sale (POS) fraud, where employees exploit their positions to steal money from their employers. Regular data checks conducted after each shift, day, week, or month serve as an effective prevention measure. ML plays a significant role in analyzing data segments to validate factors such as user log counts, transaction deletions, billing records, customer refunds, and usage of loyalty program cards.

4. Email phishing, a fraudulent technique wherein emails masquerade as legitimate communication and contain links designed to trick users into revealing sensitive information. Phishers have become adept at evading detection by concealing malicious files.

ML-based malware scanners have emerged as successful tools capable of identifying and deleting malicious emails before they reach users’ inboxes. Companies like Microsoft, with Office 365 Advanced Threat Protection, and Google employ ML to detect and block millions of harmful emails and malware, safeguarding users’ data. These ML models have rapidly evolved to enhance the identification of phishing threats, successfully blocking 99% of spam emails before they reach users.

5. Mobile fraud, which is increasing in prevalence as payment methods are frequently stored on users’ smartphones. Skilled hackers can access this information and initiate unauthorized transactions unless there is an ML-powered tool to promptly alert users.

Why Is Machine Learning So Effective for Fraud Detection?

Machine learning (ML) relies on computational statistics and utilizes mathematical models to define “normal” user behavior. By leveraging historical data, ML algorithms can make predictions and improve their accuracy over time.

ML proves
highly effective in fraud detection, especially in the realm of digital payments, which have become increasingly vulnerable due to the rise of mobile payments and the inherent security gaps in some mobile wallets. Financial institutions strive to ensure transaction safety through rule-based systems, but these often involve additional verification steps that can negatively impact the customer experience. Users are generally reluctant to add more layers of protection, as it introduces friction into their payment process.

ML and AI empower financial institutions to gain insights into customers’ spending habits and their fluctuations throughout the year. By recognizing established patterns, anomalies and suspicious transactions can be easily detected and blocked, providing customers with enhanced protection without the need for burdensome verification steps.

The Benefits of Using Machine Learning
ML can be applied in several areas of the overall payment process to prevent fraud. It allows clients’ account information to remain safe and lowers overall costs incurred, such as the time operators spend in call centers trying to help clients mitigate the consequences of scams. Beyond cost and resource savings, benefits of ML include:

Improved data credibility assessment
Computers can be taught to validate personal details in any transaction. This bridges a wide gap that might appear in long transaction sequences. By reconciling documents with system data, machine learning eliminates the risks of human mistake that often happens in these scenarios.

Better evaluation of duplicate transactions
One of the popular ways for scammers to get money is to create a new transaction at the same or similar time when the original transaction happened. Rule-based systems often fail to distinguish the difference and don’t always flag the duplicated transaction as fraudulent.

More effective data analysis
With more data from which to learn, ML will improve at recognizing patterns faster than teams of even the brightest analysts. Since human errors are a large reason why financial institutions are losing money, ML and AI solutions can minimize these errors. ML can also help with data overloads and brings added automation that often leads to increased customer satisfaction.

Fraud Detection ML Models and Algorithms

There are a
handful of algorithms used to “train machines” and the two most popular ones are supervised and unsupervised models. When an algorithm is used as a set of instructions, machines can build models by processing data to create a baseline with which they can compare new information. 

Supervised learning models

A supervised model is the most common model of ML across multiple disciplines. Once machines are “fed” with enough data with tagged transaction information, they will generate spending models and compare new data with what they already have. Fraudulent and regular user behavior is labeled beforehand so the machine can understand the difference and only needs to learn from it. The more data a machine has, the easier it is for it to make accurate assumptions. 

Unsupervised learning models

Unsupervised models differ from supervised ones because they work with data theirt is unlabeled so the machine must learn to recognize fraud on its own. In some cases, it’s difficult to identify which transaction is problematic and that’s when the machine takes up assumptions based on large data sets it has learned from. 

Semi-supervised learning models

Semi-supervised learning falls somewhere in-between between supervised and unsupervised models. It manages well in situations when labeling information is not possible, making assumptions based on discovered patterns.

Reinforcement learning models

Reinforcement learning algorithms allow machines to discover behavior norms inside a specific context to learn from. These systems constantly learn and look to find behavior that doesn’t fit and raise red flags when it finds it. 

Final Thoughts on Financial Data Fraud Prevention Using ML

The finance industry benefits greatly from the utilization of machine learning algorithms for pattern identification. These algorithms can uncover correlations within extensive datasets that would be challenging for human analysts to identify within a reasonable timeframe.

ML’s ability to rapidly analyze and learn from data is particularly valuable in finance, where it can be applied to credit history analysis, payment processing, remittance evaluation, and fraud prevention. ML and AI excel at processing large volumes of data in real-time, enabling the identification of patterns and the identification of fraudulent transactions.

By leveraging ML with available datasets, banks, neobanks, payment providers, and other financial institutions can establish robust prevention systems and offer customers a level of assistance that traditional rule-based systems cannot match. 

Ultimately, the goal for every financial institution is to
provide secure payments and safeguard customers’ sensitive information and funds, as the impact of fraud can significantly influence their future prospects and reputation.

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• Source: https://www.finextra.com/blogposting/24221/ml-in-finance-is-it-the-silver-bullet-for-preventing-fraud?utm_medium=rssfinextra&utm_source=finextrablogs