Financial frauds are criminal acts where scammers acquire money by deceiving people. It is as old as humanity has existed on this planet. It found its way when there was barter long ago and continues to exist today even after the evolution of cashless transactions.
With businesses evolving so rapidly in the global village today, financial frauds have diversified too. Sometimes even the news of scams and ideas will stun you. However, keeping in mind the learnings from scams, new technologies have emerged. Artificial Intelligence, Machine Learning, Cloud Computing, and more are now aiding businesses to prevent any such frauds.
Along with this, the financial industry is going through a significant transformation with Machine Learning. It allows systems to intuitively learn and augment their performance daily for the greater good.
A recent report by Markets and Markets has suggested that the fraud detection and prevention (FDP) economy is predicted to grow from $20.9 Billion to $38.2 Billion worldwide by 2025.
This article will discuss the various financial frauds and their prevention with Machine Learning and AI.
Table of Contents
Types of Online Frauds
- Phishing: People involved in this method of fraud will already have access to your details like phone number, email, date of birth, and probably even your address. Phishing is one of the easiest ways to get scammed, as the messages or emails received from the fraudster will seem genuine as they contain your confidential details. They attempt to steal your passwords, bank credentials, and credit/debit card details by luring you into attractive deals.
- Identity Theft: In any online transaction, your physical presence is not required. When your details and credentials get leaked to a potential scammer, they can use them to access your private financial accounts. The computer working behind the scenes will not be aware of this fraud as all it sees are just numbers and not your physical self.
- Insurance Frauds: Sometimes, even an ordinary person can trick insurance companies into believing their insured property is stolen or damaged to claim the insurance funds. This is a prevalent problem that many insurance companies face as some property can be hidden, and even physical presence will not help the agents confirm if the property is stolen.
How does Machine Leaning help in financial fraud detection?
A machine learning model must initially gather data to identify fraud. The model segments analyze and extract the necessary features from all the collected data. Next, training sets are given to the machine learning model to educate it on forecasting the likelihood of fraud. Finally, it develops machine learning algorithms for fraud detection.
For all this to work, we must first collect large amounts of financial data, and unlike a human who cannot comprehend this data, ML models require humongous amounts of data for them to develop. Then additional elements are introduced to describe honest consumer behavior and fraudulent activity. These characteristics often include the client’s location, identity, orders, network, and preferred payment method. The list of investigated features may vary depending on the sophistication of the fraud detection system.
Next, a training algorithm is developed to process this data by imputing a set of rules, guidelines, and functions—these inputs let the ML model identify if the provided information is legit or fraudulent. The more data, the easier it gets for the ML model to determine, as there are more samples for the model to learn and develop itself into a proper fraud detection AI.
Finally, after completing the training, the organization receives a fraud detection Artificial Intelligence application appropriate for their business. This application can accurately and quickly identify fraud. The machine learning model behind this AI must be regularly updated and refined to detect credit card fraud effectively. With the progression of AI and ML in fraud analysis, new methods will be discovered to trick the software, so the organization must continue the ML training to remain an absolute solution.
Benefits of using AI/ML for fraud detection
Unlike humans, who cannot perform repetitive tasks and observe deviations, machine learning tools are exclusively built to excel in them. This helps financial organizations detect fraud in a much shorter time than any human could. Thousands or even millions of payments can be precisely analyzed by algorithms every second. This improves process efficiency by significantly cutting expenses and time needed to examine transactions.
Increased rate of data collection
As the velocity of commerce is rising, it’s essential to have quicker solutions like machine learning to detect fraud. Machine learning algorithms can evaluate enormous amounts of data in a concise amount of time. They can continuously collect and analyze data in real-time and detect fraud in no time.
Financial organizations may prevent fraud and give their consumers the best level of protection by implementing machine learning technologies. Every new transaction is compared to the previous one, which includes personal information, data, IP addresses, locations, etc., to identify suspicious circumstances. Financial departments can avoid fraud involving credit or payment cards as a result.
With additional data sets, machine learning algorithms and models become more efficient. More data helps the Machine learn better since it can distinguish between various behaviors’ similarities and differences. After distinguishing between legitimate and fraudulent transactions, the system can sort through them and identify those belonging to a specific bucket.
Quality Customer Service
Before adopting AI in the banking industry, customer service agents often handled client inquiries, occasionally including a drawn-out procedure. AI can speed up detecting and analyzing fraud by automating it, enabling banks to respond to clients more quickly. By lowering false positives during fraud detection procedures, AI might also improve the consumer experience.
Machine Learning and Artificial intelligence require high-performing GPUs and a large amount of RAM. It is not feasible for financial organizations to purchase and maintain this hardware as it requires a lot of space and specialists to get optimal results. It is easier for financial organizations to outsource their hardware requirements to Ace, one of the industry’s most approachable and affordable cloud providers, catering to the latest hardware necessary for ML.
Unlike others, we provide you with the latest GPUs from the Ampere series of Nvidia to meet your demands. These GPUs offer the best-accelerated computing needed for training your machine learning models. Our pricing model is based on pay-as-you-go, which allows users to only pay for the resources they use.
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- How GPU Computing is Improving Financial Analytics
- Harness the Power of GPUs to Accelerate Analytics Processing
- Why GPUs for Deep Learning? A Complete Explanation
- Cloud GPUs: The Cornerstone of Modern AI
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