Last updated on March 1st, 2023

Banking & financial service providers and fintech companies have always been at the forefront of the targets of cybercriminals. For analysts, it is always a challenging task to identify frauds and scams across different financial sectors.

With the advancement in technology and assets available online, cyber-crooks have become more sophisticated in identifying flaws in financial services and leveraging them. IBM estimates that the world will have lost USD 44 billion to fraud by 2025.

Traditional approaches to identifying and eliminating fraud are insufficient as they are static and exceedingly time-consuming. Enterprises and organizations are therefore shifting from traditional approaches to Artificial Intelligence-driven security measures.

Artificial Intelligence (AI) has permeated every sector and has proved to be a boon for the betterment of technology. Risk analysis, risk identification, and fraud detection are excellent applications for Artificial Intelligence (AI) and Machine Learning (ML).

These technologies have applications in financial sector risk and threat identification. But to execute massive data-driven AI systems and for training sophisticated ML models, financial sector institutions require colossal computation resources. They must, in short, begin deploying state-of-the-art GPU servers to develop and accelerate AI/ ML-based solutions for risk and fraud detection.

This article will give you a walkthrough of traditional vs. AI-based fraud detection systems. It will also highlight the benefits of GPU-powered AI solutions in this emerging arena.

Risk and Fraud Detection in the Financial Sector

Risk detection refers to the identification of cyber threats and the dangers that cybercriminals pose to a financial institution. Fraud detection is the strategy used to determine fraudulent actions or scams performed by fraudsters toward financial organizations like banks, insurance companies, etc.

Many cyber criminals not just target financial institutions for money but also aim to strain the company’s reputation. Any severe fraud or cyber risk when discovered can deteriorate the organization’s brand image. Not having real-time risk analysis and fraud detection systems in place can cost millions, if not billions, of dollars.

Often, besides financial and reputational loss, cyberattacks also open up financial organizations to class-action lawsuits since there is also substantial leakage of personal and sensitive customer information as well.

Therefore, financial institutions are using different approaches to identify risks and fraud. Some well-known areas where AI-based fraud detection can be an asset are:

  • Credit card fraud identification
  • Discovering identity theft and misuse of authentication
  • Uncovering money laundering patterns on digital platforms
  • Fraudulent transactions through apps and online services
  • Loan and mortgage app fraud detection
  • Using conversational AI in identifying fraudulent insurance claims

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Traditional Detection vs. AI-based Detection

Traditional risk and fraud detection systems employ heuristic approaches to identify risks and fraudulent actions. In those detection systems, security & development engineers use rule-based pre-determined patterns to flag-out suspicious transactions or treacherous acts. Such rule-based platforms are inefficient because they rely on expected customer behaviour and particular pre-designed signatures. Hence, traditional threat detection techniques cause a massive percentage of false-positive responses.

For bridging the gap, financial sectors opted for GPU-accelerated AI solutions that enable intelligent threat detection solutions & identifying fraud in real-time. Today, AI engineers, with security professionals, can train ML models to identify network threats, false transactions, fraudulent insurance claims, etc., using pattern-based learning.

Other AI-based services like AI-based Know Your Customer (KYC) can continually provide additional insights that help financial institutions improve visibility into identity thefts and potential cybersecurity risks.

Benefits of GPU-Powered AI and ML Solutions in Fraud Detection

AI & ML, in collaboration with big data, have revolutionized the way risk and fraud detection solutions work. While artificial intelligence makes all detection autonomous, quick, and efficient, machine learning process large datasets accurately to identify human intentions and behavior for better risk & fraud identification.

However, for processing complex resource-intensive tasks, AI and ML-based solutions need another technological ingredient to push the computational power. This is where GPU servers come into the picture.

Now, let us explore some notable benefits GPUs can provide to AI-driven risk and fraud detection solutions.

#1. Faster Risk Identification in Real-time

Risk and fraud identification is so crucial to an organization that even a slight delay in risk identification can degrade the organization’s reputation and market value. Traditional risk and fraud detection systems are slow as they run on CPUs. They also use rule-based analysis and pre-determined signature-based detection that does not consume much computing resources.

But with AI and ML-based data-driven detection systems, enterprises can precisely identify risks and fraudulent attempts. GPU-powered AI and ML solutions can quickly classify an action as a risk or fraud. Thus, most financial institutions like insurance companies and banks prefer GPU servers to run these enterprise-grade AI fraud detection systems.

Also Read: How is GPU Accelerated AI for Fraud Detection

#2. Accurate Prediction with Zero False-Positive

Traditional rule-based risk detection systems are not accurate at all. They often raise false positive flags that cause false notions among internal teams. That is where data-driven ML solutions come into the picture. Data-driven AI systems often consume massive amounts of structured, semi-structured, and unstructured data so that financial and other companies can train the system to identify threats accurately.

To construct such training through massive data quickly & without lag, companies prefer to leverage GPUs. Again, since all GPU cores remain dedicated to training the ML model through the given data, model training becomes smooth and accurate. Accuracy through ML-based training also eliminates the chances of the system raising false positives.

#3. Intention & Behavior-based Fraud Detection

Real-time fraud identification and risk analysis have become a well-known benchmark for an effective customer-centric system across different sectors. Conversational AI & smart chatbots for insurance or banking sectors are prominent examples. In these AI-based solutions, the system can recognize and flag customers based on intentions and conversational behavior.

Intent & behavior-analyzing systems are often complex because they run on various metrics, factors, and algorithms like NLP, computer vision, etc. Hence, they become resource-intensive and need GPUs to accelerate their processing. With massive potential from GPUs, these systems can learn from customers in real-time to understand their communication patterns.

Based on customer interaction, these AI systems can pinpoint and flag them as fraudulent or suspicious. Furthermore, GPU-powered AI solutions prove much more effective and time-saving than manual approaches to identifying fraud.

4. Risk Prediction

Modern risk detectors have become more refined in identifying anomalies in advance. These systems use AI and ML that get train colossal chunks of data. These AI systems also contain complex algorithms having multiple factors and metrics to determine patterns and anomalies. Furthermore, to predict the risk, these systems need to compute massive statistical analyses and analyze through an extensive mathematical model.

That is where these systems need GPU to streamline prediction with more accuracy and without latency. With accurate risk prediction through GPU-powered AI solutions, organizations can identify in advance any adversarial actions or threats to the financial systems.

#5. Automating Risk and Fraud Handling and Response System

Financial sectors & insurance industry should remain vigilant about suspicious activity or unusual transactions through their servers. That is where they collaborate with real-time risk management and fraud detection systems to identify unusual movements & monitor transaction anomalies through AI algorithms.

One well-known example of such an application is comprehensive risk-based authentication, also known as adaptive authentication. In such systems, the ML algorithm operates to understand the user’s behavior and pattern, such as IP address, browser used, time duration to provide the password, etc. It determines whether the legitimate user is accessing the account & making the transaction or someone suspicious has accessed it.

Such systems are computationally resourceful and prefer cloud GPUs over regular processing units. Such systems also need elastic & scalable processing power which Cloud GPUs can deliver. Furthermore, GPUs also underlie the fraud response system which will notify the security team & the legitimate user about the malicious attempt automatically.

#6. Automating Identity Verification

Modern fintech companies prefer app-based digital transactions. These apps require enhanced security. Thus, the apps work in tandem with corporate servers to identify individuals based on biometrics, facial features, gait patterns, IP addresses, and other dynamic factors.

AI algorithms play a significant role in modern and dynamic authentication so that no one can impersonate others’ accounts to perform fraudulent financial activities. Again, AI-based Know Your Customer (KYC) systems frequently incorporate additional insights to improve visibility into potential financial risks and crimes.

Marrying the right Cloud GPU technology can provide requisite performance, efficient fraud detection, scalability, and increased accuracy. Thus, High-Performance Computation (HPC) GPUs have become necessary for financial institutions, applications and online services.

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We have concluded that technological advancements like AI/ML and GPU-powered cloud servers can complement each other, together with data-driven actions to identify risks and frauds. GPU-powered AI/ML solutions not just accelerate risk & fraud identification but can also bring accuracy and scalability. Financial organizations and other sectors should indeed leverage the power of the GPU cloud to augment the potential of these AI/ML systems.

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