How Is AI Used in Fraud Detection?

The Wild West had gunslingers, bank robberies and bounties — today’s digital frontier has identity theft, credit card fraud and chargebacks.

Cashing in on financial fraud has become a multibillion-dollar criminal enterprise. And generative AI in the hands of fraudsters only promises to make this more profitable.

Credit card losses worldwide are expected to reach $43 billion by 2026, according to the Nilson Report.

Financial fraud is perpetrated in a growing number of ways, like harvesting hacked data from the dark web for credit card theft, using generative AI for phishing personal information, and laundering money between cryptocurrency, digital wallets and fiat currencies. Many other financial schemes are lurking in the digital underworld.

To keep up, financial services firms are wielding AI for fraud detection. That’s because many of these digital crimes need to be halted in their tracks in real time so that consumers and financial firms can stop losses right away.

So how is AI used for fraud detection?

AI for fraud detection uses multiple machine learning models to detect anomalies in customer behaviors and connections as well as patterns of accounts and behaviors that fit fraudulent characteristics.

Generative AI Can Be Tapped as Fraud Copilot

Much of financial services involves text and numbers. Generative AI and large language models (LLMs), capable of learning meaning and context, promise disruptive capabilities across industries with new levels of output and productivity. Financial services firms can harness generative AI to develop more intelligent and capable chatbots and improve fraud detection.

On the opposite side, bad actors can circumvent AI guardrails with crafty generative AI prompts to use it for fraud. And LLMs are delivering human-like writing, enabling fraudsters to draft more contextually relevant emails without typos and grammar mistakes. Many different tailored versions of phishing emails can be quickly created, making generative AI an excellent copilot for perpetrating scams. There are also a number of dark web tools like FraudGPT, which can exploit generative AI for cybercrimes.

Generative AI can be exploited for financial harm in voice authentication security measures as well. Some banks are using voice authentication to help authorize users. A banking customer’s voice can be cloned using deep fake technology if an attacker can obtain voice samples in an effort to breach such systems. The voice data can be gathered with spam phone calls that attempt to lure the call recipient into responding by voice.

Chatbot scams are such a problem that the U.S. Federal Trade Commission called out concerns for the use of LLMs and other technology to simulate human behavior for deep fake videos and voice clones applied in imposter scams and financial fraud.

How Is Generative AI Tackling Misuse and Fraud Detection? 

Fraud review has a powerful new tool. Workers handling manual fraud reviews can now be assisted with LLM-based assistants running RAG on the backend to tap into information from policy documents that can help expedite decision-making on whether cases are fraudulent, vastly accelerating the process.

LLMs are being adopted to predict the next transaction of a customer, which can help payments firms preemptively assess risks and block fraudulent transactions.

Generative AI also helps combat transaction fraud by improving accuracy, generating reports, reducing investigations and mitigating compliance risk.

Generating synthetic data is another important application of generative AI for fraud prevention. Synthetic data can improve the number of data records used to train fraud detection models and increase the variety and sophistication of examples to teach the AI to recognize the latest techniques employed by fraudsters.

NVIDIA offers tools to help enterprises embrace generative AI to build chatbots and virtual agents with a workflow that uses retrieval-augmented generation. RAG enables companies to use natural language prompts to access vast datasets for information retrieval.

Harnessing NVIDIA AI workflows can help accelerate building and deploying enterprise-grade capabilities to accurately produce responses for various use cases, using foundation models, the NVIDIA NeMo framework, NVIDIA Triton Inference Server and GPU-accelerated vector database to deploy RAG-powered chatbots.

There’s an industry focus on safety to ensure generative AI isn’t easily exploited for harm. NVIDIA released NeMo Guardrails to help ensure that intelligent applications powered by LLMs, such as OpenAI’s ChatGPT, are accurate, appropriate, on topic and secure.

The open-source software is designed to help keep AI-powered applications from being exploited for fraud and other misuses.

What Are the Benefits of AI for Fraud Detection?

Fraud detection has been a challenge across banking, finance, retail and e-commerce.  Fraud doesn’t only hurt organizations financially, it can also do reputational harm.

It’s a headache for consumers, as well, when fraud models from financial services firms overreact and register false positives that shut down legitimate transactions.

So financial services sectors are developing more advanced models using more data to fortify themselves against losses financially and reputationally. They’re also aiming to reduce false positives in fraud detection for transactions to improve customer satisfaction and win greater share among merchants.

Financial Services Firms Embrace AI for Identity Verification

The financial services industry is developing AI for identity verification. AI-driven applications using deep learning with graph neural networks (GNNs), natural language processing (NLP) and computer vision can improve identity verification for know-your customer (KYC) and anti-money laundering (AML) requirements, leading to improved regulatory compliance and reduced costs.

Computer vision analyzes photo documentation such as drivers licenses and passports to identify fakes. At the same time, NLP reads the documents to measure the veracity of the data on the documents as the AI analyzes them to look for fraudulent records.

Gains in KYC and AML requirements have massive regulatory and economic implications. Financial institutions, including banks, were fined nearly $5 billion for AML, breaching sanctions as well as failures in KYC systems in 2022, according to the Financial Times.

Harnessing Graph Neural Networks and NVIDIA GPUs 

GNNs have been embraced for their ability to reveal suspicious activity. They’re capable of looking at billions of records and identifying previously unknown patterns of activity to make correlations about whether an account has in the past sent a transaction to a suspicious account.

NVIDIA has an alliance with the Deep Graph Library team, as well as the PyTorch Geometric team, which provides a GNN framework containerized offering that includes the latest updates, NVIDIA RAPIDS libraries and more to help users stay up to date on cutting-edge techniques.

These GNN framework containers are NVIDIA-optimized and performance-tuned and tested to get the most out of NVIDIA GPUs.

With access to the NVIDIA AI Enterprise software platform, developers can tap into NVIDIA RAPIDS, NVIDIA Triton Inference Server and the NVIDIA TensorRT software development kit to support enterprise deployments at scale.

Improving Anomaly Detection With GNNs

Fraudsters have sophisticated techniques and can learn ways to outmaneuver fraud detection systems. One way is by unleashing complex chains of transactions to avoid notice. This is where traditional rules-based systems can miss patterns and fail.

GNNs build on a concept of representation within the model of local structure and feature context. The information from the edge and node features is propagated with aggregation and message passing among neighboring nodes.

When GNNs run multiple layers of graph convolution, the final node states contain information from nodes multiple hops away. The larger receptive field of GNNs can track the more complex and longer transaction chains used by financial fraud perpetrators in attempts to obscure their tracks.

GNNs Enable Training Unsupervised or Self-Supervised 

Detecting financial fraud patterns at massive scale is challenged by the tens of terabytes of transaction data that needs to be analyzed in the blink of an eye and a relative lack of labeled data for real fraud activity needed to train models.

While GNNs can cast a wider detection net on fraud patterns, they can also train on an unsupervised or self-supervised task.

By using techniques such as Bootstrapped Graph Latents — a graph representation learning method — or link prediction with negative sampling, GNN developers can pretrain models without labels and fine-tune models with far fewer labels, producing strong graph representations. The output of this can be used for models like XGBoost, GNNs or techniques for clustering, offering better results when deployed for inference.

Tackling Model Explainability and Bias

GNNs also enable model explainability with a suite of tools. Explainable AI is an industry practice that enables organizations to use such tools and techniques to explain how AI models make decisions, allowing them to safeguard against bias.

Heterogeneous graph transformer and graph attention network, which are GNN models, enable attention mechanisms across each layer of the GNN, allowing developers to identify message paths that GNNs use to reach a final output.

Even without an attention mechanism, techniques such as GNNExplainer, PGExplainer and GraphMask have been suggested to explain GNN outputs.

Leading Financial Services Firms Embrace AI for Gains