Gunfights, bank robberies, bounty Identity theft, credit card fraud, and chargebacks plague the digital era.
Multibillion-dollar criminal companies profit from financial fraud. Fraudsters will profit from generative AI.
By 2026, the Nilson Report expects $43 billion in credit card losses.
Increased financial fraud includes phishing with generative AI, credit card theft using dark web data, and money laundering across cryptocurrencies, digital wallets, and fiat currencies. Digital underworlds hide many money schemes.
Financial institutions utilize AI to detect fraud to keep up. To protect clients and financial organizations, many digital crimes must be stopped immediately.
How is AI fraud detection used?
AI for fraud detection uses many machine learning models to detect client behavior, connection irregularities, and fraudulent account and activity patterns.
Fraud Copilot: Generative AI
Many financial services employ text and numbers. Generative AI and LLMs that learn meaning and context could disrupt and improve businesses. Generative AI improves chatbots and fraud detection in financial services.
Generative AI prompts allow fraudsters to evade AI guardrails. Fraudsters can create contextual emails without typos or grammar issues using human-like LLM writing. Generative AI lets crooks create several phony emails. FraudGPT and other dark web applications leverage generative AI for cybercrime.
Generative AI can financially compromise voice authentication security. Voice authentication is used by several banks. Deep fake technology can clone a customer’s voice if an attacker steals speech samples from banking systems. Voice-activated spam calls can capture voice data.
Chatbot scams are so common that the U.S. Federal Trade Commission warned against utilizing LLMs and other technologies to create fake videos and voice clones for imposter fraud and financial crime.
Generative AI detects fraud and abuse how?
Strong new fraud review tool. LLM-based assistants executing RAG on the backend can use policy documents to speed up manual fraud reviews.
LLMs assist payments companies identify risks and prevent fraud by predicting customer transactions.
Generative AI lowers transaction fraud by improving accuracy, reporting, investigations, and compliance risk.
Another fraud prevention use of generative AI is data generation. Synthetic data can increase the number of data records needed to train fraud detection models and the variety and sophistication of examples to teach the AI new fraudster methods.
With NVIDIA’s retrieval-augmented generation methodology, companies may build generative AI chatbots and virtual agents. RAG uses natural language cues to obtain data from huge datasets.
Foundation models, the NVIDIA NeMo framework, NVIDIA Triton Inference Server, and GPU-accelerated vector databases leveraging NVIDIA AI workflows can deploy RAG-powered chatbots faster.
The industry focuses safety to prevent generative AI misuse. NeMo Guardrails from NVIDIA ensure LLM-powered intelligent apps like OpenAI’s ChatGPT are accurate, suitable, on topic, and safe.
Open-source software prevents AI-powered app fraud and misuse.
AI fraud detection benefits?
Fraud detection is hard in banking, finance, retail, and e-commerce. Fraud hurts businesses financially and reputationally.
When financial services fraud models overreact and block legal transactions, consumers suffer.
Financial services firms are utilizing more data to construct more advanced models to minimize financial and reputational losses. They also want to lower transaction fraud false positives to improve customer satisfaction and merchant share.
Financial Services Companies Verify Identity with AI
The finance sector is developing AI for ID verification. Deep learning using GNNs, NLP, and computer vision can improve KYC and AML identity verification, cutting costs and enhancing regulatory compliance.
Photo identification of fake driver’s licenses and passports by computer vision. AI checks documents for fraud while NLP verifies data.
Increased KYC and AML standards affect regulation and the economy. In 2022, banks were fined $5 billion for AML, sanctions, and KYC system failures, per the Financial Times.
GPUs for Graph Neural Networks
GNNs are popular for suspicious activity detection. They can check billions of records to see if an account transmitted a transaction to a questionable account.
NVIDIA works with the Deep Graph Library and PyTorch Geometric teams to provide a containerized GNN framework with the latest improvements, NVIDIA RAPIDS libraries, and more to keep users current.
GNN framework containers are NVIDIA-optimized and performance-tested to maximize GPU performance.
The NVIDIA AI Enterprise software platform lets developers scale enterprise installations with RAPIDS, Triton Inference Server, and TensorRT.
GNNs Detect Anomalies Better
Smart fraudsters can outwit fraud detection systems. Use complex transaction chains to hide. Traditional rules-based systems may miss patterns and fail.
GNN representation relies on local structure and feature context. Neighboring nodes aggregate and send messages, spreading edge and node features.
GNNs with multiple graph convolution layers use multihop information in their final node states. Due to their larger receptive area, GNNs can track financial fraudsters’ more sophisticated and longer transaction chains to hide.
Self-supervised GNN training
Big financial fraud detection is tough due to the need to sift through tens of terabytes of transaction data and the lack of labeled fraud data needed to train models.
GNNs learn unsupervised or self-supervised and detect fraud more generally.
GNN developers can use Bootstrapped Graph Latents or negative sampling to pretrain models without labels and fine-tune models with fewer labels to produce strong graph representations. For better inference, utilize this output for XGBoost, GNNs, or clustering models.
Addressing Model Bias and Explainability
Different GNNs offer model explainability. Companies can utilize explainable AI tools and methods to explain how AI models make decisions, preventing bias.
Heterogeneous graph transformer/attention network Attention methods in each layer of GNN models allow developers to identify message paths to a final result.
Without attention, GNNExplainer, PGExplainer, and GraphMask explain GNN outputs.
Leading Financial Firms Profit from AI
Deep learning models utilizing NVIDIA TensorRT on NVIDIA Triton Inference Server boosted fraud detection by 6% for American Express.
Federation learning enhanced BNY Mellon fraud detection accuracy by 20%. BNY’s collaborative fraud detection methodology protects third-party data on NVIDIA DGX platforms with Inpher’s safe multi-party computation.
PayPal wanted a global, real-time fraud detection system to protect customer transactions.By using NVIDIA GPU-powered inference, the company reduced server capacity by 8x and increased real-time fraud detection by 10%.
Swedbank: Sweden’s largest bank saved $150 million by teaching NVIDIA GPU-driven generative adversarial networks to detect suspicious activity, fraud, and money laundering.
