Overview

With the rapid development of financial technology, the financial industry faces challenges in data processing complexity, limitations of Large Language Models (LLMs), rapid market changes, and infrastructure challenges. The diversity of financial statements, real-time analytics, risk assessments, and market information demands higher requirements for technical solutions. General LLMs show limitations in financial terminology and high-precision tasks (such as risk management and market predictions). Meanwhile, rapid market changes require frequent model updates to maintain accuracy, while computing demands from edge devices to data centers present challenges in load balancing and large-scale computing.

Business Challenges

The financial sector’s high complexity and specialized nature present several challenges in recent years:

  • Diverse and Complex Financial Data Processing: Including financial statements, real-time data interpretation and analysis, risk assessment, and dynamic market information. Due to the comprehensive nature of dynamic market information, customized system adjustments are particularly necessary.
  • Knowledge Limitations in General Large Language Models (LLMs): General models cannot accurately understand financial terminology and complex financial concepts, leading to inadequate performance in high-precision tasks such as risk management, customer analysis, and economic forecasting.
  • Insufficient Knowledge Update Frequency: The rapidly changing financial market requires constant AI model updates to maintain accuracy.
  • Technical Infrastructure Challenges: Computing demands from edge devices to data centers, including model load balancing and large-scale parallel computing.

Solution Overview

To address these challenges, the Smart Financial AI solution integrates with Infinitix AI-Stack technology to provide a comprehensive technical framework. Key features include:

AI Model Optimization and Rapid Deployment

  • Utilizes AI-Stack’s GPU partitioning and multi-card aggregation technology for efficient model training and inference
  • AI-Stack’s distributed training technology significantly reduces financial AI model training time

Modular Application Support

  • Combines training and learning with financial terminology databases, with fine-tuning capabilities for specific scenarios (such as credit assessment, financial advice, risk prediction)
  • LLM technology layer supports layered agents (AI Agents) and multimedia processing (voice, image, text)

Intelligent Resource Management

  • Achieves 90% GPU utilization through AI-Stack GPU resource scheduling, reducing resource waste from 30% to 10%
  • Integrates multi-cloud resource management and MLOps, supporting rapid deployment of development environments
  • Implements internal (HR, risk control) and external (insurance, wealth management) applications through Knowledge Base and AI Gateway

Technical Infrastructure Support

  • AI-Stack supports the full range of NVIDIA and AMD GPUs, enabling seamless support from edge devices to data centers
  • AI-Stack’s energy monitoring ensures efficient and low-carbon operation of computing resources

Solution Value

  • Rapid Application Implementation: Reduces AI development environment setup time from 2 weeks to 1 minute, improving deployment efficiency; supports quick adaptation of diverse scenario containers and images, accelerating AI model training and inference.
  • Efficient Resource Utilization: Through unique GPU partitioning technology, increases investment benefits by 10 times while integrating cross-brand GPU computing resources efficiently, achieving stable large-scale deployment and high-performance operation.
  • Professional Performance Enhancement: Financial models provided according to enterprise needs will have more accurate risk control, database analysis, and enhanced ability to predict future markets.
  • Simplified Operations: Utilizes AI-Stack’s platform integration capabilities to reduce maintenance burden, allowing teams to focus on model development and applications, with continuous model updates to address rapid financial market changes.

Solution Architecture

Architecture LayerDescriptionExamples
Application LayerFinancial-specific application layer, covering multi-scenario financial applications, AI management model load balancing, API integration, traffic management, and monitoring logs.Investment model recommendations, risk control, credit assessment, credit authorization, asset management, payment and settlement, insurtech, blockchain, digital finance, corporate finance, behavioral finance analysis, intelligent loan management, education and training, compliance and anti-money laundering
Technology LayerAI-Stack Platform: Model training automation scheduling and application deploymentNatural Language Processing (NLP), speech/semantic recognition, image and signal processing technology, financial big data, deep learning, time series analysis and prediction models, multimodal learning, Knowledge Graphs, Reinforcement Learning, privacy computing technology, edge computing and low-latency AI
Development LayerAI-Stack Platform: Provides developers with development tools and AI ecosystem resources, simplifies software development process collaboration, compatible with multiple images and development container environments.Open source software, Large Language Models (LLM), AI ecosystem, model fine-tuning, multimodal processing, RAG file retrieval
InfrastructureAI-Stack Platform: GPU management control and computing power scheduling, and HPC distributed computing scheduling technology, supporting multi-node high-performance training; provides resource monitoring, permission management, and other functions.Banking systems, intelligent devices, bank databases, on-premises servers, AI chips, GPU