Machine Learning in Financial Services

Harness the power of AI as an engine of growth, and efficiency to eliminate risk, enhance and personalize financial services. Expand market share, deepen customer relationships, and compete for and win new business — all while efficiently complying with regulations and mitigating fraud.

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AI in Financial Services

Helping transform organizations with AI technology. Leverage behavioral data to provide tailored financial services and mitigate risk. Unlock the power of AI for trading, risk management, understanding consumer and market behavior, reducing transaction costs, streamlining operations, and delivering new products and services. Automate routine financial processes and reduce the need for manual intervention.

Discover the benefits of AI in financial services.

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Challenges

Insufficient access to scalable compute resources

Gaining access to compute resources and orchestrating workloads requires extensive experience in tooling that becomes a costly distraction for data scientists and ML engineers. Infrastructure bottlenecks reduce velocity and precision and increase model ops friction, time to market, and operational risk.

Lack of standardization, process, & centralized hub

Collaboration among distributed research teams without a unified tool is a liability. Workflows that lack a standardized process and a unified hub lead to re-work and hinders the ability for data scientists to find, understand, build-on, and contribute to the various models in R+D and production.

Consumed by menial & redundant tasks

Data scientists and machine learning engineers spend roughly 25% of their time developing models. This means that 75% of their time is spent on costly distractions related to tooling and infrastructure. A end-to-end ML pipeline enables a rapid model delivery cycle without the need to perform cumbersome routine tasks.

Gradient can help

Conduct AI without bottlenecks

Experiment faster and deliver more breakthroughs. Remove DevOps pain with independent access to compute. Use the right tools for the job. Share and repurpose work across the team. More easily get models from development into production.

Build a modern AI practice

Create an organizational capability out of machine learning. Gain visibility into work happening across the organization. Accelerate the data machine learning management lifecycle. Reduce regulatory and operational risk while maintaining and updating models more frequently, with greater precision.

Become your unified AI hub

Support data science without sacrificing governance or security. Future-proof your machine learning stack. Gain transparency with complete model reproducibility. Offer a centralized platform for collaboration. Reduce model ops friction for rapid model delivery and iteration.

AI Applications

High Value Use Cases in Financial Services

  • Risk Analysis
  • Algorithmic trading
  • Reduce financial crime
  • Build more accurate credit models
  • Price & demand forecasting
  • Regulatory reporting automation
  • Precision pricing

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