Federated Learning Ready:
Future-Proofing Your Financial Institution

Let's explore how becoming Federated Learning-ready can revolutionize your data strategy, streamlining your operations, enhancing data privacy, and providing a competitive edge that directly contributes to your bottom line.

Federated Learning Recap

Federated Learning allows multiple entities to collaboratively train machine learning models on their local data without sharing raw data. This practice safeguards data privacy while extracting valuable insights, making it particularly attractive for privacy-conscious sectors like finance. By becoming Federated Learning ready, you can reduce monitoring costs and risks through improved predictive modeling and significantly reduce operational costs associated with data processing and model development.

Becoming Federated Learning Ready

Preparing for Federated Learning involves three key elements: data orchestration, a machine learning database (or feature store), and the use of declarative machine learning. Implementing these infrastructure elements not only enables you to leverage Federated Learning but also significantly improves your existing data management and machine learning practices.

Near-term benefits

Embracing Federated Learning signifies more than just a technological upgrade. It’s a strategic move towards future-proofing your financial institution. While the overarching goal might seem daunting, the near-term benefits in cost-saving, risk mitigation, and improved predictive capabilities from getting federated learning yields significant ROI. Moreover, with the global industry gravitating towards privacy-preserving technologies, getting a head start in Federated Learning gives your organization an advantage in the race.

In the short term, getting ready for federated learning will:

Reduce time and resources spent on manual data handling
Improve the speed of decision-making
Foster internal collaboration and reduce redundant work
Accelerate your model development process
Minimize the risks of overfitting and model drift
Increase robustness and reliability of your models
Reduce false positives and false negatives in your models

Requirements drive value

Data Orchestration: Robust data orchestration ensures consistent data updating, cleaning, and maintenance. Imagine your investigators and data scientists having instant access to structured, reliable data instead of trawling through disparate sources..
Machine Learning Database (MLDB): An MLDB, or feature store, facilitates faster, more effective model development and deployment. Picture a shared platform where your data scientists can easily access and reuse machine learning-ready features.
Declarative Machine Learning (DML): DML not only improves model development efficiency but also enhances compliance with regulatory standards - a crucial benefit for the financial industry.

Significant discount with public funding

As an early stage company pursuing an innovative technology, Finterai has access to strong public funding schemes, which allows us to provide federated learning readiness as a service at a steep discount relative to market rates. For your organization, that means a lower cost basis of investment, yielding an even greater return on your investment in more efficient technological infrastructure.