Use of cloud-based and GPU-accelerated AI recommendation systems

Financial services organizations have large amounts of financial data that includes not only account balances or payment transactions but also information such as FICO scores of clients and credit history. Historically, organizations haven’t been able to do much with this data to improve their business. But new automated AI methods make it possible to analyze data in real time. Financial institutions are increasingly using cloud-based artificial intelligence (AI) and machine learning (ML) predictive analytics recommendation systems to analyze huge amounts of financial data. The results of the analysis can be used to make suggestions to customers to improve customer experience, create new products, as well as provide financial institutions with new sources of revenue.

What is the recommendation system?

Recommendation systems, also called recommendation engines, are artificial intelligence systems used to suggest a product, service, or information to a user. Recommendation systems are based on user characteristics, preferences, history and data, so the recommendation is always assigned to a specific customer or user.

Using financial recommendation systems to improve customer experience

Financial services organizations are increasingly using recommendation systems to suggest new products, answer user questions, or analyze customer data to help customers solve problems. according to 2021 Forbes article“Financial services companies can leverage ML/AI to understand customers and lines of business more effectively. For example, many companies use machine learning to power financial product recommendation engines or customer engagement prompts for relationship management teams. It collects personal data, including How someone uses credit, records it and their account balances, and then suggests appropriate products to suit the needs of individuals.”

Building an effective AI recommendation solution

Predictive analysis using AI recommendation systems requires the analysis of huge amounts of data. Many financial institutions have outdated infrastructure, limited AI development budgets, and employees who lack the data science skills needed to implement AI recommendation algorithms. This Forrester report research shows Nearly two-thirds (64%) technical decision makers are not fully confident in their ability to achieve AI goals for their organizations based on existing resources. Machine learning recommendation models require huge computational resources. Legacy infrastructure with CPU-based processing cannot handle the required processing speeds, and moving to a GPU-based infrastructure provides much faster processing and training for machine learning inference models.

according to Forrester Corporation Survey“What organizations need are pre-built, configurable AI cloud services. Cloud AI services allow developers to access deep AI capabilities via APIs to support application innovation without the need for data science expertise.” The cloud includes pre-made AI models, leads to faster deployment time, and gives organizations access to responsibly built and tested AI models.

By using cloud-based AI and machine learning solutions, AI and machine learning solutions remove barriers for financial services organizations in developing AI and machine learning recommendation algorithms. The “Survey of the state of artificial intelligence in financial services“I found that ‘companies are making significant financial benefit from enabling AI across the enterprise.'” More than 30% of respondents stated that AI increases annual revenue by more than 10%, while more than a quarter stated that AI reduces annual costs by more than 10%.

Example of a recommendation system: Helping the customer improve liquidity

The bank’s recommendation system was used to analyze the real-time payment data of the customer’s business. The analysis reveals that a small merchant client has negative liquidity regularly on the third day of every month, so he will not be able to deal with any urgent problem or opportunity that arises at that time due to the cash flow issue. Based on the analysis, the bank can offer the client a liquidity analysis service to help improve cash flow and better anticipate and manage day-to-day operations.

Technology partners provide cloud-based, GPU-accelerated AI recommendation solutions

Microsoft and NVIDIA have a long history of working together to support financial institutions in providing technology to support AI and machine learning solutions such as recommendation systems. Use Microsoft Azure cloud and NVIDIA AI The platform provides scalable and fast resources required to run AI/ML algorithms, routines, and libraries.

The partnership between Microsoft and NVIDIA makes NVIDIA’s powerful GPU acceleration available to financial institutions. The Azure Machine Learning Service Integrates NVIDIA Open Source Rapids A software library that allows machine learning users to speed up their pipelines with NVIDIA GPUs. Added NVIDIA TensorRT Acceleration Library to ONNX Runtime To speed up the conclusion of deep learning. Supports Azure NVIDIA T4 Tensor Core GPUs (Graphics Processing Units)and the NVIDIA DGX H100 system optimized for cost-effective deployment of machine learning inference or analytical workloads.

Microsoft Cloud-Based Solutions for Financial Recommendation Systems

Transfer to Microsoft Azure cloud solution Provides financial institutions with a full suite of computing, networking, and storage resources integrated with workload services capable of handling the recommendation algorithm processing requirements. Microsoft Azure allows developers to build and train new AI models faster through automated machine learning, automated scale cloud computing, and embedded DevOps.

Finding or developing the right financial recommendation system can be a time-consuming process for data scientists. Microsoft provides a file GitHub repository With examples of Python best practices to facilitate Building and evaluating recommendations systems Use Machine Learning Services in Azure.


Historically, financial services organizations did not have an automated way to analyze massive amounts of data. Predictive analysis powered by GPU-based cloud solutions using AI and machine learning recommendation systems can analyze large amounts of fast-moving data in real time. This analysis can produce valuable insights into customer buying behavior, enabling financial institutions to tailor offerings to individual customers. The use of recommendation systems can also provide information that financial institutions can use to help build new business models or revenue streams.