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

What is a recommender system?

Recommendation systems, also called recommendation engines, are AI systems used to suggest a product, service, or information to a user. Recommender systems are based on user characteristics, preferences, history, and data, so the recommendation is always personalized for a particular customer or user.

Using financial recommender systems to improve the customer experience

Financial service organizations are increasingly using recommendation systems to suggest new products, answer user questions or analyze customer data to help customers solve problems. According to a 2021 Forbes article, “Financial services companies can leverage ML/AI to understand their customer and business lines more effectively. For example, many firms are using machine learning to power financial product recommendation engines or customer engagement prompts to relationship management teams. It combines personal data, including how someone uses credit, their scoring and balances and then suggests suitable products that will fit the individuals’ needs.”

Building an effective AI recommender solution

Predictive analysis using AI recommender systems requires analysis of massive amounts of data. Many financial organizations have legacy infrastructure, limited budgets for AI development and staff that lack data science skills needed to implement AI recommender algorithms. This Forrester report research shows that “roughly two-thirds (64%) of technical decision-makers are not fully confident in their ability to meet their organization’s AI goals based on current resources.” Training ML recommender models requires huge computational resources. Legacy infrastructure with CPU-based processing cannot handle the processing speeds required, moving to a GPU-based infrastructure provides much faster processing and training for ML inference models.

According to the Forrester survey, “What organizations need are prebuilt, configurable AI cloud services. Cloud AI services allow developers to access a depth of AI capabilities via APIs for fueling application innovation without requiring data science experience.” Moving to a cloud-based AI solution that includes pre-built AI models, results in faster deployment time, and gives organizations access to AI models that have been responsibly built and tested.

Using cloud-based, GPU accelerated AI and ML solutions removes barriers financial service institutions face in developing AI and ML recommender algorithms. The “State of AI in Financial Services survey” found that “Companies are experiencing significant financial benefit from enabling AI across the enterprise. Over 30 percent of respondents stated that AI increases annual revenues by more than 10 percent, while over a quarter stated that AI is reducing annual costs by more than 10 percent.

Recommender system example: Helping a customer improve liquidity

A bank’s recommender system was used to analyze real-time payment data for a customer’s business. The analysis reveals that a small merchant customer regularly has negative liquidity on the third day of every month, so they would be unable to deal with any urgent problem or opportunity arising at that time due to the cash flow issue. Based on the analysis, the bank could offer the customer a liquidity analysis service to help improve cashflow and better anticipate and manage day-to-day operations.

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

Microsoft and NVIDIA have a long history of working together to support financial institutions in providing technology to support AI and ML solutions such as recommender systems. Using Microsoft Azure cloud, and the NVIDIA AI platform provides scalable, accelerated resources needed 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 the NVIDIA open-source RAPIDS software library that allows machine learning users to accelerate their pipelines with NVIDIA GPUs. The NVIDIA TensorRT acceleration library was added to ONNX Runtime to speed deep learning inferencing. Azure supports NVIDIA’s T4 Tensor Core Graphics Processing Units (GPUs), and the      NVIDIA DGX H100 system which are optimized for the cost-effective deployment of machine learning inferencing or analytical workloads.

Microsoft cloud-based solutions for financial recommender systems

Moving to the Microsoft Azure cloud solution provides financial institutions with a complete set of computing, networking, and storage resources integrated with workload services capable of handling the requirements of recommender algorithm processing. Microsoft Azure allows developers to build and train new AI models faster with automated machine learning, autoscaling cloud compute, and built-in DevOps.

Finding or developing the right financial recommender system can be a time-consuming process for data scientists. Microsoft provides a GitHub repository with Python best practice examples to facilitate the building and evaluation of recommendation systems using Azure Machine Learning services.

Summary

Historically, financial service organizations did not have an automated way to analyze their massive amounts of data. Predictive analysis supported by GPU-based cloud solutions using AI and ML recommender systems can analyze large amounts of fast-moving data in real-time. This analysis can yield valuable insights on client buying behavior, enabling financial organizations to personalize offers to individual customers. Using recommender systems can also provide information that financial institutions can use to help build new business models or sources of revenue.