Financial services organizations face increased competition for customers from technologies such as FinTechs, mobile banking applications, and online payment systems. To meet this challenge, it is important for organizations to have a deep understanding of their customers.

Organizations are increasingly using cloud-based, GPU-accelerated artificial intelligence (AI) and machine learning (ML) predictive know your customer (KYC) analysis and recommender systems. AI recommender KYC analysis provides cross-selling business opportunities by suggesting financial products based on a customer’s credit history, credit score, and account balances. The AI solutions can help equip financial services companies with the ability to accelerate revenues, create operational efficiencies, and enhance customer experiences.

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.

How AI KYC recommender systems change the financial landscape

Many firms are using AI to power financial product recommendation engines or customer engagement prompts to relationship management teams. NVIDIA’s “State of AI in Financial Services 2022 Trends” survey found that 91% percent of financial services companies are driving critical business outcomes with investments in Al. The survey noted that KYC and fraud detection showed the largest percentage implementation gains between 2021 and 2022. According to the survey, “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.”

AI recommender systems must analyze massive amounts of data which requires huge computational resources. Many financial services organizations have legacy infrastructure with CPU-based processing that cannot handle the processing speeds required, so leveraging a GPU-based infrastructure provides much faster results and better ROI.

This 2022 Forrester report found that more than 50% of technical leaders don’t believe their organizations have the right resources in place to add Al capabilities to their applications. The Forrester report states, “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.

Recommender system example: Differentiating customers and increasing their value

Financial services organizations have traditionally been unable to gain much value from their data to improve business because they have legacy infrastructure that can’t handle the processing speeds required to run ML inference models. But new cloud-based tools, GPU-accelerated infrastructure and AI analytics allows organizations to analyze large amounts of data in real-time.

The AI analysis and use of recommender systems can yield valuable insights on client buying behavior, enabling more accurate predictions about customer needs. Organizations can use the predictive AI results to personalize offers to customers. For example, based on personalized AI analysis, a financial services firm could initiate a retention conversation with one customer while offering a helpful product recommendation to another customer. This service is not only beneficial to customers but can help increase both revenue and customer retention.

Technology partners provide cloud-based, GPU-accelerated AI KYC 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 the NVIDIA AI platform and Microsoft Azure cloud provides scalable, accelerated resources needed to run AI/ML algorithms, routines, and libraries.

The Microsoft and NVIDIA partnership 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.

According to Kevin Levitt, NVIDIA Global Business Development Lead for Financial Services, “While recommender systems have traditionally powered personalized content and product recommendations to consumers on streaming and e-commerce platforms, recommender systems also open new cross-sell and up-sell opportunities for banks and insurance companies. By driving the next best action for every consumer to take, recommenders–such as those built on the NVIDIA Merlin framework can increase conversion by providing accurate and timely personalized messages and interactions, thereby improving customer loyalty and satisfaction.”

Microsoft cloud-based solutions for financial KYC recommender systems

Financial institutions need a complete set of computing, networking, and storage resources integrated with workload services capable of handling the requirements of recommender algorithm processing. The Microsoft Azure cloud solution provides financial services organizations with the tools needed to analyze internal and external data and turn it into analytical and predictive power using cloud and AI innovations.

Developing the right financial recommender system can be a time-consuming process for data scientists. To help create and modify recommender systems, 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

To compete with non-traditional banking services, financial services organizations need an automated way to analyze their massive amounts of data to better understand their customers and the services they need. Predictive analysis supported by cloud-based GPU-accelerated solutions using AI and ML recommender know your customer (KYC) systems can analyze large amounts of fast-moving data in real-time.

AI KYC recommender system analysis can yield valuable insights on client buying behavior to suggest specific services and personalized offers to individual customers. Using AI recommender KYC systems enhances the customer experience and helps financial institutions reduce costs, and cross-sell services to build new business models.