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Since 1987 - Covering the Fastest Computers in the World and the People Who Run Them
Business-to-business (B2B) payments power the global economy. The business-to-business (B2B) payments ecosystem is massive, with $25 trillion in payments flowing between businesses each year.
Traditional B2B transactions were processed by banks sending customer payment transactions through the automated clearing house computer service which could take up to three days to clear and be processed. When a customer uses a credit card, merchants must pay fees, and it can take anywhere from 24 hours to three days to show up in the merchant account. Card-not-present payments, such as those made online or over the phone, are costly for merchants, requiring manual entry and approval. All of these payment methods have disadvantages for the customer, financial institution, or merchant.
Digital disruption in the financial services industry is a having major impact on both payment systems and customer expectations. The swift rise in technologies such as mobile banking apps, online payment systems, and non-traditional FinTech companies has changed what customers expect. Financial institutions and merchants want to speed up the payment process to meet customer expectations.
According to a McKinsey study, “More than three-quarters of Americans use some form of digital payment, which we define as any of the following: browser-based and in-app online purchases, in-store checkout using a mobile phone and/or QR code, and person-to-person payments.”
The digital age requires a real-time payment solution where payments are processed instantaneously and safely. Process automation using GPU-based cloud solutions along with artificial intelligence (AI) and machine learning (ML) solutions have the potential to power a real-time payment processing system to meet the payment needs of customers, merchants, and the financial services industry.
Financial services challenges in implementing real-time payment processing
Over the next few years, banks are looking to automate up to 25% of their processes to remain competitive and free employees to focus on more high-value efforts. A Gartner Market Guide for Digital Commerce Payment Vendors noted that, “B2B merchants have moved quickly toward digital commerce and are increasingly embracing the need for a more consumer-like, truly omnichannel, digital buying experience.”
Financial institutions face challenges in moving to a real-time payment processing solution. Many traditional banks rely on legacy systems using central processing units (CPUs) and their infrastructure cannot support the massive processing and storage requirements of processing high volumes of payment data in real-time. Financial information on checks and other financial forms must be manually reviewed and processed by staff. Payments made online or over the phone are costly for merchants requiring manual entry and approval.
Moving toward a real-time payment solution
According to this Forrester report, 84% of technical decision makers see significant opportunities with Al and believe they must implement Al to maintain a competitive advantage in their industry. However, 51% of the business leaders indicate their organization does not have the right resources to add AI capabilities.
Using cloud-based, GPU accelerated AI and ML solutions for payment processing removes barriers financial service institutions face in moving toward real-time payments systems. Training ML models and running AI in running payment transactions require huge computational resources, and processing transaction data is significantly faster on GPUs.
The Forrester survey found that “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.” Cloud AI services include prebuilt AI models, faster deployment time and greater scale of implementation across multiple applications.
AI and ML models automate many of the processes used in payment processing. ML models can extract information from financial documents that contain both unstructured and semi-structured data. By leveraging Al, machine learning, and analytics, even large amounts of fast-moving payment data can be interpreted in context.
The data created by the real-time payment (RTP) process can be pulled into a single view on cloud-based systems. AI analysis on the data can yield valuable insights on client payments and buying behavior. Organizations can use the RTP data analysis to make personalized offers of products or services. AI and ML analysis on a cloud-based GPU-accelerated platform can also be used to find the opportunities most likely to create differentiation, generate revenue, and drive growth.
Technology partners provide cloud-based, GPU-accelerated AI payment solutions
Microsoft and NVIDIA have a long history of working together to support financial institutions in meeting challenges such as supporting real-time payments. Using the Microsoft Azure cloud in conjunction with NVIDIA AI solutions provide 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. Azure Machine Learning 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), which are optimized for the cost-effective deployment of machine learning inferencing or analytical workloads.
For example, GPU-accelerated computing deep learning applications trained on NVIDIA V100s, provided 55x faster performance than CPU servers and 24x faster performance during inference for one financial processing firm.
How cloud-based solutions aid real-time payments
Many banks still use batch processing and older mainframe systems which can be complicated to update and maintain. Moving to the Microsoft Azure cloud solution provides financial institutions with a complete set of computing, networking, and storage resources integrated with workload orchestration services to aid in integrating a real-time processing solution. Microsoft Azure allows developers to build and train new AI models faster with automated machine learning, autoscaling cloud compute, and built-in DevOps.
Microsoft solutions provide fast and affordable payment processing. For example, Clearent provides global credit card processing services for merchants of all sizes and wanted a data solution that could meet its ever-increasing demand. Clearent chose the Microsoft Azure SQL Database hyperscale service tier to support its workload of dozens of microservices, manage real-time data ingestion, and process millions of credit card transaction records every month. Clearent integrates data from dozens of different sources into an operational data store and enterprise data warehouse within Azure before it analyzes the information using Microsoft Power BI.
Payment transactions power the global economy but traditional payment processing methods can’t meet processing requirements due to a delay in processing time and many manual human intervention steps.
Customers now expect digital payment transactions to be processed instantaneously and securely. Real-time payment process automation supported by GPU-based cloud solutions along with AI and ML solutions can meet the payment needs of customers, merchants, and the financial services industry.
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