Introduction to Real-Time Payments

Real-time payments (RTP) in the U.S have been gaining momentum in number of users, number of transactions and the growing maximum size of transactions. Even so, the U.S lags well behind such advanced payments networks as those operated by India and Brazil.

Growth of Real-Time Payments

In early February The Clearing House (TCH) announced its first $10 million instant payment over the RTP network which had just raised its maximum payment from $1 million to $10 million. The payment was by Computershare, a global transfer agent, from Bank of New York Mellon (BNY) to another financial institution. The average daily volume on the RTP network has jumped this year, from $909.2 million in January to $2.8 billion in mid-March, said Gregory MacSweeney, spokesman for TCH.

Average Transaction Amount

“Most of the higher value transactions appear to be corporations moving money between accounts for portfolio rebalancing, cash concentration or paying partners or suppliers. For instance, a large retail chain may move funds from its account in the Midwest, to its main account at headquarters, or move funds to another region.” The average transaction amount during that time was $2,510.

Challenges Faced by Fintech Firms

Although the RTP network has seen impressive growth, fintech firms that support it have been a little disappointed, said Erika Baumann, director of commercial banking & payments at Datos Insights. “Alacriti, ACI, Fiserv, Icon Payments and Volante — there’s nobody who isn’t active in this space,” she said. Vendors are working with their client banks to help them avoid disintermediation, but she hasn’t seen a lot of vendors meeting their growth goals. Instead she has seen a lot of disappointment at the pace of adoption, especially with smaller banks.

Regulatory Mandates

“That’s the result when you don’t have mandates from regulators. We kind of go through and get to it when we get to it.” Now RTP is doing great, she added, but it took five years to get to takeoff, and then it doubled volumes quickly. “It started off slow and then now the volumes are pretty impressive.”

Technological Challenges

RTP faced the same challenge that FedNow, the Federal Reserve’s instant payment platform, now faces. Banks are faster to set up facilities to receive instant payments, since receiving is pretty much risk free, but they take longer to start sending. But until a number of banks are sending, what is there for receive-only banks to transact? “RTP has to overcome that hurdle of getting enough banks sending; FedNow has the same hurdle.” She said FedNow has a fraction of the nation’s DDAs participating.

Core Banking Systems

Banks have faced technological challenges in going to real-time payments, said Nadish Lad, head of payments at Volante Technologies. It develops systems for real-time payments, including the system BNY Mellon used to send the first $10 million payment over the RTP network. “To execute that payment, you need every application, every step to be completely real time,” said Nadish. “Then the next problem is 24×7. These banks have core banking platforms which you can call, check the balance and validate the accounts are all good. But at 10 p.m., the core banking platform shuts down for a refresh, and then starts at 10:30 again.” Not exactly 24×7.

Implementation of Real-Time Payments

Banks don’t have to replace their cores to implement real-time, said Baumann. “For smaller banks, many are opting to connect through their cores, but non-core providers like Finzly and Volante and Alacriti are largely core agnostic,” she said. “It is still painful and expensive, but better than a core replacement that can take years, or sometimes a decade.” The big four banks absolutely hold the majority of the market share, she added. “Not only do they have about half of the total deposits in the U.S., our most recent survey of mid and large corporate indicates that about 67% of these businesses are banking with a big four.”

Conclusion

Uneven adoption of real-time payments is a problem for businesses, added Lad. “If the beneficiary is BNY Mellon, then I know they will receive it in real-time. For the other beneficiaries, I’ll have to send by ACH. If I added a day for everyone, no one is going to really benefit.” Lad said banks are still studying real-time payments more than acting. “Every prospect we talk to in the U.S market asks do you have RTP? We say yes, we have a number of clients who have been live for the last six, seven years in your U.S market. Then, when we ask what are their plans? Oh, they say, we are thinking about it. We will probably look at it next year or the year after that.”

FAQs

Q: What is the current state of real-time payments in the U.S?
A: Real-time payments in the U.S have been gaining momentum, but the country still lags behind other advanced payments networks.
Q: What is the average daily volume on the RTP network?
A: The average daily volume on the RTP network has jumped from $909.2 million in January to $2.8 billion in mid-March.
Q: What are the challenges faced by fintech firms in the adoption of real-time payments?
A: Fintech firms have been disappointed with the pace of adoption, especially with smaller banks, and face challenges such as regulatory mandates and technological hurdles.
Q: Do banks need to replace their core banking systems to implement real-time payments?
A: No, banks don’t have to replace their cores to implement real-time payments, but can opt to connect through their cores or use non-core providers.

With AI and automation for impact, our daily lives are being revolutionized in unprecedented ways. From intelligent personal assistants to self-driving cars, artificial intelligence is transforming the world as we know it. As we embark on this journey, it’s essential to understand the profound effects of AI on our society, economy, and individual lives.

Understanding AI and Automation

Artificial intelligence refers to the development of computer systems that can perform tasks that typically require human intelligence, such as learning, problem-solving, and decision-making. Automation, on the other hand, involves the use of machines and computer systems to perform repetitive and mundane tasks, freeing humans from tedious labor. The combination of AI and automation has the potential to increase efficiency, productivity, and innovation.

Types of AI

There are several types of AI, including narrow or weak AI, which is designed to perform a specific task, such as facial recognition or language translation. General or strong AI, which is still in its infancy, aims to create machines that can perform any intellectual task that a human can. Superintelligence, a hypothetical AI system, would surpass human intelligence in all domains, leading to unprecedented breakthroughs and challenges.

The Impact of AI on Industries

AI is transforming various industries, including healthcare, finance, transportation, and education. In healthcare, AI-powered algorithms can analyze medical images, diagnose diseases, and develop personalized treatment plans. In finance, AI-driven systems can detect fraud, manage portfolios, and provide personalized investment advice. Self-driving cars and trucks are revolutionizing the transportation sector, improving safety and reducing emissions.

AI in Healthcare

AI-powered chatbots can help patients with routine inquiries, freeing up medical professionals to focus on complex cases. AI-assisted diagnosis can help doctors identify diseases more accurately and quickly, leading to better patient outcomes. Personalized medicine, enabled by AI, can tailor treatment plans to individual patients’ needs, improving efficacy and reducing side effects.

AI in Education

AI-powered adaptive learning systems can tailor educational content to individual students’ needs, abilities, and learning styles. AI-driven tutoring systems can provide personalized feedback, guidance, and support, helping students to learn more effectively. AI-powered assessment tools can evaluate student performance, providing teachers with valuable insights to improve instruction.

The Social and Economic Implications of AI

As AI and automation replace certain jobs, there are concerns about job displacement and economic inequality. However, AI can also create new job opportunities in fields like AI development, deployment, and maintenance. Governments, educational institutions, and industries must work together to provide workers with the skills needed to thrive in an AI-driven economy.

Job Displacement and Creation

While AI may replace some jobs, it will also create new ones, such as AI trainer, data scientist, and AI ethicist. Workers will need to acquire new skills, such as critical thinking, creativity, and emotional intelligence, to remain relevant in the job market. Governments can implement policies like universal basic income, education, and retraining programs to support workers during this transition.

Economic Inequality

The benefits of AI and automation may not be evenly distributed, exacerbating economic inequality. Companies and governments must prioritize fairness, transparency, and accountability in AI development and deployment, ensuring that the benefits of AI are shared by all. This can be achieved through regulations, taxes, and investments in education and social welfare programs.

AI and Ethics

As AI becomes more pervasive, ethical concerns arise, such as bias, privacy, and accountability. AI systems can perpetuate existing biases if trained on biased data, leading to discriminatory outcomes. Ensuring transparency, explainability, and fairness in AI decision-making is crucial to build trust and prevent harm.

AI Bias and Fairness

AI systems can be designed to detect and mitigate bias, ensuring that decisions are fair and unbiased. Techniques like data preprocessing, algorithmic auditing, and human oversight can help identify and address bias in AI systems. Companies and governments must prioritize fairness, transparency, and accountability in AI development and deployment.

AI and Privacy

AI systems often rely on vast amounts of personal data, raising concerns about privacy and surveillance. Ensuring that AI systems are designed with privacy in mind, using techniques like data anonymization and encryption, is essential to protect individuals’ rights. Regulations like GDPR and CCPA provide a framework for companies to follow, but more work is needed to ensure that AI systems prioritize privacy.

Conclusion

The future is indeed automated, with AI and automation transforming every aspect of our lives. While there are challenges to be addressed, the benefits of AI and automation are undeniable. By prioritizing fairness, transparency, and accountability, we can ensure that the benefits of AI are shared by all, creating a brighter, more prosperous future for humanity.

Frequently Asked Questions

What is AI, and how does it work?

AI refers to the development of computer systems that can perform tasks that typically require human intelligence, such as learning, problem-solving, and decision-making. AI systems use algorithms, data, and computing power to make decisions and take actions.

Will AI replace human jobs?

AI and automation may replace certain jobs, but they will also create new ones. While some jobs may become obsolete, AI will create new opportunities in fields like AI development, deployment, and maintenance.

Is AI a threat to humanity?

AI is not inherently a threat to humanity. However, if not developed and deployed responsibly, AI can perpetuate biases, compromise privacy, and exacerbate economic inequality. By prioritizing fairness, transparency, and accountability, we can ensure that AI benefits humanity.

How can I prepare for an AI-driven future?

To prepare for an AI-driven future, acquire skills like critical thinking, creativity, and emotional intelligence. Stay up-to-date with the latest developments in AI and automation, and be open to learning and adapting to new technologies.

What are the benefits of AI?

The benefits of AI include increased efficiency, productivity, and innovation. AI can help solve complex problems, improve healthcare outcomes, and enhance customer experiences. AI can also create new job opportunities and drive economic growth.

Introduction to Capital One Software

For years, Capital One has stood apart in the financial services sector by positioning itself as a technology company that happens to be in banking. Now, it has taken a bold step further by moving from building world-class technology to selling it.

The Inspiration Behind Capital One Software

This transformation mirrors a playbook made famous by Amazon. When the retail giant developed the infrastructure to support its e-commerce platform, it soon realized the broader applicability of its innovation. The result was Amazon Web Services: a cloud platform born from internal necessity that evolved into a game-changing business. In similar fashion, Capital One’s internal journey to the cloud, and the accompanying technological challenges it overcame, have birthed Capital One Software, a commercial arm offering its hard-earned solutions to other enterprises.

Building on 23 Years of Experience

Ravi Raghu, President of Capital One Software, has been with the company for more than 23 years. In that time, he’s led multiple businesses and served on the executive committee, reporting directly to CEO Rich Fairbank. His career has been defined by both entrepreneurial initiative and a focus on building high-performing teams. That combination proved critical when Capital One launched its software business in 2022.

The Birth of Capital One Software

“Right from the founding days of our company, we realized that data and technology have been at the heart of everything we do,” Raghu reflected. “When you peel back the onion, it always comes back to data.” Capital One’s shift to the public cloud was pivotal. It wasn’t just a move to modernize infrastructure; it exposed gaps in existing enterprise-grade data management tools. That led the company to build its own solutions and recognize the market potential of those innovations.

The Journey to Becoming a Technology Company

“Our journey of becoming a technology company that happens to do banking has been a decade in the making,” Raghu explained. “As we went all in on the public cloud, we realized we had to build capabilities around data management that just didn’t exist off the shelf.” This internal necessity led to the development of Slingshot, Capital One Software’s first commercially available software product aimed at optimizing the use of Snowflake. Early validation came not just from internal success, but from Snowflake itself and its other enterprise clients.

Capital One as Alpha Client

In Raghu’s words, “Capital One is the Alpha client.” The software used internally is road-tested by teams led by Rob Alexander, Capital One’s longtime CIO, and then refined for commercial release. “My job is to take that capability and morph it,” Raghu said. “We ensure it’s not bespoke to Capital One, but applicable to any enterprise, with configuration flexibility and scalability built in.”

The Evolution to Data Management and Security: Introducing Databolt

While Slingshot was focused on Snowflake optimization, conversations with early users revealed another pressing challenge: data security. This insight led to Capital One Software’s second product, Databolt. “There’s a tsunami forming, and it’s driven by three macro forces,” Raghu said. “The rise of data breaches, increasing privacy regulations and the explosion of generative AI.” Databolt’s primary innovation lies in tokenization. As Raghu described it, “Instead of locking away sensitive data, tokenization gives you a surrogate that preserves format but is indecipherable. That means your systems and models work seamlessly without compromising security.”

Supporting Generative AI at Scale

Raghu sees a major role for Databolt in enabling secure AI adoption. “Traditional security says ‘lock everything down,’ but generative AI demands access to as much data as possible,” he underscored. “With tokenization, you can let your models learn without actually exposing the sensitive data.” Raghu predicts that AI’s rise will make robust, adaptable data security more essential than ever. “We believe tokenization will be what enables generative AI to scale responsibly,” he said.

Early Customer Success

Among Databolt’s early adopters is Early Warning Services, best known as the organization behind Zelle, the service that enables individuals to electronically transfer money from their bank account to another registered user’s bank account. With a complex, multi-bank data environment, they needed top-tier security and performance. According to Raghu, “They’ve been blown away by our performance.” Capital One’s existing relationships through Slingshot have also opened doors for Databolt, offering a natural entry point for further adoption.

What Comes Next

Looking ahead, Raghu envisions a suite of tools across the entire data lifecycle, which might include data publishing, governance, access control and infrastructure management. “We’re deeply tuned into customer feedback,” he said. “That’s what led to Databolt, and it’s how we’ll decide what comes next.” Though Capital One is just getting started with its commercial software journey, the ambition is clear: to turn its internal expertise into external impact, helping enterprises navigate complex data environments with confidence.

A Virtuous Cycle

“We [at Capital One Software] stand on the shoulders of all the tech transformation that Capital One has done,” Raghu reflected. “Now we get to build for the world. And as we learn from the world, we bring those insights back to strengthen Capital One. It’s a virtuous cycle.”

Conclusion

Capital One Software is poised to make a significant impact in the technology sector, leveraging its expertise in data management and security to help enterprises navigate the complexities of the digital landscape. With its innovative products, such as Slingshot and Databolt, and its commitment to customer feedback, the company is well-positioned for success.

FAQs

Q: What is Capital One Software?
A: Capital One Software is a commercial arm of Capital One that offers software solutions to other enterprises, leveraging the company’s expertise in data management and security.
Q: What is Slingshot?
A: Slingshot is Capital One Software’s first commercially available software product, aimed at optimizing the use of Snowflake.
Q: What is Databolt?
A: Databolt is Capital One Software’s second product, focused on data security and tokenization.
Q: What is tokenization?
A: Tokenization is a process that replaces sensitive data with a surrogate that preserves format but is indecipherable, allowing systems and models to work seamlessly without compromising security.
Q: What is the goal of Capital One Software?
A: The goal of Capital One Software is to turn its internal expertise into external impact, helping enterprises navigate complex data environments with confidence.