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2023: Real-Time Payments, Instant Payments, CDBCs, Interoperability and Payment Pre-Validation
October 16, 2023
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2023 – yet another exciting year coming up for the payments industry!

Year after year, the rate of change in the payments world continues to accelerate. With a changing macroeconomic environment, increasingly demanding customers, hyperactive regulators, important market initiatives, as well as central banks experimenting with digital currency, 2023 is sure to be both challenging and exciting for the industry 

So, what do we expect for this year?  

This article is part of the After Hours by RedCompass Labs series. 
Where the best and brightest in the financial services industry tell what they really think about payments

Response to increasing interest rates 

Central banks across the world are increasing interest rates in an attempt to keep inflation under control. As a result, venture capital has slowed down in Europe and the United States, as investors have started to become more cautious. Fintechs, previously trading at astronomic valuations compared to their revenues, have taken a significant beating. Examples include Klarna, which was forced to lay off 10% of its staff after seeing its valuation sink from $45.6 to $6.7 billion in just a year, and checkout.com, previously Europe’s most valued startup, seeing its valuation sink from $40 to $11 billion. Will we continue to see fintechs folding or scaling back their ambitions - or, will banks seize the opportunity to snap up the tech and talent fintechs bring to the table at discounted acquisition prices? Either way, 2023 looks like it will be a challenging year for fintechs in all stages of growth. 

Of course, not only fintechs and startups are affected by increasing interest rates. And more particularly, when those rates are combined with a higher level of risk of late or non-payments and decreased access to credit, corporate treasurers will need to bolster their focus on working capital optimisation. As a result, we expect to see increased focus on solutions that will help in this regard, such as cash forecasting, cash pooling, factoring, trade financing, as well as solutions that can help render request for payment and dunning more efficient.. 

As liquidity becomes an increasingly scarce commodity, banks will seek to reduce their liquidity trapped in clearings and nostro accounts by rationalising their clearing and network strategies. Whereas positive interest rates could provide an additional source of revenue for financial institutions in the form of float, we would expect payment service users to increasingly make use of real-time payment methods in order to reduce the working capital impact from liquidity in transit.   

The proliferation of real time payments 

Real time payments are here - and they’re here to stay. With 79 countries across the globe having at least one form of real time payment system, domestic low-value payments are moving faster than ever. A few key highlights to watch out for in 2023 are the go-live of FedNow in the United States, which is set to become a worthy competitor of The Clearing House, a Real Time Rail (RTR) in Canada, as well as the looming instant payments legislation in the European Union, which will render instant payments ubiquitous throughout the SEPA region. 

These real-time rails are set to provide not only an accelerated payments experience for payers and payees, but also, when combined with overlay services such as proxy databases (eg Bizum) and rich request for payment functionality (eg. TCH-RTP), can support new use cases and user journeys. As transaction limits continue to increase on real-time rails, we expect their rails to become increasingly important for cash management offerings, leveraging the 24/7 nature of the schemes to perform physical pooling operations. 

Unfortunately, scammers are making use of these new payment methods as well, and we see that authorised push payment fraud is on the rise. Regulators, as well as the private market, have started to catch on, and are seeking to limit the financial and mental strain inflicted on victims of APP scams. 

Payment pre-validation 

One method communities of payment service providers (PSPs) are using to combat authorised push payment fraud, and particularly invoice fraud, is through the development of Confirmation of Payee Schemes.  

When using such a scheme, the payer’s PSP can contact the PSP of the beneficiary and validate that the name and the account number correspond. This allows the payer to be alerted of potential frauds, for instance, when an invoice has been doctored to have the payment redirected towards a money mule account. This account validation service, often offered at no cost to retail customers, can be monetised when distributed to corporates, who can use it to avoid being defrauded when setting up direct debit mandates or onboarding suppliers. An additional benefit of CoP schemes is that they can reduce the number of non-fraudulently misdirected payments (for instance, towards closed accounts), reducing the workloads associated to returning and reconciling such payments for PSP back offices and payment service users alike. 

Although such schemes have proven to be quite effective in preventing APP in markets such as the United Kingdom and the Netherlands, history has shown that fraudsters will flock towards payment service providers that are not connected to the CoP scheme. Regulators have stepped in, with the UK’s payment systems regulator directing an additional 400 firms to introduce the protective measure, and the European Commission proposing to add a CoP obligation to all initiated instant payments through the Instant Payments legislation. 

Interoperability of CoP schemes is set to remain a challenge, as there are many domestic market practices that are hardly interoperable, and although most schemes are (loosely) based on the ISO 20022 model, there is no standardized market practice on how the XML-based format can best be translated to json. Players such as SWIFT, JP Morgan (Onyx-Confirm), iPiD, and Surepay are rising to the challenge and are working on developing interoperability services. We expect rapid evolution in this highly competitive and dynamic space in 2023.  

We see that payers are starting to expect upfront validation of their payments, not only regarding payments data, but also the end-to-end fees and FX rates they can expect. We see a renewed interest in guaranteed OUR payment methods, as well as services that allow all elements of a transaction to be pre-validated, such as SWIFT Go. The correspondent banking world is finally starting to catch up with the user experience provided by challengers such as Wise and 👉Ripple 

Revenues under pressure 

The impact these challenges have had on the payments industry should not be underestimated. They have systemically undercut financial institutions in their payment fees, instead seeking to generate revenues from FX margins. Today, customers are becoming savvier, and are better equipped to compare offers from different FX providers. This has caused downwards pressure on per-transaction revenues, especially from top-tier corporates. 

In order to safeguard profitability, financial institutions will need to choose whether to invest in capabilities that increase their straight-through processing rates and seek additional payment volumes through offerings such as embedded banking -  or, choose to partner with payments-as-a-service providers that have scale advantages. We expect artificial intelligence to play a significant role in the improvement of STP rates, as well as analytics tools capable of identifying sources of STP breakage. 

As the regulatory burden for payment service providers continues to grow in 2023, we would expect a sizeable number of smaller financial institutions to divest their payment processing activities, choosing instead to implement revenue-sharing models with larger financial institutions or payments-as-a-service providers. 

 Banks will also need to seek additional revenue streams from payment activities through the development of compelling value-added services that can help their corporate customers improve their insights or automate their internal processes.  

Interoperability 

Perhaps one of the most exciting predictions on the horizon for 2023 is that banks will finally start to see the fruit of their investment in ISO 20022 programmes. With SWIFT, as well as the clearing systems of multiple major currencies, set to go live on ISO 20022 in 2023 (EUR, GBP, AUD, CAD, and USD, to name a few), we will finally start to see some live cross-border ISO traffic. But, it’s just the start. Now that the foundations have been laid, and in order to amortise their investments, payment service providers will need to find ways to optimise their efficiency by leveraging structured data and the interoperability provided by a common global payments language. In any case, clearing and settlement mechanisms have already started to explore the exciting prospect of interoperability, with IXB set to build a transatlantic bridge for low value payments. The use of local rails for low-cost money remittances is set to remain a hot topic, which will likely be further accelerated thanks to ambitious projects such as Mojaloop. 

As banks get over the initial hurdle of going live with ISO 20022 payments, they will need to prepare for increased volumes of data-rich payments, even as the PMPG guidelines advising banks to restrict the initiation of rich payments expire in November. Additionally, they will need to start thinking about how they will handle structured addresses (especially creditor addresses!) and additional identifiers such as LEI, as many real-time gross settlement mechanisms will start mandating this from 2025.  

Central Bank Digital Currencies 

Central Bank Digital Currencies are starting to become a reality. The European Institutions are expected to provide a legal foundation for the Digital Euro to become a reality in the second quarter of 2023. Despite the initial reluctance to include international interoperability and programmable money, the Eurogroup has started to open up to those functionalities, which are set to add significant value for users of the digital currency. The European Central Bank has been hard at work at developing a proof of concept and is expected to make a decision about launching a production Digital Euro in the fall of 2023 

The United States, despite having a slow start in their CBDC investigations, is catching up, and the topic is high up on the list of priorities for the US Department of Treasury. As such, we expect the Fed, which had expressed their hesitance to issue CBDC without clear congressional and executive support, to kick their exploration efforts in order not to fall behind their European and Asian counterparts. In the meantime, the Bank of International Settlements is running a wide range of projects to explore various aspects of CBDC and CBDC interoperability. 

Conclusion 

2023 promises to be an exciting year in payments, and RedCompass Labs is excited to continue accompanying our clients in defining and delivering their strategy to address the changing payments paradigm.

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🤖Can Decentralized AI Stop Big Tech from Owning the Future of Robotics?🤖
The race to build the future of robotics is no longer just about robots. It's about who controls the intelligence behind them.
 
Over the last three years, a small group of companies has emerged as the backbone of the AI revolution. Microsoft provides cloud infrastructure. NVIDIA supplies the chips. Google, OpenAI, Anthropic, Meta, and others develop the models. Together, they control much of the compute, data, and software stack powering modern AI.
 
Now that AI is moving into the physical world, many are asking a bigger question:
 
Will these same companies end up controlling robotics too?
 
It's a valid concern.
 
The latest generation of robots relies on enormous amounts of compute, simulation, training data, and foundation models. Many robotics startups today are built on infrastructure provided by large technology companies. NVIDIA's Omniverse is becoming a key simulation environment for robot training. Microsoft Azure is powering the training of robotics foundation models. Physical AI startups increasingly depend on hyperscale cloud infrastructure to train and deploy intelligent systems. Recent partnerships across the industry show just how central Big Tech has become to robotics development.
But while Big Tech is building the highways, another movement is trying to ensure it doesn't own every destination.
 
That movement is decentralized AI.
 
Why Decentralized AI Exists
 
The idea behind decentralized AI is simple. Instead of a handful of companies owning the models, compute infrastructure, data pipelines, and intelligence networks, these resources are distributed across thousands of participants.
 
This means anyone can contribute compute, contribute models, validate outputs and can participate.
The most visible example today is the decentralized AI network known as Bittensor (@bittensor). The network has evolved into a large ecosystem of specialized AI markets called subnets, where participants compete to provide useful machine intelligence and are rewarded based on performance. Rather than relying on a single company, intelligence is generated and validated by a distributed network of miners and validators.
 
Think of it as an attempt to build an open marketplace for AI instead of a world where intelligence is rented from a few centralized providers.
 
Why This Matters for Robotics
 
Robotics has a unique problem. Unlike chatbots, robots operate in the physical world. They need to perceive environments, make decisions, move safely and they need to learn continuously.
 
The challenge is that collecting and training on real-world robotic data is incredibly expensive. That's one reason large companies have such an advantage. They can afford the compute, simulation environments, and data infrastructure needed to train robotics models at scale.
 
This is where decentralized systems become interesting.
 
Instead of one company collecting all the data and training all the models, decentralized networks could allow thousands of contributors to participate in building robotic intelligence.
 
Imagine a future where:
  • Warehouse robots contribute operational data.
  • Delivery robots contribute navigation data.
  • Factory robots contribute manipulation data.
  • Developers contribute models.
  • Validators evaluate performance.
The resulting intelligence becomes a shared network rather than a proprietary asset.
 
That vision is beginning to emerge.
 
Bittensor's Move Toward Physical AI
 
While many people associate Bittensor (@bittensor) with language models and AI services, parts of the ecosystem are increasingly exploring embodied intelligence and robotics.
 
One example is Kinitro, a subnet focused on incentivizing the training and evaluation of embodied AI systems. The goal is to create competitive environments where developers build robotic intelligence and are rewarded based on performance.
 
The broader Bittensor ecosystem has also expanded into compute marketplaces, distributed inference systems, bandwidth infrastructure, and AI coordination layers that could eventually support robotics workloads. Several subnets now focus on decentralized compute, confidential inference, data transfer, and model training, critical components for future robotic systems.
 
In other words, the pieces are starting to appear.
 
Not a decentralized robot network yet.
 
But the infrastructure that could support one.
 
Beyond Bittensor: The Rise of Physical AI Networks
 
Bittensor isn't alone.
 
Across the industry, researchers and builders are experimenting with decentralized approaches to physical AI.
 
New research published in 2026 introduced the concept of DAO-enabled decentralized physical AI, or DePAI. The idea combines robotics, decentralized infrastructure, AI models, governance systems, and human oversight into a single framework. Instead of centralized control, robots and physical infrastructure could be coordinated through transparent rules and distributed ownership models.
 
At the same time, developers are exploring decentralized operating systems for robots that allow machines to communicate directly with each other and with distributed compute resources. These architectures are designed to make robotic systems more resilient and less dependent on a single cloud provider.
 
The goal is not simply decentralization for its own sake.
 
The goal is resilience.
 
If one server fails, the system continues.
 
If one company disappears, the network survives.
 
If one participant leaves, innovation continues.
 
But Here's the Reality
 
Decentralized AI faces the same challenge every decentralized technology faces.
 
Big Tech has resources. A lot of resources.
 
Training advanced robotics models requires enormous compute budgets, sophisticated simulation environments, access to specialized hardware, and vast amounts of real-world data.
 
That's why many robotics startups still partner with major cloud providers and AI companies. It's often the fastest path to deployment.
 
And there are legitimate concerns about whether decentralized networks can maintain quality, reliability, and security at the scale required for industrial robotics. Even researchers studying decentralized AI systems have highlighted risks around concentration, incentives, governance, and network security.
 
The challenge isn't just decentralizing intelligence.
 
It's decentralizing intelligence while maintaining performance.
 
That's much harder.
 
The Most Likely Outcome
 
The future probably won't be fully centralized. And it probably won't be fully decentralized either. Instead, we're likely heading toward a hybrid model.
 
Large technology companies will continue providing chips, cloud infrastructure, simulation platforms, and foundational research.
 
At the same time, decentralized AI networks will emerge as alternative coordination layers where intelligence, data, and economic value can be shared more openly.
 
The companies building robots may use NVIDIA hardware.
 
Train on Azure.
 
Run foundation models from OpenAI.
 
But they may also participate in decentralized data networks, decentralized compute markets, and decentralized intelligence protocols.
 
The future of robotics could end up looking less like a monopoly and more like an ecosystem.
 
The Bigger Question
 
The real question isn't whether decentralized AI can eliminate Big Tech.
 
It can't.
 
At least not anytime soon.
 
The real question is whether decentralized AI can prevent a future where a handful of companies control every robot, every model, every dataset, and every decision made by the machines operating around us.
 
As robots become workers, assistants, delivery drivers, factory operators, and even economic agents, that question becomes increasingly important.
 
Because the battle for the future of robotics is no longer about hardware.
 
It's about who owns the intelligence.
 
And that battle is just getting started.
 
 

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Navigating the world of blockchain 🧭
Navigating the world of blockchain can feel like learning a completely foreign language. Between technical jargon and fast-moving Web3 terminology, getting started can be overwhelming.

Whether you are exploring digital assets, building on-chain, or simply trying to understand decentralized technology, here is your foundational glossary of essential blockchain terms every beginner should know.

🏛️ 1. Core Architecture: The Base Layer

  • Blockchain: A distributed, immutable digital ledger that records transactions across a peer-to-peer network of computers. Once data is written to a block and added to the chain, it cannot be altered without altering all subsequent blocks.
  • Block: A collection of verified transactions grouped together. Once filled, the block is cryptographically linked to the previous one, forming a chronological "chain."
  • Node: An individual computer connected to a blockchain network that helps validate transactions, store ledger data, and maintain network consensus.
  • Consensus Mechanism: The set of rules and algorithms that network nodes use to agree on the validity of transactions.

    • Proof of Work (PoW): Requires miners to solve complex mathematical puzzles using computational power (e.g., Bitcoin).
    • Proof of Stake (PoS): Requires validators to lock up ("stake") native tokens as collateral to participate in block validation (e.g., Ethereum).

🔑 2. Ownership & Security: Wallets and Keys

  • Public Key (Address): An alphanumeric string that acts like your bank account number or email address. It is safe to share publicly so others can send you digital assets.
  • Private Key: A secret cryptographic passphrase or key that grants full access and control over your wallet assets. Never share your private key or seed phrase with anyone.
  • Seed Phrase (Recovery Phrase): A sequence of 12 to 24 random words generated when you set up a wallet. It acts as the master backup key to restore your wallet and access your funds on any device.
  • Hot Wallet vs. Cold Wallet:

    • Hot Wallet: A software-based crypto wallet connected to the internet (e.g., browser extensions, mobile apps), making it convenient for frequent transactions but higher risk.
    • Cold Wallet: An offline hardware device (e.g., Ledger, Coldcard) designed to isolate private keys from internet-connected threats.

⚙️ 3. Execution & Functionality: Smart Contracts and Apps

  • Smart Contract: Self-executing code stored on a blockchain that automatically enforces agreement terms once predetermined conditions are met—eliminating the need for intermediaries.
  • dApp (Decentralized Application): Applications built on top of a blockchain network that run via smart contracts rather than centralized cloud servers.
  • Gas Fees: Network transaction fees paid to validators or miners to cover the computational energy required to process actions on a blockchain.
  • Layer 1 vs. Layer 2:

    • Layer 1 (L1): The underlying primary blockchain network (e.g., Bitcoin, Ethereum, Solana) that handles base security and finality.
    • Layer 2 (L2): Secondary frameworks or companion networks built on top of an L1 to increase transaction speeds and lower gas fees (e.g., Arbitrum, Optimism, Base).

💰 4. Financial & Market Concepts

  • Tokenomics: The economic design, supply dynamics, utility, and distribution model of a cryptocurrency or token project.
  • DeFi (Decentralized Finance): Financial services—such as lending, borrowing, trading, and earning interest—built on smart contracts without traditional banks or financial intermediaries.
  • Liquidity: The ease with which an asset can be bought or sold in a market without significantly impacting its price.
  • DYOR (Do Your Own Research): A foundational golden rule in the Web3 space reminding users to independently verify technical code, whitepapers, and team backgrounds before making any capital commitments.

💡 Quick Cheat Sheet

"Not your keys, not your coins."

If you do not hold the private keys or seed phrase to your digital wallet, you do not truly own the assets inside it—a centralized entity or exchange does. Always prioritize security first as you explore the space.

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AI Is Coming for Your Job Title

Artificial intelligence may or may not take your job, but it has already broken into the human resources department and vandalized the org chart.

The evidence is all over LinkedIn, where perfectly serviceable occupations now arrive wearing titles such as “forward-deployed and agentic AI architect.” That person may be building sophisticated software. They may also be helping a chatbot remember what happened three prompts ago. Either way, somebody approved the business cards.

The expanding AI lexicon offers a useful counterpoint to the darker debate about technology and employment. Most discussion centers on how many jobs AI will eliminate. Hiring data presents a more complicated picture that includes a weak overall labor market containing a small but rapidly growing neighborhood of AI-related work.

Indeed Hiring Lab found that the number of postings on Indeed mentioning AI surged 134% from its February 2020 level by the end of 2025, even as total postings stood only 6% above that benchmark. AI appeared in a record 4.2% of Indeed postings in December.

AI, in other words, is not merely changing work. It is adding syllables to it.

The Titles Employers Actually Want

The undisputed champion is AI engineer, which ranked No. 1 on LinkedIn’s 2026 Jobs on the Rise list. The ranking, based on growth during the previous three years, also highlighted AI consultants and strategists, AI and machine-learning researchers and data annotators.

The title is popular partly because it is wonderfully accommodating. An AI engineer might build applications around large language models, connect corporate data to an AI system, improve model performance or spend Thursday afternoon persuading a customer service bot not to offer refunds for products the company doesn’t sell.

Indeed’s data showed the terminology spreading beyond Silicon Valley. Nearly 45% of data and analytics postings contained an AI-related term at the end of 2025, along with roughly 15% of marketing postings and 9% of human resources listings. A more recent Indeed analysis reported by Business Insider found that the number of frequently advertised job titles explicitly referencing AI rose from 264 in 2022 to 822 in the first quarter of 2026. Nearly two-thirds were outside traditional technology fields.

That produces titles such as AI marketing manager, AI learning specialist, responsible AI counsel and AI transformation lead. These are not always new occupations. Frequently, they are familiar jobs that have discovered a highly effective résumé keyword.

LinkedIn data cited by the World Economic Forum estimated that AI investment has supported 1.3 million positions, including AI engineers, data annotators and forward-deployed engineers, plus more than 600,000 AI-enabled data center jobs. The server racks, unlike the chatbots, still need electricians.

The Jobs With the Science-Fiction Salaries

At the upper end, AI has created a compensation market that resembles professional sports, except the competitors wear hoodies and discuss inference latency.

Syracuse University review put chief AI officer compensation between $200,000 and more than $500,000, while specialized roles can exceed $400,000 after bonuses and equity. Frontier research engineers, AI infrastructure specialists and engineers who can train or deploy advanced models command some of the largest packages.

Then there is the forward-deployed engineer, an old Palantir title that the AI boom has placed on a rocket sled. These engineers embed with customers, translating an executive’s desire to “do something with AI” into software that works. The Next Web reported that Indeed postings for the role were about 19 times higher in January than a year earlier.

CTO guide from the blog Signal Through the Noise placed forward-deployed engineer compensation between $238,000 and $700,000, research-engineering packages as high as $1.4 million and chief AI officer compensation above $1 million in some cases. It also made a less flattering observation: Many lavishly differentiated titles describe the same three basic functions. People build AI products, train models or keep the infrastructure from catching fire.

The Department of Unnecessary Titles

AI has created some genuinely new work. Evals engineers design tests to determine whether models perform reliably. AI red teamers try to make systems fail before customers do. Model behavior engineers study why an AI system responds as it does. AI governance leaders manage risks involving data, bias, security and regulation.

Other titles seem to have escaped from a brainstorming retreat.

There is the Claude Evangelist, whose mission apparently combines product education with the traditional duties of an apostle. There are vibe coders, who build software by describing what they want and accepting AI-generated code with varying degrees of supervision. “Vibe engineer” is the more respectable version, roughly equivalent to putting on a blazer before asking the machine to fix the login page.

“Context engineer” is a real discipline involving the data, instructions, memory and tools supplied to AI models. “Prompt engineer,” once advertised as a possible six-figure profession for gifted chatbot whisperers, is increasingly treated as one skill inside a broader AI role.

The CTO guide also identified “builder,” “AI-native developer,” “RAG engineer,” “agentic AI engineer” and “principal agentic GenAI forward-deployed context architect,” the last of which appears to require both technical proficiency and exceptional lung capacity.

Has AI created entirely new jobs? Absolutely. Some occupations, including AI safety, evaluation and model governance, exist because modern generative systems introduced new technical and business problems. However, many job titles are old jobs with fresh vocabulary, higher salary bands and a sudden aversion to the words “software developer.”

That may be the safest prediction about AI and employment. The machines will automate some tasks, generate others and force companies to rethink the division of labor. Before any of that is settled, however, corporate America will form a steering committee, appoint a chief agentic transformation evangelist and schedule a meeting to determine what that person does.

Source

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