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The Future of Open Banking: API Standards, Interoperability, and Competition
Can a joint effort solve Open Banking's interoperability issues?
Dinarian888
March 29, 2023
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Open banking is a financial services concept that allows third-party developers to create applications and services based on banking data using application programming interfaces. (APIs).

Its goal is to provide clients more control over their financial data and to increase competition in the banking industry. As open banking evolves, various factors, such as API standards, interoperability, and competitiveness, are influencing its future.

API Requirements

API standards are crucial to open banking's success because they provide a common vocabulary for diverse systems to connect with one another. The use of standardized APIs will allow developers to create applications that can communicate with numerous banks and financial organizations.

As a result, a more open and linked environment will emerge, benefiting both consumers and companies.

The Payment Services Directive 2 (PSD2) in Europe requires banks to provide accessible APIs for third-party applications to access consumer data. This has resulted in the creation of many API standards, including those developed by the Berlin Group, Open Banking UK, and STET.

These standards define how APIs should be built, documented, and implemented, making it easier for developers to create cross-bank applications.

Interoperability

The capacity of multiple systems to function together effortlessly is referred to as interoperability. Interoperability in the context of open banking means that different banks and financial organizations can exchange data with one another using a common standard.

Customers will be able to view all of their financial information at one location, regardless of which bank they use.

The lack of interoperability has been a fundamental impediment to open banking adoption. Customers have been wary about using open banking services because they require them to share financial information with third-party companies.

Customers may be unwilling to use these services if data cannot be transferred securely and efficiently.

Several projects have been developed to encourage interoperability in open banking to overcome this difficulty. The Financial Data interchange (FDX) in the United States and the Open Banking Implementation Entity (OBIE) in the United Kingdom, for example, are trying to develop common standards for open banking data interchange.

These standards will make it easier for banks and third-party providers to communicate data in a secure and efficient manner, allowing for greater use of open banking services.

Competition

Open banking is opening up new avenues for competition in the banking business. Open banking enables the development of new services that compete with existing banking products by allowing third-party providers access to consumer data.

As a result, banks are being forced to innovate and improve their own services in order to remain competitive.

Payment services are one area where open banking is likely to drive competition. Open banking APIs can be used to develop new payment systems that are faster, less expensive, and more convenient than existing ways.

This has the potential to disrupt the current payment environment, opening up new opportunities for fintech startups and other innovative businesses.

Financial management services are another area where open banking is likely to boost competition. Third-party suppliers can design applications that assist clients to manage their finances more successfully by gaining access to customer data.

Budgeting tools, savings calculators, and investment management services are examples of such services. As these services become more generally available, they may open up new avenues for rivalry in the financial management industry.

Solving the Interoperability Issue

Open banking technology aims to provide greater transparency and innovation in the financial sector by making banking services more accessible and convenient. However, one of the biggest challenges that open banking faces is interoperability, which refers to the ability of different systems to communicate and exchange data seamlessly. Tacking this issue is possible but it requires effort. We've highlighted 5 ways of improving interoperability in open banking.

Standardization of APIs

The first step towards improving interoperability in open banking is to standardize APIs across the industry. Currently, there are different API standards used by different banks and financial institutions. This can create significant challenges for third-party providers who need to adapt to each API, which can lead to inconsistencies in data exchange. By standardizing APIs, open banking can reduce the complexity of integrating with multiple systems and enable seamless data exchange across platforms.

Collaboration among banks and third-party providers

Collaboration among banks and third-party providers is crucial for improving interoperability in open banking. Banks can work with third-party providers to identify areas of improvement and develop common solutions that can be used by all parties. For instance, a common authentication mechanism can be developed that can be used by all third-party providers to access customer data. This will eliminate the need for third-party providers to develop their authentication mechanisms, which can reduce the risk of data breaches.

Implementation of industry-wide standards

Implementation of industry-wide standards can also help to improve interoperability in open banking. There are several standard-setting bodies that are working towards developing common standards for open banking. For instance, the Open Banking Implementation Entity (OBIE) in the UK has developed a common standard for APIs that is being used by banks and third-party providers in the country. The adoption of such standards can help to create 💥a level playing field💥 for all players in the industry and eliminate the need for custom solutions.

Creation of data exchange platforms

The creation of data exchange platforms can also help to improve interoperability in open banking. These platforms can act as intermediaries between banks and third-party providers and enable seamless data exchange across platforms. As an example, the Financial Data Exchange (FDX) in the US is a non-profit organization that has developed a common API standard for data sharing between banks and third-party providers. FDX also provides a secure data exchange platform that enables banks and third-party providers to exchange data in a standardized format.

Integration with emerging technologies

Finally, 💥open banking can leverage emerging technologies such as artificial intelligence (AI) and blockchain to improve interoperability.💥 AI can be used to analyze data patterns and identify inconsistencies in data exchange, which can help to improve the accuracy and reliability of data exchange. 💥Blockchain, on the other hand, can be used to create a decentralized network for data exchange, which can improve security and eliminate the need for intermediaries.💥

Conclusion

Open banking is a game-changing idea with the potential to change the banking system. Adoption of API standards, interoperability, and competitiveness will be key to open banking's success.

Open banking will enable the development of new services that will benefit clients and foster innovation in the banking industry by developing common standards for data interchange, promoting interoperability, and driving competition. It will be intriguing to observe how open banking transforms the financial services market and improves the client experience as it evolves.

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Revolut Leak Shows the Cost of Constant ID Collection
Revolut’s mistake is the news, but the bigger problem is the growing number of companies being encouraged or required to keep copies of our most sensitive identity documents.

Online bank Revolut has revealed that it gave out sensitive personal and financial information of an undisclosed number of its customers in response to a fake government request.

The information that was handed over to an “unauthorized third party” reportedly includes names, dates of birth, occupations, addresses, phone numbers, account numbers, transaction histories (including Bitcoin), and even copies of government-issued IDs and onboarding verification selfies.

Revolut claims that derived biometric face data was not.

The company said that the data was handed over in response to an email that came from a real government agency’s domain, but was not actually sent or authorized by that agency.

The email passed several authentication checks (SPF, DKIM, and DMARC) that are designed to establish the authenticity of a message’s origin and integrity, but do not verify the legitimacy of the legal request itself.

Revolut said that it complied with the request “under the reasonable belief that it was an authentic government agency request” – and only later found out that it was not.

Revolut said it later realized its mistake, blocked the email address, and reported the incident to the relevant authorities.

Revolut said that only a “limited” number of its customers were affected by the data leak, and that the company’s systems were not hacked, nor was any money stolen.

The story broke on September 11 when Revolut customers started receiving an email notice about a data leak, and the news was picked up by media outlets the following day.

Revolut notice explaining customer identity and financial data was shared after an unauthorized government email request.

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The more companies are forced to collect and store such information, and the more of it they have, the more opportunities there are for this data to be leaked, either by the company itself or a third party it works with. That's what makes governments' push for more ID checks just to access ordinary parts of life so reckless.

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Dinarian888@dinarian888
August 28, 2026
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This Is The Income A Family Needs To Live Comfortably In Every US State

Here’s the short version of what it takes for a family of four to live comfortably in 2026 by state:

In Massachusetts, you’d need nearly $330,000 a year - the highest figure in the entire country. Only three states clear the $300,000 mark: Massachusetts, Hawaii, and California. At the other end of the spectrum, Mississippi is the most affordable at about $188,000. That’s a full $142,000 less than what you’d need in Massachusetts.

So… how much does a family of four need in your state?

This map shows the pre-tax income a household with two working adults and two kids needs to live comfortably in every U.S. state.

The numbers come from SmartAsset (as of February 2026). They’re based on the familiar 50/30/20 budget: 50% for necessities, 30% for discretionary spending, and 20% for savings or other goals. These aren’t bare-minimum survival numbers—they’re what it takes to live pretty well while still putting money aside.

And as Visual Capitalist notes, Massachusetts sits at the very top of that list. Massachusetts tops the ranking, with a family of four needing $329,555 per year to meet the 50/30/20 benchmark.

Hawaii follows at $313,165, while California ranks third at $302,682.

Rank State Income needed for family of four (2026)

  • 1 - Massachusetts - $329,555
  • 2 - Hawaii - $313,165
  • 3 - California - $302,682
  • 4 - Connecticut - $298,189
  • 5 - New Jersey - $295,110
  • 6 - New York - $291,533
  • 7 - Colorado - $283,213
  • 8 - Washington - $281,798
  • 9 - Oregon - $280,966
  • 10 - Vermont - $280,384
  • 11 - Alaska - $272,064
  • 12 - New Hampshire - $267,904
  • 13 - Rhode Island - $264,659
  • 14 - Minnesota - $263,078
  • 15 - Maryland - $257,837
  • 16 - Maine - $250,931
  • 17 - Montana - $249,434
  • 18 - Pennsylvania - $247,936
  • 19 - Illinois - $244,109
  • 20 - Virginia - $242,944
  • 21 - Nevada - $242,278
  • 22 - Indiana - $241,696
  • 23 - Wisconsin - $238,451
  • 24 - Arizona - $236,870
  • 25 - Utah - $235,789
  • 26 - Delaware - $228,134
  • 27 - Ohio - $226,221
  • 28 - Idaho - $226,054
  • 29 - Florida - $223,392
  • 30 - New Mexico - $223,142
  • 31 - Nebraska - $223,059
  • 32 - Missouri - $217,734
  • 33 - Georgia - $214,573
  • 34 - Michigan - $214,323
  • 35 - South Carolina - $212,909
  • 36 - North Carolina - $212,410
  • 37 - Wyoming - $212,410
  • 38 - Oklahoma - $211,910
  • 39 - North Dakota - $210,496
  • 40 - Kansas - $207,917
  • 41 - Iowa - $204,422
  • 42 - Texas - $203,424
  • 43 - West Virginia - $202,592
  • 44 - South Dakota - $201,760
  • 45 - Alabama - $198,931
  • 46 - Louisiana - $197,933
  • 47 - Tennessee - $197,267
  • 48 - Arkansas - $195,437
  • 49 - Kentucky - $194,854
  • 50 - Mississippi - $187,533

Connecticut, New Jersey, and New York aren't far behind, bringing the number of states with comfortable-income thresholds above $290,000 to six.

Colorado and Vermont Make the Top 10

As expected, many of the highest income thresholds are concentrated in the Northeast and along the West Coast.

However, Colorado has the seventh-highest threshold in the country at $283,213, ranking above Washington and Oregon.

Vermont rounds out the top 10 at $280,384, despite having the second-smallest population of any U.S. state. Meanwhile, nearby states like New Hampshire, Maine, and Rhode Island all fall outside the top 10.

Just Six States Come in Below $200,000

Despite the wide range in living costs across the country, only six states have a comfortable-income threshold below $200,000 for a family of four.

Mississippi ranks lowest at $187,533, followed by Kentucky. The states of Arkansas, Tennessee, Louisiana, and Alabama also fall below the $200,000 mark.

The gap between Massachusetts and Mississippi exceeds $142,000 per year, meaning the Massachusetts benchmark is about 76% higher.

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Dinarian888@dinarian888
August 16, 2026
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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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