We are living through a seismic shift in the financial world, a trend sometimes referred to as “unbanking.” This isn’t just a failure of the regular banks. That’s a pretty radical change in how we get and use financial services. Digital identity 3.0 and embedded finance are accelerating this transformation. They are fundamentally changing what money and banking mean in our society. At Calloutcoin.com our mission is to help simplify this confusing and rapidly evolving landscape, helping you understand the technologies and trends that are powering this transformation.

Crypto Chronicles - An Overview

Before going into the nitty gritty of unbanking, it’s important to set the stage for what’s happening in the digital financial space. Cryptocurrencies have been the catalyst that has enabled blockchain technology to provide the building blocks of many of the cool things we’re seeing right now. They’ve proven the value of decentralized, transparent, and accessible financial systems.

Introduction to Cryptocurrency

Cryptocurrency, as the name implies, is, at base, a digital/virtual currency that relies on cryptography for security. For traditional currencies issued by central banks, or fiat currencies, the backing and authority come from a central government. Bitcoin, the original and still most popular cryptocurrency, debuted in 2009. Since then, over 9,500 other cryptocurrencies have joined the ranks. We sometimes refer to these as altcoins, and they all have their own special properties and use cases.

Cryptocurrency can deliver on the promise of more financial freedom and lower transaction fees. Moreover, it offers more privacy than conventional banking institutions. Cryptoeconomic security Transactions are recorded cryptographically on an immutable public ledger, making every transaction transparent and verifiable. Unfortunately, the anonymity that some cryptocurrencies provide has made them appealing tools for various illicit activities, from money laundering to drug trafficking to terrorist financing.

The Evolution of Digital Currency

The concept of digital currency predates Bitcoin. In the 1990s, initial efforts to introduce digital money, such as DigiCash’s scheme, had a hard time taking off. Despite their environmental benefits, technological limitations and centralized control prevented them from going viral. What was truly revolutionary about bitcoin was that it was the first decentralized system. This innovation removed the necessity for a mutually trusted third party to verify transactions.

This evolution still profoundly impacts our financial landscape today, as innovations and use cases are developed daily.

  1. Early attempts: Centralized digital currencies with limited adoption.

  2. Bitcoin's emergence: The first decentralized cryptocurrency.

  3. Altcoins: A proliferation of cryptocurrencies with diverse features.

  4. Stablecoins: Cryptocurrencies pegged to stable assets like the US dollar, aiming to reduce volatility.

  5. Central Bank Digital Currencies (CBDCs): Digital currencies issued by central banks, representing a digital form of fiat currency.

Model Context Protocol (MCP) is an emerging standard that holds a significant place in this brave new digital world. It adds value and meaning to digital engagements. MCP offers a unified approach to making sense of and interpreting this context in which all models, and machine learning models in particular, exist.

Understanding MCP (Model Context Protocol) - Definition, Functionality, and Significance

MCP provides a structure that helps define the setting into which a model is deployed. Though simple, it is a powerful set of rules, and helpful in orienting ourselves to this unique context. This is acutely the case for machine learning models, in which the context can make or break the model’s performance and reliability. Data provenance, including the data sources that were used, the model’s intended use, and any extraneous factors that may impact its predictive capacity.

🔍 What is MCP (Model Context Protocol)?

MCP works to make sure models are used in appropriate ways, and their limitations properly understood. It provides you with a realistic idea of the model’s capabilities and limitations. This understanding is critical to being able to make decisions based on its outputs. MCP is key to ensuring transparency and accountability in the use of AI.

MCP’s magic comes from creating a systematic process for capturing and communicating a model’s context. This includes:

⚙️ How MCP Works

By supplying this information, MCP educates users on what the model is doing and what to look for in interpreting its outputs. Doing so helps better informed decisions to be made and minimizes the risk of incorrect interpretation or use of the model.

  • Data Provenance: Tracing the origin and quality of the data used to train the model.
  • Model Assumptions: Clearly stating the assumptions made during the model's development.
  • Intended Use: Defining the specific scenarios in which the model is designed to be used.
  • Limitations: Identifying the known limitations of the model and potential biases.
  • Performance Metrics: Providing metrics to evaluate the model's performance in different contexts.

On balance, MCP is a huge step in the right direction of fostering the responsible development and deployment of machine learning models. MCP offers a clearer, more consistent way to interpret and convey a model’s context. This will help ensure that models are applied correctly and that their limitations are clearly identified. As AI adoption accelerates, MCP will be key to ensuring transparency, accountability, and public trust in AI systems.

🧠 Why Use MCP?

The core banking systems market in China is a fast growing and highly competitive arena. It’s powered by the country’s fintech-friendly banking industry, and the growing need for digital financial services, or DFS. Understanding the key players, integrators, and trends in this market is crucial for anyone looking to navigate the Chinese financial landscape.

  • Improved Transparency: MCP makes models more transparent by providing a clear understanding of their context.

  • Enhanced Trust: By documenting the model's limitations and assumptions, MCP helps build trust in its outputs.

  • Better Decision-Making: MCP allows users to make more informed decisions based on the model's predictions.

  • Risk Mitigation: By identifying potential biases and limitations, MCP helps mitigate the risks associated with using the model.

  • Regulatory Compliance: MCP can help organizations comply with regulations that require transparency and accountability in the use of AI.

🚀 Final Thoughts

Some of the major players include:

Core Banking Systems Market in China - Key Players, Integrators, and Trends

These vendors compete based on functionality, scalability and cost. For example, international players have to be sensitive and obedient to local regulations and standards. This compliance often presents a daunting barrier to entry for them.

Major Vendors in the Chinese Market

System integrators are key to the deployment and ongoing maintenance of core banking systems. They use APIs to help you integrate the core banking system with all the other systems. This includes things like payment gateways, customer relationship management (CRM) systems, and data warehouses. Some of the key system integrators in the Chinese core banking market include:

  • Huawei: A leading technology company that offers a range of core banking solutions tailored to the Chinese market.

  • iSoftStone: A local vendor that provides core banking systems and related services to Chinese banks.

  • SunGard (now part of FIS): An international vendor with a significant presence in China, offering core banking solutions and consulting services.

  • Temenos: A global provider of core banking systems that has gained traction in the Chinese market.

The Chinese core banking market is currently driven by several key trends:

System Integrators (SIs) in Core Banking

These trends pose interesting opportunities for vendors and system integrators. For one, they are able to offer novel solutions and services that are tailored explicitly for the needs of Chinese banks.

  • IBM: A global IT services company with a strong presence in China, offering system integration services for core banking projects.

  • Accenture: Another global IT services company that provides system integration and consulting services to Chinese banks.

  • Infosys: An Indian IT services company that has expanded its presence in China and offers system integration services for core banking.

  • Local SIs: Several local system integrators specialize in core banking projects, offering expertise in local regulations and standards.

Current Market Trends: Legacy Replacement, Cloud Adoption, and AI Integration

A smart, agile core banking market The mainframe-dominant core banking market in Australia and New Zealand (ANZ) is highly evolved and acutely competitive. The market is incredibly vibrant, with a competitive landscape of home-grown and external challengers. With banks rushing to maintain competitive systems and improve customer experience, meeting these expectations drives this growth.

  • Legacy Replacement: Many Chinese banks are replacing their legacy core banking systems with modern platforms to improve efficiency and scalability.

  • Cloud Adoption: There is a growing trend towards adopting cloud-based core banking solutions to reduce costs and improve agility.

  • AI Integration: Banks are increasingly integrating artificial intelligence (AI) into their core banking systems to automate tasks, improve customer service, and detect fraud.

  • Regulatory Compliance: Banks need to comply with increasingly stringent regulations, which is driving demand for core banking systems that can support regulatory reporting and compliance.

The ANZ core banking market is dominated by a few key providers:

Analysis of Core Banking Market in Australia and New Zealand

These providers all compete on functionality, scalability and cost. At the same time, new players have to adhere to a changing set of local regulations and industry standards. This requirement can be a major barrier to entry for them.

Leading Core Banking System Providers in ANZ

Meanwhile, system integrators, who are responsible for implementing and maintaining core banking systems, can be significant actors on the ANZ stage. They don’t stop at API-ifying the core banking system. This allows them to integrate with payment gateways, CRM systems, and data warehouses. Some of the key system integrators in the ANZ core banking market include:

The ANZ core banking market is expected to continue to evolve in the coming years, driven by several key factors:

System Integrators for Core Banking in ANZ

These forces are creating unique opportunities for vendors and system integrators alike. They are able to now offer unique solutions and services tailored to the unique needs of ANZ banks.

Future Outlook: Technology, Regulation, and Market Dynamics

This vibe coding is a remarkable new frontier in software development. It uses artificial intelligence (AI) to help developers code faster and produce higher-quality software. This style of development goes beyond just filling out code and finding bugs. It leverages the power of AI to understand the intent and context of the code you are currently writing.

Vibe coding focuses on harnessing AI-powered tools and techniques to help developers get productive code written. These tools help identify vulnerabilities within the code through automated static analysis in real-time. They further offer code improvement suggestions and code generation from natural language descriptions. The goal is to create a more intuitive and efficient coding experience, allowing developers to focus on the high-level design and architecture of their software.

Vibe Coding - A New Era of AI-Enhanced Software Development

Our goal with vibe coding isn’t to get AI to replace developers. Rather, it sets them up to do their jobs better and makes them more productive. Most importantly, it enables developers to write better code more quickly, cutting down the cost to design and configure complex software ecosystems.

Introduction to Vibe Coding

Hugging Face Smolagents is a surprisingly powerful framework for building code agents. These generative AI driven tools can automate a much wider variety of software development tasks. These agents are able to write code, debug code, and even deploy software, which allows them to be powerful development assistants.

The idea of agents isn’t fresh — the general notion has existed for decades in the field of artificial intelligence. The early agents were mostly rule-based systems—agents that could do a defined task in a narrow and deep domain. All of this has changed with the advent of deep learning and large language models, as agents have gotten more sophisticated and capable than ever before.

Benefits of AI in Software Development

Modern agents can understand natural language, reason about complex problems, and interact with their environment in a flexible and adaptive manner. They are limited to uses in chatbots, virtual assistants, and other apps—including customer service, healthcare, retail, and robotics.

  • Improved Code Quality: AI can help identify potential bugs and vulnerabilities in code, leading to higher quality software.

  • Increased Productivity: AI can automate repetitive tasks and provide code suggestions, allowing developers to write code faster.

  • Reduced Errors: AI can detect errors in real-time, reducing the likelihood of introducing bugs into the codebase.

  • Enhanced Collaboration: AI can help developers understand each other's code, making it easier to collaborate on complex projects.

  • Faster Learning: AI can provide personalized learning experiences, helping developers learn new programming languages and frameworks more quickly.

Building Code Agents with Hugging Face Smolagents

Code agents can be used to automate tasks such as:

A Brief History of Agents

One of the biggest problems in creating building code agents is making sure they perform code enforcement safely and effectively. Sandboxing code agents is essential, but even sandboxed code must run safely. They must be vigilant to not create security vulnerabilities and to maintain the integrity of the system.

To address this challenge, Hugging Face Smolagents provides several security features:

Introduction to Code Agents

Monitoring and evaluation code agents performance will be fundamental to guarantee they are doing it properly and efficiently. This involves tracking metrics such as:

  • Code Generation: Generating code snippets based on natural language descriptions.

  • Code Debugging: Identifying and fixing bugs in code.

  • Code Refactoring: Improving the structure and readability of code.

  • Code Documentation: Generating documentation for code.

  • Code Deployment: Deploying code to production environments.

Secure Code Execution

By tracking these metrics, developers can see where the agent can be improved and maximize its performance.

Constructing a profound research agent is an intense aim. Yet, it requires nothing short of an ideal combination of natural language understanding, document search and logical inference capabilities. In short, the agent has to understand research questions deeply. Then, it needs to identify what information is relevant and synthesize that information into a cohesive answer.

  • Sandboxing: Code is executed in a sandboxed environment that limits its access to system resources.

  • Code Analysis: Code is analyzed for potential security vulnerabilities before it is executed.

  • Access Control: Access to sensitive resources is controlled through a system of permissions and authentication.

Monitoring and Evaluating the Agent

Here are the steps involved in building a deep research agent:

  • Task Completion Rate: The percentage of tasks that the agent successfully completes.

  • Error Rate: The number of errors that the agent makes.

  • Execution Time: The time it takes for the agent to complete a task.

  • Resource Usage: The amount of resources (CPU, memory, etc.) that the agent uses.

Vibe coding and code agents usher in an exciting era in software development. They use AI to make the coding experience better and to help automate other processes. Collectively, these new technologies promise to significantly accelerate developer productivity and efficiency. This enables their teams to focus on the big picture design and architecture of their software. As AI technology progresses, we’re just scratching the surface for the possibilities of how AI can be applied to improve software development.

Building a Deep Research Agent

Embedded finance is changing the game on how we use financial services. By, figuratively speaking, embedding financial tools into the platforms people already use every day, it creates an effortless transaction experience that delights customers. Fintech consumers can engage with financial services seamlessly and immediately within the context of their most-used applications. No trip to a bank or separate financial institution required! According to a recent McKinsey survey, almost 70% of banks are acknowledging this shift. Because today, only 20% of their APIs are consumed outside their business, and that’s going to double by 2025! This significant development further highlights the need for embedded finance to be a primary consideration by traditional banks.

Embedded finance is nothing new, it’s just emerged in a different form. Early use cases are like when retailers issue their own store credit cards, or payment processors embed themselves natively into e-commerce platforms like Shopify. Nevertheless, overall, due to rapid technological advancements and evolving consumer expectations, it has grown faster and wider. Today, embedded finance encompasses a wide range of services, including:

  1. Define the Research Domain: Clearly define the domain in which the agent will be conducting research.

  2. Gather Data: Collect a large corpus of text and data related to the research domain.

  3. Train the Agent: Train the agent using machine learning techniques to understand natural language and extract relevant information.

  4. Implement Reasoning Capabilities: Implement reasoning capabilities that allow the agent to synthesize information and draw conclusions.

  5. Evaluate the Agent: Evaluate the performance of the agent using a set of research questions and compare its answers to those of human experts.

Final Thoughts

Digital Identity 3.0 is a key part of the arguable unbanking trend to bewilder US bank executives. It is known as the SSI, or self-sovereign identity revolution – the next generation of digital identity solutions which are more user-controlled, secure and interoperable. Digital Identity 3.0 puts users in control of their identity and overall online experience. First, unlike previous versions which were centralized and run by a single entity, this new approach enables secure data sharing with multiple service providers.

IAM (Identity and Access Management) is fundamental to embedded finance, streamlining the ecosystem of multiple APIs and Third-Party Providers (TPPs). Extensible IAM solutions integrate with third-party biometrics and identity verification tools. They help augment fraud detection with risk management and help create safe identity proofing and ongoing, risk-based authentication.

Embedded finance isn't a new concept; it has evolved over time. Early examples include retailers offering store credit cards or payment processors integrating with e-commerce platforms. However, recent technological advancements and changing consumer expectations have accelerated its growth and expanded its scope. Today, embedded finance encompasses a wide range of services, including:

  • Embedded Payments: Integrating payment processing directly into a platform, such as a ride-sharing app.

  • Embedded Lending: Offering loans or credit lines through a non-financial platform, such as an e-commerce site.

  • Embedded Insurance: Providing insurance products through a platform, such as a travel booking site.

  • Embedded Investments: Allowing users to invest directly through a platform, such as a robo-advisor app.

Digital Identity 3.0 is a critical component of the unbanking phenomenon. It refers to the next generation of digital identity solutions, characterized by greater user control, security, and interoperability. Unlike earlier versions of digital identity, which were often centralized and controlled by a single entity, Digital Identity 3.0 aims to empower users to manage their own identities and share them securely with various service providers.

Key features of Digital Identity 3.0 include:

  • Decentralization: Users have greater control over their identity data and can choose who to share it with.

  • Self-Sovereignty: Users own their digital identities and can manage them independently of any central authority.

  • Interoperability: Digital identities can be used across different platforms and services.

  • Security: Strong cryptographic techniques are used to protect identity data from unauthorized access.

  • Privacy: Users have greater control over their privacy settings and can choose what information to share.

IAM (Identity and Access Management) is fundamental to embedded finance, streamlining the ecosystem of multiple APIs and Third-Party Providers (TPPs). Extensible IAM solutions use third-party biometrics, identity verification, and risk management to enable secure identity proofing and continuous risk-based authentication.

The integration of IAM with embedded finance offers several benefits:

  • Enhanced Security: IAM helps protect sensitive financial data from unauthorized access.

  • Improved Compliance: IAM helps organizations comply with regulations related to data privacy and security.

  • Streamlined User Experience: IAM simplifies the login process for users,