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Generative AI in Banking - All You Need to Know
May 26, 2023
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Imagine a world where AI-powered systems can quickly identify fraudulent activities by analyzing intricate patterns in transactions, thereby safeguarding the interests of both financial institutions and their customers. Picture virtual assistants capable of understanding natural language and providing personalized financial advice based on individual preferences and goals. These are just a glimpse of the exciting possibilities that generative AI brings to the table.

In the ever-evolving landscape of banking, the integration of cutting-edge technologies has become a necessity to stay competitive and meet the growing demands of customers. One such revolutionary technology that has gained significant traction in recent years is Generative Artificial Intelligence (AI). From streamlining operations to enhancing customer experiences, generative AI holds tremendous potential to reshape the banking sector as we know it.

In this comprehensive guide, we'll take you on an illuminating journey through the world of generative AI in banking. Whether you're a curious beginner seeking an introduction to this transformative technology or a seasoned professional looking for in-depth technical analysis, this article has you covered.

Exploring Generative AI

Definition and Principles of Generative AI

Generative AI, also known as generative adversarial networks (GANs), is a subfield of artificial intelligence that focuses on creating new and original content by learning patterns and generating output that closely resembles real data. Unlike other AI techniques that rely on pre-existing datasets and patterns, generative AI has the remarkable ability to generate novel content, such as images, music, text, and even human-like conversations.

At its core, generative AI operates on a fascinating principle: a generator model is trained to create content, while a discriminator model evaluates the generated content against real examples. Through an iterative process, these models engage in competition, constantly improving and refining the generated output. This interplay of generator and discriminator forms the foundation of generative AI, enabling it to produce astonishingly realistic and creative content.

The generator aims to create realistic output, while the discriminator evaluates the generated output against real examples. Through an iterative process, these models engage in a dynamic dance, continually improving their performance.

The generator receives random input and transforms it into an output that mimics the characteristics of the training data. The discriminator, on the other hand, distinguishes between real and generated output. As the models compete, the generator strives to create content that becomes indistinguishable from real data while the discriminator becomes increasingly adept at making accurate judgments. This adversarial training loop drives the generative AI system to produce increasingly authentic and high-quality output.

How does it differ from other AI techniques?

Creativity and Novelty

Unlike traditional AI techniques, which rely on predefined rules and patterns, generative AI excels at creating original and innovative content. By learning patterns from training data, the generator can generate output that goes beyond existing examples, surprising users with its creativity and novelty. This unique capability makes generative AI an ideal tool for tasks like art generation, music composition, and storytelling.

Uncertainty and Exploration

Generative AI embraces uncertainty and encourages exploration. By introducing randomness into the model's input, it can generate diverse variations of content, allowing for experimentation and exploration of alternative possibilities. This ability to venture into uncharted territories distinguishes generative AI from rule-based approaches that produce predictable and deterministic output.

Transfer Learning and Adaptability

Generative AI models trained on large datasets can acquire a deep understanding of underlying patterns and structures. This knowledge can be transferred to new tasks or domains, enabling the model to generate content in unfamiliar contexts. The adaptability of generative AI sets it apart from other AI techniques that require extensive retraining for each new application.

Generative Latent Spaces

Generative AI models operate in a high-dimensional latent space, where each point represents a potential output. By exploring this latent space, users can manipulate various attributes of the generated content, such as style, color, or emotion. This interactive and controllable aspect of generative AI offers immense creative possibilities and empowers users to shape the output according to their preferences.

Applications of Generative AI in Banking

Enhancing Customer Experience

Personalized Recommendations and Offers: Generative AI empowers banks to deliver personalized recommendations and offers tailored to individual customers' needs. By analyzing historical data, customer behavior, and preferences, banks can leverage Generative AI algorithms to suggest relevant financial products and services. This enhances customer engagement, fosters loyalty, and drives revenue growth.

Virtual Assistants and Chatbots: Virtual assistants powered by Generative AI have become invaluable tools in the banking industry. These intelligent agents interact with customers in real-time, providing assistance, answering queries, and guiding them through various banking processes. By leveraging natural language processing and machine learning algorithms, virtual assistants ensure prompt and personalized customer support, available 24/7.

Risk Assessment and Fraud Detection

Anomaly Detection and Pattern Recognition: Generative AI plays a crucial role in identifying anomalies and patterns in financial transactions. By analyzing historical transactional data and learning patterns of legitimate and fraudulent activities, banks can employ Generative AI algorithms to detect unusual behavior, identify potential risks, and mitigate fraud. This proactive approach enhances security, protects customers' assets, and reduces financial losses.

Real-Time Transaction Monitoring: Generative AI enables real-time monitoring of transactions, providing banks with the ability to detect and prevent fraudulent activities as they occur. Through advanced data analytics, machine learning models, and anomaly detection techniques, banks can swiftly identify suspicious transactions, trigger alerts, and take immediate action. Real-time transaction monitoring enhances fraud prevention capabilities and safeguards the integrity of the banking system.

Automating Back-Office Operations

Document Processing and Verification: Generative AI streamlines back-office operations by automating document processing and verification. By leveraging optical character recognition (OCR) and natural language understanding (NLU) capabilities, banks can automate data extraction, validate document authenticity, and accelerate processes such as loan approvals, account openings, and compliance checks. This reduces manual errors, enhances efficiency, and improves overall operational productivity.

Data Entry and Reconciliation: Generative AI simplifies data entry and reconciliation tasks, which are traditionally time-consuming and prone to human error. By automatically extracting relevant information from various sources, matching and reconciling data sets, and identifying discrepancies, banks can streamline their back-office operations. This automation minimizes manual efforts, ensures data accuracy, and optimizes resource allocation.

Improving Decision-Making with Generative AI

Predictive Analytics for Investment Strategies

Predictive analytics has long been a staple in investment strategies, aiming to forecast market trends and identify optimal opportunities. However, the integration of Generative AI brings a new dimension to this field, enabling unparalleled accuracy and insights.

Portfolio Optimization: Generative AI algorithms, fueled by vast historical and real-time data, transform portfolio optimization. Leveraging advanced machine learning techniques, these algorithms detect intricate patterns, correlations, and nonlinear relationships that evade human observation. By combining diverse asset classes, risk profiles, and market dynamics, Generative AI empowers investment professionals to construct optimized portfolios that strike a delicate balance between risk and reward.

Market Trend Analysis: Generative AI has become a game-changer for market trend analysis. By leveraging deep learning models and neural networks, this technology unravels hidden patterns and uncovers meaningful insights within extensive datasets. It effectively synthesizes structured and unstructured data, such as market news, social media sentiments, and economic indicators, to predict market movements with unprecedented accuracy. Armed with these insights, investors can make informed decisions, outmaneuver competitors, and capitalize on emerging opportunities.

Credit Scoring and Loan Approvals

The lending industry is ripe for transformation through the application of Generative AI. By leveraging advanced algorithms, this technology enhances the precision and efficiency of credit scoring and loan approval processes.

Assessing Creditworthiness: Generative AI's intricate algorithms delve deep into the realm of creditworthiness assessment. By incorporating a multitude of factors, such as credit history, income stability, debt-to-income ratio, and behavioral data, these models provide lenders with comprehensive and granular insights. Advanced machine learning techniques, including ensemble methods and deep neural networks, enable the accurate evaluation of an applicant's creditworthiness. The result is fairer lending decisions and minimized risk exposure for financial institutions.

Streamlining Loan Application Processes: Generative AI streamlines and accelerates the loan application process, benefiting both borrowers and lenders. Through natural language processing (NLP) and optical character recognition (OCR), the technology automates the extraction and analysis of essential documentation, such as financial statements, tax returns, and identification records. By digitizing and interpreting this information, Generative AI significantly reduces the manual effort required, expedites decision-making, and enhances overall process efficiency. This streamlined approach ensures faster loan approvals, granting borrowers prompt access to much-needed funds.

Ethical Considerations in Generative AI Banking

Ensuring transparency and fairness

Transparency and fairness are paramount in generative AI banking systems. Customers should have a clear understanding of how their data is being collected, used, and processed. To achieve this, banks must adopt strategies that prioritize transparency:

Model Explainability: Employing interpretable generative AI models, such as explainable neural networks or decision trees, enables banks to provide clear explanations of the underlying decision-making processes. This transparency helps customers understand how their data influences outcomes, fostering trust and accountability.

Algorithmic Auditing: Regular audits of AI algorithms are essential to identify biases or unfair practices. This involves scrutinizing training data for potential biases, testing for discriminatory outcomes, and addressing any discrepancies promptly. Techniques like adversarial testing or counterfactual fairness can aid in uncovering hidden biases.

Fairness Metrics and Monitoring: Implementing fairness metrics during model development and deployment allows banks to measure and monitor the impact of AI systems on different demographic groups. Techniques such as disparate impact analysis or equalized odds can help detect and rectify biases to ensure fair treatment for all customers.

Guarding against bias and discrimination

Bias and discrimination have the potential to undermine the ethical foundations of generative AI banking. Here are some approaches to mitigate bias and ensure fairness:

Diverse and Representative Training Data: To minimize biased outcomes, banks must ensure training data is diverse and representative of the customer base. Incorporating data from different demographics and continuously updating datasets helps reduce the risk of discriminatory practices.

Pre-processing Techniques: Techniques like data augmentation, oversampling, or undersampling can help balance imbalances in training data and mitigate the amplification of biased patterns during model training. Advanced techniques like adversarial training or causal inference can also address complex forms of bias.

Regular Bias Assessments: Continuous monitoring and auditing of AI systems are crucial to identify and rectify biases that may emerge during deployment. Regular assessments using fairness evaluation tools, coupled with human-in-the-loop validation, can contribute to ongoing fairness and accuracy.

Privacy and data protection concerns

Protecting customer privacy and ensuring data security are critical aspects of generative AI banking. The following measures can safeguard privacy and address data protection concerns:

Differential Privacy: By integrating differential privacy techniques, such as noise injection or secure multi-party computation, banks can protect sensitive customer information while maintaining the utility of the data for AI model training.

Federated Learning: Adopting federated learning frameworks allows banks to train AI models on decentralized customer data without compromising data privacy. This technique enables model updates to be performed locally on user devices while preserving the privacy of individual data.

Privacy-Preserving Data Sharing: Employing privacy-preserving techniques like homomorphic encryption or secure multi-party computation allows collaboration between banks and regulatory authorities while safeguarding customer data privacy. This facilitates compliance with regulatory requirements and enhances customer trust.

Challenges and Limitations of Generative AI in Banking

Overcoming implementation barriers

Implementing generative AI in banking requires careful planning and consideration. Several barriers need to be addressed to ensure successful adoption and integration. Here are some noteworthy challenges:

Infrastructure and Resource Requirements: Successful deployment of generative AI necessitates robust computational infrastructure. Banks must invest in high-performance computing systems with adequate storage capabilities to support the training and inference processes of AI models. Additionally, allocating sufficient processing power, such as Graphics Processing Units (GPUs) or specialized AI accelerators, is essential for achieving optimal performance.

Scalability and Efficiency: Generative AI models, such as Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs), are computationally intensive and resource-demanding. Banks must design scalable architectures and optimize algorithms to handle large-scale datasets and complex computations. Techniques like model parallelism, distributed training, and model compression can help improve efficiency and reduce computational overhead.

Ethical and Legal Considerations: The technical implementation of generative AI must address ethical and legal concerns. Ensuring fairness, transparency, and accountability in AI systems requires techniques such as explainable AI and algorithmic auditing. Banks need to develop guidelines and frameworks that govern AI operations, promote the ethical use of data, and address potential biases or unintended consequences.

Dealing with data quality and availability

The effectiveness of generative AI models heavily relies on the quality and availability of data. In the banking sector, the following challenges are encountered:

Data Privacy and Security: Banks deal with vast amounts of sensitive customer data, necessitating robust data privacy and security measures. Applying techniques like differential privacy, secure multi-party computation, and federated learning can help protect customer data during model training and inference. Encryption and anonymization techniques should be employed to minimize the risk of data breaches and ensure compliance with privacy regulations.

Data Bias and Imbalance: Addressing data biases and imbalances is crucial to prevent biased outcomes generated by AI models. Technical approaches such as data augmentation, oversampling, and undersampling can help mitigate bias in training datasets. Implementing bias detection and mitigation algorithms, including fairness metrics and adversarial training, can further enhance the fairness of generative AI models.

Data Integration and Accessibility: Banks often face challenges when integrating and consolidating data from heterogeneous sources. Technical solutions, such as data normalization, data cleansing, and data standardization, are necessary to ensure seamless integration of data from multiple systems. Establishing robust data governance frameworks, data pipelines, and data quality monitoring systems can enhance data accessibility and integrity.

Regulatory and compliance issues

The banking industry operates under strict regulatory frameworks to maintain stability, protect consumers, and prevent financial crimes. Integrating generative AI into this environment presents specific challenges:

Explainability and Interpretability: Regulatory agencies demand transparency and interpretability in AI systems. Techniques such as attention mechanisms, feature importance analysis, and rule-based explanations can provide insights into AI model decisions. Banks should explore interpretable AI models, such as rule-based systems or decision trees, to enhance explainability and meet regulatory requirements.

Anti-Money Laundering (AML) and Fraud Detection: Generative AI can contribute significantly to AML and fraud detection in banking. Technical advancements, such as anomaly detection algorithms, deep learning architectures, and graph-based analysis, can improve the accuracy and efficiency of AI-powered fraud detection systems. Continuous model monitoring and updates, coupled with collaboration between banks and regulatory bodies, are crucial to stay ahead of emerging threats.

Data Retention and Right to Erasure: Compliance with data retention policies while respecting customers' rights to data erasure poses technical challenges. Banks must develop mechanisms to manage data retention and erasure in generative AI systems effectively. Techniques like federated learning, decentralized storage, and secure data deletion protocols can help strike a balance between regulatory compliance and individual data rights.

Case Studies: Successful Implementations

Empowering Banks with Azure OpenAI Service:

To swiftly leverage the power of intelligence and drive operational efficiencies, banks are embracing the Azure OpenAI Service. This cutting-edge platform seamlessly integrates advanced models from OpenAI with the enterprise-grade capabilities of Microsoft Azure, providing banks with an accelerated path to deploying generative AI solutions. 

The key advantage of this integration is that all data, including training data and content, remains securely within the confines of the banks' own Azure tenants. Furthermore, by building on the Microsoft Cloud platform, banks gain access to robust enterprise-grade security features and role-based access controls. The recent introduction of GPT-4, OpenAI's most advanced Large Language Model (LLM) to date, elevates the precision and insight-generation potential for banks.

Transforming Banking Operations:

Writing Assistance and Content Generation: Generative AI serves as a game-changer in content generation and writing tasks within banks. Leveraging large pre-trained models, banks can now produce highly polished reports, summaries, and marketing materials with exceptional efficiency and accuracy. By automating content creation, generative AI empowers banks to streamline their operations while maintaining the human touch required for quality assurance.

Reasoning over Structured and Unstructured Data: Generative AI empowers banks to unlock valuable insights by conducting comprehensive reasoning over both structured and unstructured data. This capability facilitates informed decision-making, the identification of intricate patterns, and the discovery of hidden opportunities within vast and diverse data sources.

Summarization of Reports and Text: The extraction of pertinent information from extensive reports can be an arduous task. Generative AI simplifies this process by automatically summarizing reports, extracting key insights, and condensing substantial volumes of information into concise and digestible summaries. This invaluable feature saves time, enhances the responsiveness of advisors, and improves overall productivity.

Empowering Contact Center Agents

Contact centers act as crucial touchpoints for delivering exceptional customer experiences. Generative AI has revolutionized this domain, equipping contact center agents with invaluable tools to elevate customer interactions.

Generative AI empowers contact center agents to:

Summarize Conversations: Generative AI enables agents to swiftly summarize customer conversations, providing real-time insights and sentiment analysis throughout the entire interaction. This comprehensive understanding empowers agents to deliver personalized support and effectively address customer needs.

Real-time Coaching: Leveraging generative AI, supervisors can offer real-time coaching to contact center staff, enhancing agent performance during customer interactions. This dynamic guidance ensures consistent service quality and fosters continuous improvement.

Knowledge Base Enhancement: Generative AI enriches contact center knowledge bases by automatically extracting actionable insights from customer interactions. This iterative process facilitates faster response times, boosts customer satisfaction, and maximizes engagement levels.

Empowering Advisors: Enhanced Knowledge Search

For advisors in the banking industry, swiftly locating specific information within extensive documentation poses a significant challenge. Generative AI serves as a potent ally, offering enhanced knowledge search capabilities that expedite information retrieval.

Generative AI assists advisors through:

Powerful Summarization: Leveraging its contextualization and summarization capabilities, generative AI enables advisors to extract vital information from complex financial product documentation swiftly. This expedites responses to client inquiries, elevates decision-making, and fosters comprehensive client engagement.

Comparison Tables: Generative AI leverages its analytical prowess to generate visually compelling comparison tables summarizing key attributes of various financial products. This innovative visualization empowers advisors to effectively communicate complex information to clients, facilitating informed decision-making.

Content Generation: Accelerating Pitch Book Development

Pitch books serve as crucial components in investment banking, playing a pivotal role in proposing capital raises and mergers. Generative AI revolutionizes the development of pitch books, expediting the process while maintaining quality and accuracy.

Generative AI accelerates pitch book development by:

Automated Content Generation: Through generative AI, banks can automate the generation of pitch book content, collaborating with multiple sources such as client overviews, deal strategies, and marketing materials. Human oversight ensures the quality and precision of the generated content.

Iterative Improvement: Generative AI provides an iterative feedback loop, enabling continuous enhancement of pitch book content based on human oversight and feedback. This iterative process ensures that the generated content aligns with the desired standards of excellence.

The Future of Generative AI in Banking

Emerging Trends and Advancements

Advanced Fraud Detection and Prevention: Generative AI is at the forefront of combating fraud in the banking industry. Through deep learning algorithms, it can detect patterns, anomalies, and deviations within large datasets, providing real-time fraud alerts. Advanced generative models, such as Generative Adversarial Networks (GANs), analyze transactional data, customer behavior, and historical patterns to identify fraudulent activities with remarkable accuracy. By constantly learning from evolving threats, generative AI bolsters security measures, safeguarding both financial institutions and their customers.

Hyper-personalized Customer Experiences: Banking institutions are leveraging generative AI to deliver hyper-personalized customer experiences. By analyzing extensive customer data, including transaction history, financial goals, and preferences, generative AI algorithms generate tailored recommendations, product offerings, and financial advice. This level of personalization enhances customer satisfaction, fosters customer loyalty, and strengthens the overall relationship between banks and their customers.

Intelligent Risk Assessment and Management: Generative AI empowers banks to make informed decisions by providing intelligent risk assessment and management capabilities. Through advanced machine learning techniques, generative AI algorithms analyze market trends, historical data, and customer profiles to accurately assess credit risks and determine optimal lending decisions. This level of precision enables banks to minimize potential losses, optimize loan approvals, and maintain a healthy financial portfolio.

Autonomous Process Automation: Generative AI is driving process automation in banking operations, streamlining repetitive tasks and improving operational efficiency. Natural Language Processing (NLP) models, combined with robotic process automation (RPA), enable banks to automate customer support, document processing, and compliance tasks. By freeing up human resources from mundane activities, generative AI allows employees to focus on higher-value tasks, such as complex problem-solving and strategic decision-making.

Potential Impact on Job Roles and Workforce

Transformation of Traditional Banking Roles: Generative AI will reshape traditional banking roles, automating routine tasks and augmenting the capabilities of banking professionals. Data entry, document processing, and basic customer support will be automated, enabling employees to transition to more strategic roles that require human judgment, creativity, and critical thinking. This shift will increase the demand for professionals skilled in AI technologies, data analytics, and algorithm development.

Collaboration between Humans and AI: The integration of generative AI within banking operations emphasizes the collaboration between humans and AI systems. While AI automates repetitive tasks, human expertise remains invaluable in areas such as ethical decision-making, complex problem-solving, and establishing meaningful customer relationships. Banks will need to foster a culture that promotes collaboration between humans and AI, encouraging employees to work alongside AI systems to achieve optimal results.

Upskilling and Continuous Learning: The advent of generative AI necessitates continuous upskilling and learning for banking professionals. To thrive in this evolving landscape, employees must acquire expertise in AI technologies, data science, cybersecurity, and regulatory compliance. Financial institutions should invest in training programs and provide resources to support their workforce in acquiring the necessary skills and knowledge. This focus on upskilling will ensure a smooth transition to a future where generative AI plays a pivotal role in banking operations.

Conclusion

In conclusion, the emergence of generative AI in banking has ushered in a new era of technological innovation and transformation. This groundbreaking technology has the potential to revolutionize the way banks operate, enhance customer experiences, and drive unprecedented growth in the industry. From automating routine tasks to detecting fraudulent activities, generative AI is proving to be a game-changer.

By harnessing the power of generative AI, banks can now analyze vast amounts of data in real time, enabling them to make data-driven decisions with precision and agility. This technology empowers financial institutions to identify patterns, predict trends, and optimize operations, ultimately leading to improved efficiency, cost savings, and enhanced risk management.

As we look ahead, it is clear that generative AI will continue to reshape the banking landscape, enabling institutions to unlock new opportunities, streamline processes, and stay ahead of the competition. For both financial professionals and individuals, understanding the potential of generative AI and its implications will be essential to leverage its benefits fully.

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🤔 PayPal’s UK and European acquiring business is a bright spot. The UK and EU delivered 20% of overall revenue, growing 11% YoY. Square and Toast don’t have market share here, while iZettle, which PayPal acquired in 2018, is a strong market player. Overall though, it’s yet another tech stack and business that’s not part of a single global platform.

The two banks provided accounts to UK front companies secretly owned by an Iranian petrochemicals company. PCC has used these entities to receive funds from Iranian entities in China, concealed with trustee agreements and nominee directors. 

🤔 This is the headline every bank CEO fears. Oof. Shares of both banks have been down since the news broke, but this will no doubt involve crisis calls, committees, appearing in front of the regulator, and, finally, some sort of fine.

🤔 The "risk-based approach" has been arbitraged. A UK company with relatively low annual revenue would look "low risk" at onboarding. One business the FT covered looked like a small company at a residential address to compliance staff. They'd likely apply branch-level controls instead of the enterprise-grade controls you'd see for a large corporation. 

🤔 Hiring more staff won't fix this problem; it's a mindset and technology challenge. In theory, all of the skill and technology that exists to manage risks with large corporate customers (in the transaction banking divisions) are available to the other parts of a bank. In practice, they're not. Most banks lack a single data set and the ability for compliance officers in one team to see data from another part of the org. Getting the basics right with data and tooling is incredibly hard and will involve a multi-year effort. 

🤔 These things are rarely the failure of an individual or department; the issue is systemic. While two banks are named in this headline, the issue is everywhere. Banks need more data and better data to train better AI and machine learning. That all needs to happen in real-time as a compliment to the human staff. Throwing bodies at this won't solve the visibility issue teams have.

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What is XAH and Xahau?

If you're new to XRP, you may have noticed some of us discussing another network named 'Xahau'.

It's Like XRP ... But Different

The Xahau network was created in 2023, and its starting point was the open-source code for the XRP Ledger. A small team of researchers and entrepreneurs decided to add smart contracts to the network code.


The XRP Ledger has no smart contract capabilities, by default.

To integrate smart contracts, the team decided to use an architecture that includes 'WASM' or 'web assembly' code. Each account can have up to 10 'hooks' installed that are triggered for transactions that match specific criteria. They can run before or after a transaction is processed. This enables a variety of use cases that do not involve the need to change the network's core code.

Hooks

A 'hook' is what is known as a smart contract that can be triggered in relation to a specific account and its transactions.

The term arises from the programming world, where it generally means "code that runs based on triggering conditions." In Xahau's case, it indicates code that is run before, or after, a transaction is processed.
 
Each hook must be installed on a specific account by the party that controls the account - i.e., the secret key holder.
 
What Can XAH Do That XRP Cannot?
 
The primary benefit from the use of hooks, is that the core network code does not need to be changed every time a new use case is identified. This means that additional use cases can be addressed immediately, with no requirement for intervening steps, such as:
  • Community review
  • Community approval
  • Amendment voting
All of those steps are eliminated with the use of hooks; new use cases can be addressed as fast as the code can be developed.
 
To read more about how hooks enables Xahau to handle more use cases than even the XRPL, you can read this article:
 
Key Differences From XRP
 
Other unique differences from the XRP Ledger include:
  • Much smaller supply ~612 million coins vs. 100 billion coins
  • XAH hodlers are rewarded at 4% of their account balance. There are no rewards for XRP.
  • Governance participants are incentivized
  • Payment channels available for user-created tokens (IOUs)
  • URI tokens instead of NFT tokens
Who's Who of Xahau?
 
The list of those that are either founders, or closely associated with the founding organizations, is extensive. Here are the names of three organizations mentioned in the whitepaper, or their current moniker:
  • Xaman (a.k.a. XRPL Labs)
  • Gatehub
  • InFTF (Inclusive Financial Technology Foundation)
There exists a long list of impressive developers, architects, and technologists among the Xahau inner circle. But the three names that people associate most prominently with the leadership of the Xahau network are Wietse Wind, Richard Holland, and Denis Angell. The links to their 'X' accounts are:
 
Friend Or Foe?
 
This topic is one of the most contentious.
 
While Ripple, the company with the largest stake of XRP, showed interest in hooks early on, they ultimately decided to advocate for a different approach; the use of an EVM-based solution (Ethereum Virtual Machine) to handle smart contracts on the XRP Ledger. This decision was met with consternation by the Xaman team that had worked with them for several years to advocate for the use of hooks.
 
You can read more about the 'business politics' part of this topic here:
 
So how do Xahau fans view the relationship between XRP and XAH?
 
The Xahau team - and many of its community members - advocate for the use of a 'dual-chain' solution to implement smart contracts. This can be accomplished by the use of 'listener' software, along with native Xahau hooks.
 
A proof of concept, developed by Denis Angell, has demonstrated that bi-lateral communication can work with a simple approach.
 
From an economic standpoint, every chain that has its own digital asset is a competitor; but the simple way to think about Xahau, is that a 'bunch of XRP geeks' decided to implement smart contracts on their own version of the XRP Ledger.
 
The team emphasized transparency along the way, and initially received support from the primary XRP stakeholder, Ripple. They published Xahau as open-source code that could, in theory, be back-engineered and integrated with the XRP Ledger. You can clearly observe the team's idealistic mindset in early marketing mistakes, where they named their digital asset 'XRP Plus' in an effort to emphasize the way that they viewed their creation. While this resulted in confusion - and even suspicion - in its early days, the team quickly pivoted, and named their digital asset 'XAH', which became its ticker symbol.
 
Synergy effects between the two camps speak to a genuine camaraderie, with many Xahau developers being open and willing to help with changes to the core XRP Ledger protocol. You can find many examples of this open dialogue on the 'X' platform.
 
How To Purchase XAH
 
If you wish to speculate by buying XAH directly, it is available in a variety of convenient locations, depending on where you are located. If you're in a country that is supported by Bitrue, you can directly purchase or trade XAH by using that exchange.
 
On January 20th, 2025, Bitmart announced that it supports trading of XAH for customers in their list of supported countries; And in late March, another major exchange announced that they would be supporting XAH trading pairs: Coinex.
 
If you're located in the United States, you can purchase XAH directly from a vendor known as 'C14'. The xApp for C14 is located in the Xaman wallet.
 
XRP Ledger geeks can also purchase XAH IOUs on the XRPL Dex and then convert them to 'real' XAH using a Gatehub bridge. This is available in countries that Gatehub supports.
 
Which XAH Accounts Should I Follow?
 
On the 'X' platform, there exists two major community groups for XAH fans:
In addition to the Xahau notables I've already mentioned in this article, my advice is to take a look at who is posting in the above two communities. There are many impressive leaders and entrepreneurs included. You should be able to find multiple 'X' accounts that reflect your interests.
 
Xahau Development Roadmap
 
Xahau leaders have published a roadmap for 2025 that lists their various goals for the ecosystem:
 
To read a detailed explanation for each item, refer to this: Xahau Roadmap Super Thread
 
One of the most incredible waypoints listed is 'JavaScript Hooks Implementation.' 🤯
JavaScript!
 
With the 'JavaScript Hooks Implementation', Xahau is making history; it will enable anybody that knows JavaScript to easily create and install a smart contract. While networks like Ethereum are impressive early movers, they require developers to learn a new language and syntax.
 
Xahau will soon open 'crypto smart contracts' to a group of developers that number in the tens of millions.
 
Project L-10K
 
Project L-10K is one of the most important items in the pipeline. L-10K refers to the effort to boost the throughput of Xahau consensus to over 10,000 transactions per ledger! This will benefit hosted projects such as Evernode, and future issued assets. Heading up the effort is Richard Holland, who provided a progress update to the community in late May of 2025:
 
To learn more about this ambitious effort, you can watch his full presentation here:
The Future Of Defi And Payments
 
Once you've seen the extensive list of use cases that XAH easily handles, it's truly inspiring. Xahau is everything that you love about XRP, plus a long list of more things to love. ❤️
 
Be an early adopter of XAH and the Xahau network! Join the community groups listed and follow the accounts that seem to reflect your own interest - speculator, developer, or crypto fan. You have a place in our community, no matter what your background or interests are. Welcome to the future of crypto Defi and Payments
 
Sources:
 
 
NOTE: Payment channels for IOUs is currently in amendment status for the XRP Ledger, authored by Denis Angel here:
 
 

🙏 Donations Accepted 🙏

If you find value in my content, consider showing your support via:

💳 PayPal: 

1) Simply scan the QR code below 📲
2) or visit https://www.paypal.me/thedinarian

🔗 Crypto – Support via Coinbase Wallet to: [email protected]

Or Buy me a coffee: https://buymeacoffee.com/thedinarian

Your generosity keeps this mission alive, for all! Namasté 🙏 Crypto Michael ⚡ The Dinarian

 
 
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