The future of financial technologies depends on AI

Maciej Zieliński

03 Jun 2020
The future of financial technologies depends on AI

AI has changed the way financial institutions collect and analyze data over the last few years. It has transformed the business environment by challenging companies and creating innovative business models.

In January this year, the Cambridge Centre for Alternative Finance together with the University of Cambridge Judge Business School and the World Economic Forum presented the results of research on the impact of AI on the financial sector.  From the responses of 151 institutions from 33 countries, a clear picture of AI as a key business driver emerged. Companies are beginning to see how much potential there is in artificial intelligence - most of the FinTechs surveyed already use it to create new services and products.

Application of AI in financial technologies

One of the most important conclusions drawn from the study is the rapidly changing importance of artificial intelligence in everyday business. Approximately 64% of financial institutions expect to use AI in the next two years in process automation, risk management, customer acquisition and service, as well as the creation of new products. Today, only 16% of companies participating in the survey do so. On the other hand, as many as three quarters of respondents expect that artificial intelligence will be very important in the development of the financial services industry in the short term.

As McKinsey & Company writes in its analysis, "companies that have made the strategic decision to implement AI on a full scale and in key business areas quickly see the value of this decision". They achieve attractive return on investment, grow faster and have much higher margins than companies that do not invest in artificial intelligence.

What exactly can AI help in the world of financial technologies? It enables a faster, more accurate assessment of a potential borrower, at a lower cost, taking into account a wider range of factors; it has enormous processing power and helps manage both structured and unstructured data; it is very effective in preventing credit card fraud and relieving customer service centres by powering smart chatbots. And these are just a few examples of how artificial intelligence can streamline your daily business.

Practical applications of artificial intelligence

One of the companies that already take advantage of AI is Underwrite.ai, which processes thousands of data to assess credit risk for people and companies applying for loans. Traditionally, analyses are impractically expensive and too slow to be used effectively in financial institutions in real time. By using artificial intelligence, this process has been optimized so that advanced credit analysis can be used without unnecessary time and large investments.

Examples of revolutionary applications of artificial intelligence in financial technologies can be multiplied. One of them is Kavout, an investment platform using AI to process huge sets of unstructured data and identify patterns in real time in financial markets. Another, Ayasdi uses existing data sets to help financial institutions detect mortgage fraud and money laundering, maximize liquidity and predict customer behaviour. 

Another is Kasisto, who also uses AI in his proposed solutions. For financial institutions, a KAI chatbot has been created, which helps to reduce the number of call centres by offering customers self-service solutions and additionally assists in making daily financial decisions.

- Financial institutions need technology that will help them better engage customers and reach new market segments, while building a stronger brand, said Zor Gorelov, CEO of Kasisto, quoted by PR Newswire. - Kasisto is the best AI conversation platform implemented in large banks around the world, working with millions of users in different countries in multiple languages and channels.

Another example, Feedzai, helps banks manage risk by monitoring transactions and alerting customers in case of suspicious changes in payment behaviour before processing payments. Feedzai has established cooperation with Citibank among others.

The Simundyne platform can also be an interesting example. It allows for quick and safe simulations, which allow for testing an unlimited number of scenarios in a safe environment. 

- After several years of consultations about virtual reality simulation and risk modeling conducted with many global institutions, I realized that traditional modeling methods are no longer up to date - admitted Justin Lyon, CEO of Symudyne, in an interview with MarketsMedia, explaining what prompted him to create the platform. - They do not capture the complexity of real systems and cannot effectively use the enormous power of technology and large data sets.

The Cambridge Centre for Alternative Finance study showed that although technology is a key element of further progress, it does not stand in the way of mass deployment of artificial intelligence. Existing solutions for years have still not been implemented by many companies dealing with financial services on a daily basis, which is attributed to obstacles in the form of lack of trust, complicated regulations and large amount of needed data. Artificial Intelligence can help you to take your business to the next level of sophistication and fully exploit its potential.

Tagi

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Nextrope Launches “AI-Powered Smart Contract Auditing” Project

Miłosz Mach

03 Mar 2025
Nextrope Launches “AI-Powered Smart Contract Auditing” Project

Next Enterprises Sp. z o.o. is implementing a project co-financed by the European Funds, titled "Smart Contract Auditing with Artificial Intelligence". The goal of the project is to develop and deploy an advanced AI model that enables efficient analysis, vulnerability detection, and security auditing of smart contracts, taking into account their complexity and uniqueness.

Planned Project Tasks:

  • Development of an AI model trained on Solidity keywords;
  • Creation of an effective model in simulated conditions;
  • Analysis of the unpredictability of compiled code execution within the Ethereum Virtual Machine (EVM) in the context of the developed model in a controlled environment;
  • Validation of the model in real-world conditions.

Target Groups:

  • Specialized audit firms focused on smart contract security;
  • Companies developing and/or deploying smart contracts on various platforms;
  • Exchanges, wallet providers, and decentralized applications (dApps) in the blockchain sector;
  • Government agencies or industry compliance bodies responsible for blockchain technology regulation;
  • Smart contract security specialists and developers.

The implementation of the developed tool will enable automated and efficient auditing of smart contracts. The model will provide detailed insights and recommendations for optimizing transaction costs and improving contract performance. As a result, users will be able to make informed decisions, enhancing security and operational efficiency within the blockchain ecosystem. Key benefits stem from the model’s training on smart contract code, audit data, and detected vulnerabilities. Additionally, the incorporation of chaos theory principles will allow for more precise risk and anomaly forecasting.

By deploying this advanced AI model, the project will enhance the security, efficiency, and accessibility of blockchain technology for end users. This will translate into tangible social and economic benefits, including:

  1. Economic Security
  2. Business and Financial Security
  3. Increased Public Trust
  4. Optimization of Transaction Costs
  5. Support for Innovation and Entrepreneurship
  6. Education and Public Awareness

Project Value: 4,173,953.24 PLN
European Funds Contribution: 3,090,156.39 PLN

#EUFunds #EuropeanFunds

Challenges in Smart Contract Auditing

Smart contracts have become a fundamental component of blockchain technology, eliminating intermediaries, and automating processes. However, their growing significance also introduces new challenges, particularly in ensuring security and compliance with industry standards.

Traditional smart contract audits rely heavily on manual code reviews, which are expensive, time-consuming, and prone to human error. As cyber threats continue to evolve, the use of advanced technologies to support the auditing process is imperative.

The Role of AI in Data Analysis

Artificial intelligence (AI) introduces a new paradigm in smart contract security assessment by leveraging its capability to process vast amounts of data and identify patterns that may go unnoticed with traditional auditing methods. AI enables:

  • Automated code analysis and real-time detection of potential vulnerabilities,
  • Optimization of auditing processes by reducing human errors and improving threat identification efficiency,
  • Better adaptation to evolving regulatory requirements and emerging threats within the blockchain ecosystem,
  • Rapid analysis of large datasets, allowing for quick insights and the detection of non-obvious dependencies in smart contract code.

By utilizing AI, the auditing process becomes more comprehensive, precise, and scalable, enabling continuous risk monitoring and adaptation to new attack vectors.

A New Era of Smart Contract Security with AI

With the support of European Funds under the European Funds for a Modern Economy (FENG) program, we are conducting research on next-generation blockchain auditing methods, reinforcing Nextrope’s position as a leader in innovative technology solutions.

The "Smart Contract Auditing with Artificial Intelligence (AI)" project contributes to key aspects of blockchain security by:

  • Automating smart contract audits, accelerating verification processes, and improving their accuracy,
  • Optimizing costs, making professional audits more accessible to a broader range of entities,
  • Raising security standards and enhancing regulatory compliance,
  • Increasing trust in smart contracts, fostering broader technology adoption.

Interested in learning more about our project or discovering how to utilize AI in your company? 📩 Contact us at contact@nextrope.com for further details!

Tagi

How NOT to Create a DAO: Common Pitfalls You Should Avoid

Kajetan Olas

27 Dec 2024
How NOT to Create a DAO: Common Pitfalls You Should Avoid

Decentralized Autonomous Organizations (DAOs) represent a fundamental shift in how communities, companies, and initiatives can coordinate efforts, funds, and decisions on the blockchain. By leveraging transparent smart contracts and on-chain governance mechanisms, DAOs aim to distribute authority, reduce overhead, and foster a more democratic decision-making process. However, building a successful DAO isn’t just about cutting-edge tech or grand ideas—it also requires a clear vision, well-crafted governance rules, and a strategically engaged community.

In this article, we’ll take a counterintuitive approach by highlighting how not to create a DAO. By focusing on common pitfalls—from legal oversights to governance missteps—we can better understand what truly contributes to a thriving, sustainable DAO. This perspective aligns with the importance of recognizing cognitive biases, such as insensitivity to base rates and the conjunction fallacy, which often lead enthusiastic founders to overlook real-world data and complexity. Avoiding these traps can be the difference between launching a resilient DAO and watching an ambitious project crumble under misaligned structures or unmet expectations.

2. Missing the Governance Threshold Mark

Governance Thresholds Gone Wrong

Governance thresholds dictate how many votes or what percentage of voting power is needed to pass a proposal within a DAO. Striking the right balance here is crucial. Thresholds that are set too high can stifle progress by making it nearly impossible for proposals to succeed, effectively discouraging member participation. On the other hand, thresholds that are too low can lead to frivolous proposals or constant voting spam, making governance more of a burden than a benefit.

When designing your DAO’s thresholds, consider:

  • Community size and engagement levels: Larger communities might handle higher thresholds more comfortably, while smaller groups may benefit from lower requirements to encourage active participation.
  • Type of proposals: Operational decisions may need a lower threshold, whereas critical changes (such as tokenomics or treasury management) often require more consensus.
  • Voter fatigue: The more frequently members are asked to vote—and if it’s too easy to put forward proposals—the greater the risk of apathy or disengagement.

Over-Complex vs. Over-Simplified Governance

It’s tempting to either pile on complicated governance rules or oversimplify them to keep decision-making quick. However, both extremes can be problematic. Simplicity in governance is key to enhancing clarity and participation. Overly complex smart contracts and procedural layers can dissuade newcomers from getting involved, while an oversimplified model might fail to address potential conflicts or security vulnerabilities.

Some issues to watch out for:

  • Complex Smart Contracts: More code means more potential bugs and greater difficulty in auditing or updating governance logic.
  • Opaque Voting Processes: If members can’t easily understand how votes are tallied or how proposals are introduced, engagement drops.
  • Excessive Centralization in “Simple” Models: In trying to streamline governance, some DAOs inadvertently concentrate power in the hands of a few decision-makers.

Ultimately, aiming for a balanced governance framework—one that is easy enough for members to participate in but comprehensive enough to protect the DAO from abuse—is central to avoiding the pitfalls of governance threshold mismanagement.

3. Underestimating Legal and Regulatory Aspects

Legal Wrappers and Compliance

Building a DAO without considering legal and regulatory frameworks is a common recipe for disaster. While decentralization is a powerful concept, it doesn’t absolve projects from potential liabilities and compliance obligations. Assigning your DAO a formal legal wrapper—whether it’s a foundation, a cooperative, an LLC, or another entity type—can help mitigate personal risks for contributors and align your organization with existing regulatory regimes.

Failing to think through these details often leads to:

  • Personal Liability for Founders: Without a proper legal entity, core contributors might become personally responsible for any legal disputes or financial mishaps involving the DAO.
  • Regulatory Crackdowns: Governing bodies worldwide are actively monitoring DAOs for compliance with securities laws, anti-money laundering (AML) regulations, and tax obligations. Ignoring these can lead to penalties, fines, or forced shutdowns.

Non-Existent or Inadequate Documentation

Equally problematic is the lack of clear documentation outlining the DAO’s legal structure and operational protocols. From voting procedures to treasury management, every aspect of the DAO’s lifecycle should be properly documented to reduce ambiguity and help new members understand their responsibilities. Inadequate documentation or outright neglect can create:

  • Confusion Over Roles and Responsibilities: Without explicit definitions, it’s easy for tasks to fall through the cracks or for disagreements to escalate.
  • Inability to Enforce Rules: DAOs rely on both smart contracts and social consensus. Formalizing rules in documentation helps ensure consistent enforcement and prevents unwelcome surprises.

In short, underestimating the legal dimension of DAO creation can derail even the most innovative projects. By proactively addressing legal and regulatory considerations—and maintaining thorough documentation—you not only protect core contributors but also fortify trust within your community and with external stakeholders.

Overlooking Community Building

The Importance of Community Engagement

A DAO, at its core, is nothing without an active and supportive community. Driving grassroots enthusiasm and participation is often the deciding factor between a thriving DAO and one that fizzles out. Yet, it’s surprisingly easy to underestimate just how vital it is to nurture community trust and engagement—especially during the early stages.

Some common pitfalls include:

  • Treating Community Members as Passive Observers
    Instead of viewing your community as a dynamic force, you might slip into a one-way communication style. This discourages members from taking initiative or contributing fresh ideas.
  • Lack of Clear Roles and Channels
    Without well-defined roles and open communication channels—like forums, Discord servers, or governance platforms—members can feel confused about where to participate or how to add value.
  • Ignoring Early Feedback
    In a DAO, the “wisdom of the crowd” can be a powerful asset. Overlooking or trivializing user feedback can lead to missed opportunities for innovation and improvement.

Failing to Incentivize Properly

Well-structured incentives lie at the heart of any successful DAO. Whether you’re offering governance tokens, staking rewards, or recognition badges, these incentives must be aligned with the DAO’s long-term goals. Misalignment often causes short-sighted behavior, where participants chase quick rewards rather than contributing meaningfully.

  • Overemphasis on Token Speculation
    If the primary draw for community members is the promise of quick token price gains, you risk attracting speculators instead of builders. This can lead to fleeting participation and sell-offs at the first sign of trouble.
  • Neglecting Non-Monetary Rewards
    Recognition, social standing, and meaningful collaboration can be just as powerful as financial incentives. When a DAO fails to provide pathways for skill development or leadership, member engagement wanes.
  • Cognitive Bias Traps
    Biases such as the conjunction fallacy can mislead founders into believing that if multiple positive outcomes are possible (e.g., rising token prices, active participation, mainstream adoption), then all those outcomes will inevitably happen together. This wishful thinking can blind DAOs to the need for thoughtful, data-driven incentive models.

To avoid these pitfalls, DAO creators must actively foster a culture of transparency, collaboration, and mutual respect. By setting clear expectations, leveraging diverse incentive structures, and consistently involving community feedback, you ensure members are motivated to contribute more than just their votes—they become co-creators in the DAO’s shared vision.

5. Ignoring Technical Considerations

Token Standards and Governance Frameworks

A solid technical foundation is essential when you create a DAO, particularly if it involves on-chain governance. Selecting the appropriate token standards and governance frameworks can significantly impact your DAO’s security, efficiency, and scalability.

Some pitfalls to watch out for include:

  • Choosing Incompatible Token Standards
    If your DAO relies on a token that isn’t easily integrated with governance contracts or lacks upgradeability, you might face roadblocks when implementing new features or patching vulnerabilities.
  • Underestimating Smart Contract Complexity
    Even “simple” governance tokens can hide complex logic behind the scenes. Overlooking these complexities may result in bugs, lockouts, or exploits that harm the DAO’s reputation and finances.
  • Ignoring Off-Chain vs. On-Chain Dynamics
    Governance strategies often combine on-chain decisions with off-chain discussions (e.g., using platforms like Discord or forums). Failing to synchronize these two spheres can fracture community engagement and hamper decision-making.

Poor Architecture and Security

Robust security isn’t just about preventing hacks—it's about building an architecture that can adapt to evolving threats and changing community needs.

Key oversights include:

  • Inadequate Auditing
    Smart contracts require thorough reviews, both automated and manual. Rushing to mainnet deployment without proper audits can lead to major losses—financial, reputational, or both.
  • No Contingency Plans
    If a vulnerability is discovered, how will you respond? Lacking emergency procedures or fallback governance mechanisms can leave a DAO paralyzed when critical decisions must be made quickly.
  • Over-Engineered Solutions
    While security is paramount, over-complicating the DAO’s architecture can create unintended vulnerabilities. Keeping your setup as simple as possible reduces attack surfaces and makes it easier for community members to understand and trust the system.

In short, technical considerations form the bedrock of a functional DAO. Choosing appropriate token standards, thoroughly auditing contracts, and designing for both present-day and future needs are non-negotiable steps in avoiding costly pitfalls.

Best Practices and Lessons

When studying successful DAOs, certain themes emerge time and again. According to Aragon the most robust DAOs share a commitment to simplicity, iteration, and transparent governance. Instead of rolling out overly sophisticated models from day one, they evolve and adapt based on community feedback and real-world performance.

Here are a few best practices worth emulating:

  • Iterative Approach to Governance
    Start small and build up. Launch a Minimal Viable DAO (MVD) to test voting processes, incentive mechanisms, and proposal management. Gather community feedback and refine before taking bigger steps.
  • Simple, Transparent Rules and Processes
    Ensure proposals are easy to understand and that the voting process is accessible to all token holders. Overly complicated frameworks can dissuade new members from participating.
  • Clear Roles and Shared Responsibilities
    Define contributor and community member roles early on. Whether you rely on working groups, committees, or elected leaders, clarity prevents power vacuums and fosters collaboration.
  • Open Communication and Education
    From Discord channels to public documentation, keep conversation and learning at the heart of your DAO. Encourage members to ask questions, propose improvements, and take leadership roles.

Academic Perspectives

Beyond practical experience, a growing body of research offers theoretical insights that can strengthen DAO governance. The discusses emerging patterns in DAOs, including how incentives and on-chain rules interact with off-chain social dynamics. By examining these findings, DAO creators can better anticipate challenges—like voter apathy, whale influence, or collusion—and integrate solutions from the outset.

Incorporating academic perspectives can help:

  • Validate Governance Assumptions
    Empirical data and rigorous analyses can confirm or challenge the assumptions behind your DAO’s architecture, preventing costly mistakes.
  • Stay Ahead of Regulatory and Social Shifts
    Academics often explore how upcoming policies or societal trends might impact DAOs, offering a forward-looking lens that day-to-day builders might miss.
  • Establish Credibility
    Aligning your DAO’s structure and operations with recognized research signals professionalism and thoroughness, potentially attracting more serious contributors, partners, and investors.

Conclusion

As you can see, creating a DAO involves more than just deploying a smart contract and distributing tokens. By examining these common pitfalls—from poor governance thresholds to inadequate legal structures, from neglecting community engagement to disregarding technical complexities—you gain a clearer picture of what not to do when you set out to create a DAO. Failing to address these areas often leads to compromised security, stalled decision-making, regulatory headaches, or outright community collapse

At Nextrope, we specialize in tailored blockchain and cryptocurrency solutions, including DAO creation and tokenomics design. If you’re looking to avoid these common pitfalls and build a thriving DAO that stands the test of time, feel free to contact us or explore more resources on our blog.