Data Broker Email List

The #1 site to find Data Broker Email Lists and accurate B2B & B2C email lists. Emailproleads.com provides verified contact information for people in your target industry. It has never been easier to purchase an email list with good information that will allow you to make real connections. These databases will help you make more sales and target your audience. You can buy pre-made mailing lists or build your marketing strategy with our online list-builder tool. Find new business contacts online today!

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First Name
Last Name
Phone Number
Address
City
State
County
Zip
Age
Income
Home Owner
Married
Property

Networth
Household
Cradit Rating
Dwelling Type
Political
Donor
Ethnicity
Language Spoken
Email
Latitude
Longitude
Timezone
Presence of children
Gender

DOB
Birth Date Occupation
Presence Of Credit Card
Investment Stock Securities
Investments Real Estate
Investing Finance Grouping
Investments Foreign
Investment Estimated
Residential Properties Owned
Traveler
Pets
Cats
Dogs
Health

Institution Contributor
Donates by Mail
Veteranin Household
Heavy Business
Travelers
High Tech Leader
Smoker
Mail Order Buyer
Online Purchasing Indicator
Environmental Issues Charitable Donation
International Aid Charitable Donation
Home Swimming Pool

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Our email list is divided into three categories: regions, industries and job functions. Regional email can help businesses target consumers or businesses in specific areas. Data Broker Email Lists broken down by industry help optimize your advertising efforts. If you’re marketing to a niche buyer, then our email lists filtered by job function can be incredibly helpful.

Ethically-sourced and robust database of over 1 Billion+ unique email addresses

Our B2B and B2C data list covers over 100+ countries including APAC and EMEA with most sought after industries including Automotive, Banking & Financial services, Manufacturing, Technology, Telecommunications.

In general, once we’ve received your request for data, it takes 24 hours to first compile your specific data and you’ll receive the data within 24 hours of your initial order.

Our data standards are extremely high. We pride ourselves on providing 97% accurate Data Broker Email Lists, and we’ll provide you with replacement data for all information that doesn’t meet your standards our expectations.

We pride ourselves on providing customers with high quality data. Our Data Broker Email Database and mailing lists are updated semi-annually conforming to all requirements set by the Direct Marketing Association and comply with CAN-SPAM.

Data Broker Contact Lists

Emailproleads.com is all about bringing people together. We have the information you need, whether you are looking for a physician, executive, or Data Broker Email Lists. So that your next direct marketing campaign can be successful, you can buy sales leads and possible contacts that fit your business. Our clients receive premium data such as email addresses, telephone numbers, postal addresses, and many other details. Our business is to provide high-quality, human-verified contact list downloads that you can access within minutes of purchasing. Our CRM-ready data product is available to clients. It contains all the information you need to email, call, or mail potential leads. You can purchase contact lists by industry, job, or department to help you target key decision-makers in your business.

If you’re planning to run targeted marketing campaigns to promote your products, solutions, or services to your Data Broker Email Database, you’re at the right spot. Emailproleads dependable, reliable, trustworthy, and precise Email List lets you connect with key decision-makers, C-level executives, and professionals from various other regions of the country. The list provides complete access to all marketing data that will allow you to reach the people you want to contact via email, phone, or direct mailing.

Data Broker Email leads
Data Broker Email leads

Our pre-verified, sign-up Email marketing list provides you with an additional advantage to your networking and marketing efforts. Our database was specifically designed to fit your needs to effectively connect with a particular prospective customer by sending them customized messages. We have a dedicated group of data specialists who help you to personalize the data according to your requirements for various market movements and boost conversion without trouble.

We gathered and classified the contact details of prominent industries and professionals like email numbers, phone numbers, mailing addresses, faxes, etc. We are utilizing the most advanced technology. We use trusted resources like B2B directories and Yellow Pages; Government records surveys to create an impressive high-quality Email database. Get the Email database today to turn every opportunity in the region into long-term clients.

Our precise Email Leads is sent in .csv and .xls format by email.

Data Broker Email Lists has many benefits:

Adestra recently conducted a survey to determine which marketing channel was the most effective return on investment (ROI). 68% of respondents rated email marketing as ‘excellent’ or ‘good.

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Segment your list and target it effectively:

Your customers may not be the same, so they should not receive the same messages. Segmentation can be used to provide context to your various customer types. This will ensure that your customers get a relevant and understandable message to their buying journey. This allows you to create personalized and tailored messages that address your customers’ needs, wants, and problems.

Data Broker email leads
Data Broker email leads

Segmenting your prospects list by ‘who’ and what is the best way to do so. What they’ve done refers to what they have done on your website. One prospect might have downloaded a brochure, while another person may have signed up for a particular offer. A good email marketing service will let you segment your list and automate your campaigns so that they can be sent to different customer types at the time that suits you best.

Almost everyone has an email account today. There will be over 4.1 billion people using email in 2021. This number is expected to rise to 4.6 billion by 2025. This trend means that every business should have an email marketing list.

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Businesses can market products and services by email to new clients, retain customers and encourage repeat visits. Email Lists marketing can be a great tool for any business.

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One email per week is all it takes to establish unbreakable relationships with customers.

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A business must have an email list to use email marketing. You will need a strategy to capture these email addresses.

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Emailproleads are a long-standing way to market products and services outside the business’s database. It also informs existing customers about new offerings and discounts for repeat customers.

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Talk to our friendly team about how we can help you decide who should be included in your future email list.

The #1 site to find business leads and accurate Data Broker Email Database Lists. Emailproleads.com provides verified contact information for people in your target industry. It has never been easier to purchase an email list with good information that will allow you to make real connections. These databases will help you make more sales and target your audience. You can buy pre-made mailing lists or build your marketing strategy with our online list-builder tool. Find new business contacts online today!

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Data Broker Email list

What is the impact of AI altering some aspects of financial markets?

AI methods are utilized to asset management and buy-side market for allocation of assets and stock selection using ML models’ capability to recognize signals and to capture the underlying relationships that exist in large data. They also serve to improve operations workflows as well as risk control. The application of AI techniques can be restricted to large institutions or asset managers with the resources and capacity to invest in these techniques. buy Data Broker database for marketing

Data Broker Email leads
Data Broker Email leads

When utilized in trades, AI adds a layer of complexity to the traditional trading using algorithms, since the algorithms learn from inputs from data and then evolve into computer-programmed algorithms that can identify and then execute trades with no human intervention. In highly digitalized markets such as FX and equity marketplaces, AI algorithms can enhance the management of liquidity as well as execute large orders, with minimal market impactby optimizing length, size, and order size in a dynamic manner dependent upon market trends. The traders can also employ AI to manage risk and to manage order flow to speed up execution and create efficiency. 

Similar to non-AI models as well as algos, the usage of the similar ML models used in a wide range of finance professionals could result in herding behavior and one-way market, which could pose risks for stability and liquidity of the system, especially in situations of extreme stress. While AI algo trading could enhance liquidity during periods of normality, it could cause convergence, and, as a result, to periods of insolvency during times of stress as well as flash crashes. Market volatility can increase due to massive purchases or sales that are that are executed at the same time, leading to new vulnerability sources. The convergence of trading strategies increases the potential for self-reinforcing feedback loops which could result in abrupt price movements. The convergence of these strategies also increases the likelihood of cyber-attacks because it is easier for cybercriminals to influence other agents operating similarly. These risks are present in every type of trading algorithms, however using AI increases the risk due to their capacity to continuously learn and adapt to changing circumstances in a completely self-sufficient manner. For instance, AI models can identify signals and study the consequences of herding and adjust their behavior and learning to advance upon the first signals. The level of complexity and the difficulty of explaining and replicating the decision-making process of AI models and algorithms make it difficult to manage the dangers. 

 

Machine Learning, ARTIFICIAL Intelligence and BIG Data in Finance (c) The OECD 2021

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AI technology could increase the risk of illegal practices in trading that aim to manipulate markets and make it harder for supervisors to spot these practices when collusion between AI models is present. This is because of the dynamic adapting capability of self-learning models as well as deep-learning AI models, since they recognize interdependencies between themselves and change their behavior to match the actions and actions of market participants or AI models, and possibly reach an outcome that is collusive without human intervention, and possibly without the user having any idea. 

Figure 2. Effects of AI on business models and activities in the financial sector

Asset Management Credit intermediation

Recognize signals, and capture the the underlying patterns in massive data

Improve operational workflows, improve managing risk

Potentially, alpha-generating

Problems with concentration, competition, and other 

Source: OECD Staff.

AI models in lending may lower the costs associated with credit-related underwriting as well as facilitate the credit extension process to clients with thin file which could lead to financial inclusion. The application of AI could result in efficiencies in data processing in order to assess of the creditworthiness of potential clients, improve the underwriting process and enhance the loan portfolio management. This can also permit the provision of credit scores to clients who have low credit histories, which can help in financial inclusion of small and medium-sized enterprises (SMEs) and, potentially, encouraging financial inclusion for people who are not banked. 

Despite their enormous potential, AI-based algorithms and the use of inaccurate information (e.g. regarding race or gender) in lending could pose the possibility of a disparate impact on results of credit and the potential for bias, discriminatory or unjust lending. As well as inadvertently creating biases or perpetuating them in credit allocation, AI-driven models can make the discrimination in credit allocation more difficult to detect and make the outputs of the model are difficult to understand and communicate to potential customers. This is especially true for credit offered by BigTech which rely on access to huge amounts of customer information, which raises concerns about anti-competitive practices and market dominance in the technological aspect of providing services (e.g. cloud). 

The application in the use of AI techniques in finance based on blockchain could increase the efficiency potential of DLT-based systems, and enhance potential of smart contract capabilities. AI could increase the reliability of smart contracts, which allows the code that runs them to adjust dynamically in response the market’s conditions. The application in the use of AI for DLT systems also brings or even increases problems that arise in traditional financial products, including the difficulty of understanding AI decision-making systems and the difficulty managing systems and networks that are based on obscure AI models. In the present, AI is mostly being utilized to manage risk for smart contracts, as well as for detection of weaknesses inside the program. It is worth noting however that smart contracts were in existence since before the invention in AI applications and depend on simple software. In the present, the majority of smart contracts utilized in a tangible way are not tied to AI methods and the majority of the benefits that are attributed to the application for AI within DLT systems is still in the realm of speculation at the moment.

Machine Learning, ARTIFICIAL Intelligence and BIG DATA IN Finance (c) The OECD 2021

Data Broker mailing lists

Data Broker Email listing
Data Broker Email listing

In the near future, AI could support decentralised applications of financial decentralisation (‘DeFi’), by automatising credit scoring based upon users’ data on the internet such as investment advisory services and trading using financial data and insurance underwriting. In the future artificial intelligence-based smart contracts which can be self-learning1 and adapt dynamically without human intervention may lead to the creation completely autonomous chains. The application of AI could further facilitate disintermediation, by replacing off-chain third-party sources of information by AI inference directly on the chain. It is important to note however that AI-based systems cannot necessarily solve issues with the garbage in garbage out dilemma as well as the issue of poor quality or insufficient data inputs blockchain-based systems. This is what gives an opportunity for serious risks for investors and market integrity as well as the reliability of the systems according to what size market size. DeFi market. In addition, AI could amplify the many risks that are present with DeFi market, adding more complexity to alre

ady challenging to supervise autonomous DeFi networks without a single access points to regulatory oversight or governance structures that permit accountability and the compliance of the oversight frameworks.

The most important risks and challenges and mitigation options

The introduction in the use of AI in finance may increase the risks that exist in the financial sector due to the capacity of AI to adapt and learn to changes in the market in a completely autonomous manner and create new challenges and risk. Risks currently in place are due to the use of data in a non-optimal way and the use of low quality data, which could cause biases and discriminatory outcomes, which ultimately hurt the financial consumer. Risks of concentration and associated problems with competition could arise from the requirements for investment of AI methods, which may result in dependence on only a few major players. Risks to market integrity and compliance may result from the absence of an adequate model governance framework which takes into consideration the unique nature of AI and the absence of clearly defined accountability frameworks. These risks also arise from supervision and oversight mechanisms that might require adjustments to accommodate the new technology. New risks that arise due to the application of AI are related to the unintended implications of AI-based systems and models that ensure stability in markets and market integrity. Significant risks arise from the complexity of understanding how AI-based models produce result (explainability). The increased usage for AI in finance can create a risk of increased interconnectivity in the financial markets, and several operational risks linked to these methods could threaten the stability to the system of finance during times of crisis. Data Broker database for sale

The use of large data in AI-powered apps could be a major risk to the financial sector that is caused by the challenges and risks relating to the quality of information used, the privacy and security of information cybersecurity as well as fairness concerns. Based on how they are utilized, AI methods have the potential to prevent discrimination based on human interaction or increase biases, unfair treatment and discrimination within financial services. The discrimination and biases that are inherent in AI may result due to the use of poor quality, inadequate or flawed data used in ML models or accidentally through inferences and proxy data (for instance, determining gender based on buying activity data). Alongside the concerns of protecting consumers’ financial interests and competition concerns, there could be problems arising from the use of large information and the ML model which are related to the concentration of market players in certain markets or the risk of collusions with tacit. 

The most commonly acknowledged issue that is faced by ML models is in understanding how and why the model produces results. This is usually described using the term “explainability,” which is associated with several significant risk factors. The large-scale utilization of models that are opaque can cause unintended consequences when users of models and supervisors are unable to anticipate how the decisions made by ML models may negatively impact the market. The deliberate lack of transparency from companies in order to protect their own advantage contributes to the uncertainty of explaining and raises questions about the oversight of AI algorithms and models based on ML, as well as to the capacity of users to alter their strategies when they experience low performance or during situations of pressure.

Incompatibility of explanations is not only not compatible with the existing regulations and laws, but as well as internal governance, control and risk management frameworks that financial services providers use. It hinders the ability to make decisions.

Artificial Intelligence, Machine Learning and BIG DATA in Finance (c) The OECD 2021

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the ability of investors to comprehend the impact of their models on the market or cause disturbances, and could increase systemic risks associated with procyclicality. In addition, the inability of users to modify their strategies during periods of stress can cause increased market volatility and a recurrence of liquidity when there is a high degree of stress, which can trigger flash-crash type events. The problem of explainability is exacerbated by a widening lack of technical literacy and the inconsistency between the complexity typical to AI model and the requirements of human-scale reasoning, interpretation and reasoning that match human cognition. There are regulatory issues in relation to transparency and auditing for these models in various applications of financial services. 

Financial market professionals that use AI-powered models need to keep working to improve the ability of their models to explain in order to understand their behavior during normal market conditions as well as in times of stress and to manage the risks associated with it. There are varying opinions on the degree of explainability that is attained by AI-powered models dependent on the kind of AI employed. A balance has to be found between the accuracy of the model as well as its predictability. The implementation of disclosure requirements regarding using AI-powered systems and models can help ease the burden that arise from explaining as well as provide greater peace of mind and bolster trust for consumers who are using AI-powered services. 

Risks that could be posed to AI systems should be continuously monitored and evaluated in order to make sure that AI systems operate in a reliable and durable manner. The resilience of AI systems can be enhanced through careful training and retraining of ML models using datasets that are large enough to be able to detect the non-linear relationship and tail events within the data (including artificial models). Monitoring, testing, and validating AI model models over their lifetimes as well as based on their intended goals is essential to spot and correct “model drifts”2 (concept shifts, or drifts in data) that affect the accuracy of the model’s predictions. Model drifts can occur as tail effects, like the COVID-19 crisis cause discontinuities in the data and are very impossible to eliminate, since they aren’t replicated within the information used to create the model. Human judgement is vital at all times during AI implementation, from the input of data to the evaluation of the model’s outputs. It helps to avoid the danger of misinterpreting the meaningless correlations that are observed from activity patterns as causal relations. Automated control mechanisms or “kill switches’ may also be utilized as a last resort of defense that allows you to stop AI-based systems in the event they fail to work in accordance with their intended function but this is ineffective as it introduces operational risk and ensures insufficient resilience when the existing business system is required shutdown in the event that it is stressed. 

Data Broker Email database
Data Broker Email database

Explicated governance frameworks that define distinct lines of accountability for AI-based systems all the way through their lifespan, from creation to deployment, can enhance existing models’ governance structures. Model governance committees within the internal model or model review boards for financial service providers are charged with establishing models’ governance standards and procedures to build models, as well as documentation and validation at any stage of the model. They are expected to be more prevalent as more firms adopt AI by financial institutions and possibly a ‘upgrading’ in their roles, competences as well as some of the processes required to handle the complexity of the AI models (e.g. the frequency of validation of models).

Data Broker Email list

The need for clear accountability mechanisms is increasing important, especially because AI models are being used in highly-valued decision-making scenarios (e.g. the ability to access credit). There are also risks when outsourcing AI techniques to third-party companies with respect to accountability and the dynamics of competition (e.g. concentration risk, risk of dependency). The outsourcing to AI structures or models might cause risks related to the chance of convergence in market positions. This could cause herding behavior and the convergence of trading strategies. There is also the possibility that a large portion of the market could be affected simultaneously which may cause a period of inliquidity during periods of tension. 

The non-technological approach used by a variety of jurisdictions to regulate financial markets products is likely to become a challenge due to the growing complexity of some of the most innovative applications that make use of AI within finance.

Possible inconsistencies between current legal and regulatory frameworks could result from the use of modern

Artificial Intelligence, Machine Learning and BIG Data IN FINANCE (c) The OECD 2021

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AI techniques (e.g. due to the inability to explain or the re-adapting nature of the deep-learning models). Additionally, there could be a an increased risk of fragmentation in the regulatory landscape related towards AI at the international, national and sectoral levels.

The development of skills to build and manage new risk associated with AI is essential as AI applications become more commonplace in the finance industry. The use of AI in the financial sector could result in substantial job losses throughout the entire industry, leading to challenges in employment.

AI in finance needs to be considered a technology which enhances human abilities rather than replacing them. Combining ‘human’ machines’ in which AI aids human judgment instead of replacing the human judgment (decision-aid rather than decision maker) can allow the benefits of AI to be realized while ensuring accountability and control over the final decision-making process. A proper emphasis should be given to human supremacy in the process of making decisions, particularly when it comes to more valuable applications (e.g. lending decisions). 

Considerations on policy 

Regulators and policy makers play an obligation to ensure that the application in the use of AI within finance will be in line with the regulatory goals of maintaining financial stability, protecting the financial consumer, and promoting competitiveness and market integrity. The policy makers must think about the possibility of supporting AI innovations in the financial sector while protecting financial consumers as well as investors, and encouraging an orderly, fair and transparent markets. New risks arising from the implementation of AI methods must be identified as well as mitigated in order to encourage and support the adoption of ethical AI. The existing requirements for supervision and regulation might need to be clarified and sometimes modified, depending on the need, to resolve some of the perceived contradictions of current agreements with AI applications.

The implementation of supervisory and regulatory guidelines regarding AI methods could be viewed in an appropriate and contextual framework, based on the significance of the technology and its potential impact on outcomes for consumers and the market’s performance. This is likely to increase the application of AI without restraining the development. But, applying proportionality must not jeopardize the most fundamental prudential or stability safeguards, nor safeguarding investors or financial consumers, which are all crucial obligations of policy makers. 

Policymakers should think about increasing their attention on improved data governance in financial firms, with the aim to increase consumer protections across AI financial applications. Specific specifications and best practices in managing data using AI-based methods might be considered, focusing on the quality of data, the adequateness of the dataset utilized based on the application by an AI model, and security measures that guarantee the reliability of the model in relation to avoiding biases. A proper sense-checking of the model’s results against baseline data and other tests that consider the possibility of protected classes being determined from other attributes of this data is two instances of the best methods to reduce the risk of discrimination. The need for greater transparency regarding how personal information and opt-out choices regarding the use of personal data may be considered by the authorities. 

Policy makers must consider the need to disclose requirements regarding how they use AI methods in the supply of financial services and how they could affect the outcome for customers. Financial customers should be educated about the potential use of AI techniques for the production of a service, and also the possibility of involvement with an AI system rather than an individual human being so that they are able to make informed decisions about other products. Information that is clear about AI’s capabilities and limitations should be provided. AI technology’s strengths and weaknesses must be provided in this information. The introduction of requirements for suitability for financial services based on AI should be considered by regulators to assist firms in determining the degree to which potential customers have a clear understanding of how using AI influences the performance of the service. Data Broker email database providers

Regulators must consider ways to address the perception that AI is incompatible with lack of clarity in AI with the existing law and regulation. It may be necessary to revise and/or modify the current frameworks used for models of Governance and Risk Management for firms that provide financial services.

Artificial Intelligence, Machine Learning and BIG Data in Finance (c) The OECD 2021

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to tackle the issues that arise from the application of AI-based models to address these issues. The supervisory focus may be changed from documenting the process of development and the way in the model’s development in its prediction, to the model’s behavior and outcomes. In addition, supervisors might want to investigate more technical methods to manage risks, including stress testing for adversaries to the model or outcomes-based indicators (Gensler and Bailey 2020[2[2]).

Data Broker Email lists
Data Broker Email lists

Data Broker email listing

Policy makers should look into the necessity of clear model governance frameworks and the assignment of accountability in order to increase trust in AI-driven technology. Extensive governance frameworks that establish clearly defined lines of accountability in the design and supervision of AI-driven systems throughout their entire lifecycle, from creation to deployment, can be drafted by financial service providers to enhance existing procedures for operations that are that are related to AI. The internal model governance frameworks can be modified to better reflect risks that arise due to using AI as well as to reflect the desired results for consumers as well as an evaluation of whether and how these outcomes can be achieved using AI technology. A thorough documentation and audit trail of these processes can aid in the supervision of this activities by supervisors. 

The increased confidence offered from financial institutions about the reliability and strength of AI models is crucial as policymakers seek to protect against the accumulation of risks to the system, and can help AI financial applications build trust. The effectiveness of models has to be evaluated in the most extreme market conditions to avoid the risk of systemic vulnerabilities and risks that could arise during situations of stress. Automated controlling mechanisms (such such as the kill switch) that issue alarms or turn off models during times of stress can help in limiting risks, however they can expose the firm to risks that could be new to operations. Backup plans, models and processes must be implemented to ensure continuity of operations in the event that models fail or react with unexpected consequences. Additionally, regulators may look at adding buffers or minimum buffers for banks if they were to establish capital weights or risk using AI algorithmic models (Gensler and Bailey 2020[22). 

Frameworks that allow for proper training, retraining, and thorough tests of AI models can be developed or reinforced to ensure ML model-based decision-making functions exactly as it was intended and is in line with all applicable laws and regulations. The data used to train models must be sufficient to record non-linear relations and tail-related events in this data set, regardless of whether it is it is synthetic in order to improve the reliability of models during times of uncertainty and emergencies. Continuous testing of models based on ML is essential to find and correct models that drift.

Regulators must consider promoting regular monitoring and verification of AI models that are essential to their security, and are one of the most efficient methods to increase the resilience of models to prevent and correct models that drift. Standardised practices for monitoring and validation can help in increasing the strength of the model and determine if the model requires modification, redevelopment, or replacement. Validation of models, as well as the required approvals and sign-offs should be separate from the design of the model, and recorded as accurately as is possible for the purpose of supervision. The timeframe for tests and validations would have be determined according to the appropriate criteria, based on the level of complexity of the model as well as the significance of the decisions taken by this model.

A proper focus could be put on the primacy of human beings in decision-making in the case of high-value uses-cases like the decision to lend, which can directly affect consumers. Data Broker email database providers

Authorities should look into the implementation of processes that permit clients to challenge the outcomes that are generated by AI algorithms and demand redress. This would aid in building trust with these AI systems. The GDPR is one illustration of these policies that provide individuals with the right to’request human intervention’ as well as to voice their opinions in the event that they want to challenge the decision taken through an algorithm (EU 2016[3[3]).

The decision makers of the future should be aware of the increasing technological difficulty of AI and whether resources will have be put in place in order to keep up with advancements in technology. Because of the revolutionary impact of AI on specific financial market transactions as well as the new kinds of risks that arise from its use, AI has become a major concern for policymakers over the last few years. The funds should be allocated to research and development of skills as well as for financial sector players as well as for enforcement authorities.

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The function of policy makers is vital in encouraging technological innovation in the field and ensuring that financial customers and investors are adequately protected , and that markets surrounding these products and services are free, efficient and transparent. It is important for policy makers to think about enhancing their current arsenal of protections against the risks arising from, or increased by AI. AI. A clear and transparent discussion of the use of AI and the security measures put that are in place to safeguard it and the users will aid in establishing trust and confidence and encourage the use of these innovative methods. With the ease of trans-border financial services, an inter-disciplinary dialogue between policy makers and business community could be encouraged and maintained at both national as well as international levels.
wheels are a broad concept which covers all automated solutions that are mostly a trader-driven flow. A computer-driven algo wheel is an automatized process for routing using AI methods to assign an algorithm for brokers to orders based on an algorithmic list that is pre-configured options (Barclays Investment Bank 2020[27[27.). In other words, AI-based wheel models determine the most efficient strategy and broker email database providersthrough which to route the order, depending on market conditions and trading objectives/requirements. 

Algo wheels for two reasons: first, to gain efficiency gains through improved execution performance; and second, to improve workflow efficiency by the automation of small order flows or standardizing broker algorithms to standard name conventions. Market participants claim that using algo wheels can reduce market biases of traders regarding the choice of the broker and the broker’s algorithm used on the market.

Algo
The estimate is that around 20% of all trading transactions are currently going through algorithm wheels. The system is gaining more acceptance as a method of sorting and measuring the most efficient broker algorithms (Mollemans 2020[28[28.]). But, those who utilize it, do so to make up 38% of the flow. The potential for widespread adoption of algo wheels may cause growth in the general quantity of electronic trading which could be beneficial to the marketplace of online brokerage (Weber 2019, 2019[29[29, 30]).

Data Broker Email leads
Data Broker Email leads

Data Broker email leads

The application in the use of AI for trading been through several levels of development as well as corresponding level of complexity, adding an additional layer of traditional algorithmic trading in each stage of the process. The first generation algorithms were comprised of buy or sell orders based on basic parameters, then and then algorithms that allowed to dynamically price. Second-generation algorithms employed strategies to break huge orders and limit market impacts, which helped to get higher prices (so-called “execution algorithms”). Strategies based upon deep neural networks have been developed to offer the most efficient execution and order placement that minimizes market impact (JPMorgan 2019[30[30]). Deep neural networks are modeled after the human brain using an array of algorithms that are designed to recognize patterns and they are not dependent on human input to perform and learn (IBM 2020[31[31]). Utilizing these techniques can benefit market makers to improve the management of their inventory , and lower the costs of their balance sheets. As the technology of AI is advanced, AI algorithms evolve into computer-controlled, automated algorithms that can learn from the input of data and are less dependent on human interaction. buy Data Broker database online

In actual practice, the most advanced versions of AI nowadays are mainly utilized to recognize signals that come from ‘low-informational value’ events in flow-based trading11, which are made up of events that are less apparent that are difficult to detect and deduce value from. In addition to assisting with the speed of execution, AI is employed to separate signals from the noise of data and transforms that information into decisions about trading. The less advanced algorithms are typically used in ‘high-informational events’. These are made up of financial news that are easier to all parties to comprehend and for which execution speed is essential.

At the moment of their development, models based on ML do not intend to the frontrunners in trading and are not able to benefit from the speed of action like HFT strategies. Instead, they’re restricted to use offline in the context of calibration of algorithm parameters or to improve algorithms’ decision-making logic instead of for execution purposes (BIS Markets Committee 2020[32[32]). In the near future, however as AI technology improves and becomes utilized in more scenarios that could enhance what is possible with traditional algorithms for trading, which will have implications for the financial markets. It is likely to happen in the near future, when AI techniques are deployed in the execution phase of trades. This will provide greater capability for automated execution of trades , and supporting the entire chain of actions starting with the detection of signals, to generating strategies, and then carrying them out. Algorithms based on ML will permit the automatic and dynamic modification of their own algorithms when trading. In this scenario the existing requirements to algorithms in trading (e.g. 

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protections in the systems for risk management prior to trading (e.g. automated control mechanisms to turn off the algorithm if it is over the limit built into the risk management model) could be extended to algorithmic trading using AI.

2.2.1. Unintended consequences and dangers Data Broker Email list

The adoption of similar or identical models by a lot of traders can cause unintended negative effects for the market, and also result in the increase of market stress. The introduction of models that are widely used will naturally decrease the opportunities for arbitrage offered, which could reduce margins. It will ultimately benefit consumers as it reduces bid-ask spreads. However it could cause convergence, herding and single-way markets with potential implications to the stability of market as well as for liquidity conditions , particularly in times of extreme stress. Like any algorithm the widespread usage that is similar to AI algorithms can lead to the possibility that feedback loops self-reinforcing themselves which could, in turn, result in sharp price swings (BIS Markets Committee 2020[32[32.]).

The access to big data and advanced AI-based analytics models that use these data sets have revolutionized the method of assessing credit risk. Credit scoring models driven by AI incorporate the application of traditional credit data, when available, with data that is that is not directly related in relation to creditworthiness (e.g. the digital footprints of social media, data from online platforms and transactional data that is accessible via Open Banking initiatives). 

The application of AI models for credit scoring could reduce costs of underwriting and also allow for the evaluation of creditworthiness for clients who have a limited credits (‘thin file’). This can result in the credit extension to companies that are viable but cannot demonstrate their viability using past performance information and tangible collateral asset possibly increasing the access to credit and helping the growth of the economy by easing constraints on SME financing. Recent empirical research could lower the requirement for collateral, by reducing the information gaps that exist in the credit market (BIS 2020[42]). The approval rates of credit for a portion of the population who have been historically disadvantaged like the near-prime or those who are underbanked in the population, might be more efficiently served by alternative scoring techniques, possibly increasing financial inclusion. Despite this model, AI-driven credit scoring models are not tested for extended credit cycles or in the event of a recession and there is a lack of evidence-based proof of the benefits of using ML-based methods to promote financial inclusion. For instance, certain studies suggest that using ML models for assessing credit risk will result in less access to credit for majorities of ethnic groups (Fuster and co.. 2017[43]) However, other studies show that lending decision rules built on ML predictions decrease racial biases in the marketplace for consumer loans (Dobbie et al. (2018) [4444]).

2.3.1. Credit scoring based on AI/ML, transparent, and fairness in lending buy Data Broker database online

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Despite their vast potential for speed, efficiency and risk scoring of the ‘unscored’, AI/ML-based models raise risks of disparate impact in credit outcomes and the potential for discriminatory or unfair lending (US Treasury, 2016[45]).13 Similar to other applications of AI in finance, such models also raise important challenges related to the quality of data used and the lack of transparency/explainability around the model.

The best intentions of ML models could inadvertently result in biased conclusions or discriminate against certain groups or groups of persons (e.g. due to gender, race, ethnicity or religion) (White and Case 2017[46[46]). If AI/ML is not properly designed and controlled, models are at chance of enhancing or reforcing existing biases and creating discrimination in credit allocation more difficult to identify (Brookings 2020[47[47]).

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Similar to any model that is used in the financial sector there is a risk of ‘garbage into, garbage out’ is present in AI/ML-based models used for risk scoring and beyond. Poorly labelled data could include labeled or incorrect data, or data that reflect human biases or insufficient data (S&P 2019[48[48]). The neutral ML model that has been developed using insufficient data is at risk of producing incorrect results, regardless of whether it is fed “good data. A neural network that is trained using high-quality data and then fed insufficient data, can produce poor output, regardless of the fact that it is a well-trained algorithm. This, in conjunction with the inability to explain in ML models can make it more difficult to determine the incorrect use of data, or unsuitable data for AI-based applications.

As such, the use of poor quality or inadequate/unsuitable data may result in wrong or biased decision-making. The use of biased or discriminatory scoring might not be deliberate from the point of view of the company employing the model. Instead algorithms could blend facially neutral data points and consider them to be proxy for immutable traits like gender or race which could be used to circumvent existing discrimination laws (Hurley 2017[49[49]). For instance, although an officer for credit may be cautious about including gender-specific variations in the system, the algorithm could discern gender of a transaction based on its activities, and then use that information to assess creditworthiness, thus circumventing laws. The possibility of bias is also present in the data that is used as variables and, as the model is trained on information from outside sources that have already included certain biases, it perpetuates the previous biases.

Like other applications in the field of AI within finance ML-based models also raise questions of transparency due to their inability to explain, i.e., the difficultness of understanding and replicating the process of making decisions (see section 3.4). Questions of explainability are especially relevant in lending decisions, for example. buy Data Broker database online

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Lenders have to be accountable for their actions and have to be able to provide the reason for the denial of credit extensions. In addition, customers have a limited capacity to spot and contest unjustified credit decisions, and they have little possibility of understanding what steps they need to do for improving their credit score.

The regulations in the developed countries make sure that particular data points aren’t considered in credit risk assessment (e.g. US regulation on zip code or race information, UK regulation around protected category data). Regulation that promotes anti-discrimination laws like those in the US regulations on fair lending is in use in numerous jurisdictions and regulators around the world are looking at the potential for discrimination and bias risks that AI/ML algorithms and algorithms may create (White and Case 2017[46[46]).

In certain jurisdictions, evidence of unfair treatment for instance, lower credit limits on average for those belonging to protected groups than for those from other categories, are classified as discrimination, regardless of whether the intention was to discriminate. The most effective ways to mitigate these risks include that there are auditing mechanisms that examine the findings that the models produce against the baseline data and the testing of these scoring systems to verify their accuracy and fairness (Citron and Pasquale 2014[5050]) and the governance frameworks that AI-enabled products and services , and the assignment of the responsibility to the human component of the project, just to give a few examples. Data Broker email id list

2.3.2. BigTech Financial Services and BigTech

As BigTech increasingly leverage their free access to vast amounts of customer data that feed into AI-driven models to provide financial services, their deployment of AI raises issues around data privacy and concerns over ways in which the collection, storage and use of personal data may be exploited for commercial gain (DAF/CMF(2019)29/REV1). This could be detrimental to customers, including by discriminatory practices relating to pricing and availability of credit.

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Access to customer data through BigTech gives them a distinct advantage over traditional financial services companies. This advantage could be further enhanced by the application of AI that can provide opportunities to create new, personalized and efficient services offered by these companies. The dominance of BigTech in certain areas of the market could lead to excessive market concentration (see also Section 3.2) and increase the dependence of the market to few large BigTech players, with possible systemic implications depending on their scale and scope (DAF/CMF(2019)29/REV1), (FSB, 2020[51]). This could cause concerns about the risks that financial consumers face who may not have access to the same variety of products, options, prices or information that they would receive offered by traditional financial service providers. This could also cause difficulties for supervisors getting access to and auditing financial services offered by these companies. Data Broker email id list

Another risk is been related to anti-competitive practices and market concentration on the technology element of the service. There is a possibility of the emergence of a few important players in market segments for AI solutions or services that incorporate AI techniques (e.g. cloud computing services that also offer AI services) and evidence of this can be seen in certain regions around the globe (ACPR 2018[3333). Competition issues are also evident due to the advantage BigTech players enjoy with regard to data of customers. Particularly, they are able to leverage their data advantage to establish monopoly positions with respect to customer acquisition (for instance, through efficient price discrimination) as well as through the creation of barrier to entry that could be imposed on lesser players.

In 2020, at the end of the year at the end of 2020, at the end of 2020, European Union and the UK released regulatory proposals, known as called the Digital Markets Act, that are aimed at establishing an ex-ante framework that will regulate digital platforms that are ‘gatekeepers’ like BigTech which aims to reduce certain of the risks mentioned above and to ensure fair and transparent market access to digital technology (European Commission 2020[52[52]). The proposed obligations include the requirement that Gatekeepers to allow business users access to data generated through their activities, and to provide data portability, and prohibit them from using information via business user accounts to compete business users (to mitigate dual-role risk). The proposal also includes solutions for self-referencing, parity and other issues.

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and criteria for ranking to ensure that there is no favoritism to the services provided by Gatekeeper Gatekeeper its own services against those offered by third party providers.

2.4. Integration of AI in financial products based on Blockchain

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The applications of distributed ledger technology (DLT) which include blockchains, has exploded over the last few years in all sectors and most notably in finance. This rapid expansion of blockchain software is driven by the alleged advantages of efficiency, speed and security that these new technologies could provide, aided via automation, disintermediation and automation (OECD 2020[5353). A wide-spread use of DLTs within finance could be triggered by efforts to improve efficiency through disintermediation, such as in financial markets for securities (issuance and post-trade/clearing and settlement) as well as in payments (central bank digital currencies as well as fiat-backed stablecoins) as well as tokenization of assets in general and could lead to the transformation of functions and models of business for financial institutions (e.g. custodians). 

An emergence of AI in conjunction with DLTs in blockchain-based financial systems is seen by market as a means to achieve more efficient results from these systems, since the increased automation can boost the efficiency offered by blockchain-based systems. However, the extent of AI implementation in blockchain-based projects does seem to be sufficient currently to warrant assertions of convergence among both technologies. 

There is no convergence, what’s being observed in the real world is the introduction of AI applications within certain blockchain systems, with specific uses (e.g. to manage risk, read below) as well as the use of DLT solutions within certain AI methods (e.g. for data management). The latter requires making use of DLTs for feeding data to an ML model using the immutable and non-intermediated nature of the blockchain, and permitting the sharing of confidential data in a zero-knowledge manner without compromising privacy or confidentiality obligations. Utilizing DLTs as part of AI mechanisms is anticipated to permit users of these systems to monetize the data that they own, and which is utilized by ML models as well as different AI driven systems (e.g. IoT). The implementation of such AI applications is motivated by the technology’s potential to further enhance efficiency gains of automation and disintermediation within DLT-based networks and systems.
The most significant impact of AI in DLT-based finance could be to enhance the automation capability that smart contracts provide. A variety of applications of AI are evident as specific uses-cases that are utilized in DLT-based networks for example managing risk compliance (e.g. anti-fraud, the introduction of automated limitations to networks) and management of data and inference (e.g. improving the functionality for Oracles14). The majority of these applications are in the process of developing. Data Broker Email list

Particularly, AI can be used in blockchain networks to decrease (but not completely eliminate) security risks and to protect against the possibility of a breach of the network, as when it comes to payment applications. Making use of the potential of AI will allow those using blockchains to detect suspicious activity that could be linked to fraud or theft, as they occur despite the necessity for both public and private keys in order to compromise the security of the user. Additionally, AI applications can improve the on-boarding process for the network (e.g. biometrics to aid in AI identification) and AML/CFT checks for the supply of any digitally-enabled financial service. The incorporation AI AI within DLT systems may aid in compliance processes and risk management in these networks. For instance, AI applications can provide the results of a wallet address analysis which can be utilized for compliance purposes with regulatory requirements or to conduct an internal risk evaluation of the transaction partners (Ziqi Chen et al. 2020[54[54]). But, if financial intermediaries are removed from transactions with financial institutions therefore, the effectiveness of current approaches to financial regulation that focus on entities that are regulated could be diminished (Endo, 2019)15. 

The incorporation of AI-based solutions in DLT-based systems at protocol level may assist authorities in meeting their regulatory goals efficiently. This can be accomplished via the automatic sharing of the regulated entity’s information with officials in an effortless, real time fashion, such as

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and also through and through the programming of regulations and regulations into the code of programs, encouraging compliance on a regular basis. As well as through the participation by regulators in the form of nodes on decentralised networks is being thought of with the markets as one way to overcome the difficulties of regulating these platforms that do not have an centralized authority.

In the case of data, theoretically speaking, AI could be used in DLT-based systems in order to enhance the quality of data feeds to the chain, since the task of curation shifts from nodes belonging to third parties to autonomous automated AI-powered systems. increasing the security of data recording and sharing since these systems are less difficult to alter. Particularly using AI could enhance the efficiency of off-chain nodes from third parties that are known as ‘Oracles Nodes that feed external data to the network. Utilizing Oracles within DLT-related networks is a risk. the risk of incorrect or insufficient data feeds to the network due to insufficient or even malicious off-chain third-party nodes (OECD 2020[53[53]). In theory, the application of AI could further boost disintermediation, by creating AI inference directly onto the chain, that would make third party suppliers of data to the chain, like Oracles and others, redundant. In reality, it can serve as a protection by determining the veracity and authenticity of the data supplied by the Oracles and preventing cyber-attacks or manipulating the data of third-party transfer into the network.

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The implementation of AI applications may theoretically improve the trust of participants in the network since they can check the information supplied to them by Oracle and look for any security issues. However the introduction of AI will not always solve the garbage in garbage out’ issue because the issue of insufficient or poor quality the data sources is an ongoing problem which is also seen in AI-based applications and mechanisms (see the section 3.1).

2.4.1. AI improving abilities of smart contracts

The biggest impact of the incorporation of AI techniques into blockchain-based systems can be seen in their use in smart contracts. It could have implications for the risk management and governance of such contracts as well as many possible (and yet to be tested) implications on functions and processes that DLT-based systems. In the theory of things, using AI will enable self-regulated DLT chains that are functioning on a completely independent basis.

Smart contracts were in existence since before the invention of AI applications and depend on simple software. Even today, many smart contracts that are used in a tangible way are not tied with AI techniques. Therefore, a lot of the claims for benefits derived due to the application in AI within DLT systems are still speculative, and the claims of industry about the convergence between AI and DLTs functions in the marketing of products must be taken with a pinch of salt.

However, AI use cases are highly beneficial to enhance the capabilities of smart contracts, specifically in the area of managing risk and the detection of weaknesses in the software used to create the contract. AI tools like NLP can be utilized to analyze the patterns of smart contract’s execution to detect fraud, and increase its security. Additionally, AI can perform testing of the code in a manner that human code reviewers are unable to do, both in terms speed and detail level/scenario analysis. Since the code is the basis for the automated creation of smart contracts, flawless code is the key to the security of these contracts. 

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Box 2.4. Intelligent contracts for DLT systems

Smart contracts can be described as distributed software that are created and run on the blockchain. They comprise of self-executing contracts that are written as blockchain ledgers. The code is that are executed automatically upon achieving certain trigger events specified within the program (OECD 2019, 2019[5555).

Smart contracts are programs that operate within Ethereum. Ethereum blockchain. The code that these programs run determines how they operate and how they will operate. They create rules, just like regular contracts, and then automatically implement them through the code when the conditions defined within the program are fulfilled.

Smart contracts aren’t managed by a user, they are distributed to the network and operate according to the program. Accounts of users can interact with the smart contract through making transactions that fulfill an action specified on the contract.

Smart contracts allow for the elimination of intermediaries that DLT-based networks could be benefited from, and are the main sources of efficiency that these networks are able to provide. They enable the complete automatization of certain actions, such as the transfer of funds or payments when certain conditions are met that are pre-defined and recorded in the code without intervention from a human. The legality of smart contracts is yet to be clarified, since they aren’t legally binding contracts in the majority of states (OECD 2020[5353). As long as it’s not clarified that the law of contract applies to smart contracts enforceability and financial security problems will remain. The auditability of smart contracts requires the involvement of the market players who would like to verify the foundation of how these smart contracts are formulated. 

In theory, the use technology like AI within smart contract may improve the automation capability of smart contracts, improving their autonomy, while also allowing the code to be dynamically adjusted in response to the market or environmental conditions. The application of NLP which is a subset of AI can enhance the ability to analyze smart contracts that are tied to conventional contracts, laws or court judgments and go deeper in analyzing the intentions of the parties in the contract (The Technolawgist, 2020[56]). It is important to note however that such application to AI in smart contracts is just a matter of theory at the moment and need to be evaluated in real-world instances.

Risks to operations as along with compatibility and interoperability of traditional infrastructure with DLT-based AI technologies are still being studied. AI techniques like deep learning require large amounts of computational power and could be an obstacle for their successful implementation using the Blockchain (Hackernoon 2020[57]). It is believed that, at this point in the development of the infrastructure the storage of data off chain would be a better choice for real-time recommendation engines in order to avoid delays and lower cost (Almasoud and co. 2020[58[58]). The operational risks that come with DLTs are still to be addressed in the future as technology, and applications made possible by it improve. Data Broker Email list

2.4.2. Self-learning smart contracts, as well as the governance for DLTs: autonomous chains as well as decentralized Finance (DeFi)

In the theory of AI-powered smart contracts, they could serve as the basis to create self-regulated chains. Researchers have suggested that in the near future, AI could also be utilized to predict and automate in self-learned smart contracts, which are similar to models using the reinforcement-learning AI techniques (Almasoud and al. 2020[58[58]). Also, AI could be used to process and extract information from real-time systems and incorporate this data in smart contracts. This means that code for smart contracts could be created.

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The chain could be automatically adjusted and the administration of the chain will not require any intervention from a human which would result in completely autonomous, self-regulating decentralised chains that can be adjusted automatically.

Decentralised autonomous organizations (DAOs) are organizations which are autonomous codes in the chain. They’ve existed before, but may be further enhanced through AI-based methods. For instance, AI could provide real-time data feeds that could be used as inputs for software, and will calculate the desired decision to make (Buterin 2013, [59]). AI-powered self-learning smart contract will play an essential function in the introduction of new functions to the chain’s logic and learn from the experiences of the chain and adapt or implement new rules, thus creating the overall governance for the chain. The current DeFi projects are usually controlled by DAOs. DAOs’ have a variety of centralized aspects, such as voting on-chain by the holders of governance tokens and off-chain consensus. In addition, such human involvement could become an avenue for regulators to control (Ushida and Angel 2021[60[60]). However, the incorporation of AI in DAOs may facilitate further decentralization and decrease the effectiveness of regulatory methods.

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The application of AI to create fully autonomous chains brings significant concerns and risks for its users as well as the ecosystem. In these situations, AI smart contracts, rather than human beings, will make decisions and run the system without human intervention in the process of making decisions or operating of the system. There are significant ethical concerns related to this. Additionally, the introduction of automated mechanisms to turn off the model immediately (so-called “kill switches”) (see the section 2.2 to learn more about definitions) is extremely difficult in these networks, in part due to the distributed characteristics of the system. This is among the most significant issues being faced in the DeFi domain.

AI integration into blockchains can help decentralised applications in DeFi by using cases to increase efficiency and automation of the delivery of specific financial services. Indicatively, the introduction of AI models could support the provision of personalised/customised recommendations across products and services; credit scoring based on users’ online data; investment advisory services and trading based on financial data; as well as other reinforcement learning16 applications on blockchain-based processes (Ziqi Chen et al., 2020[54]). Similar to other financial applications based on blockchain the implementation to use AI in DeFi could enhance its capabilities in the DLT use-case , by providing additional functions, but it will not significantly impact or alter the businesses used within DeFi applications.

Box 2.5. AI to help with ESG investing

ESG ratings differ greatly between ESG rating agencies due to the different frameworks, measuresand crucial indicators and metrics using data, qualitative judgment and how subcategories are weighted (Boffo and Patalano 2020[61(Boffo and Patalano, 2020[61]). Despite the proliferation of ESG ratings however, market participants are often not equipped with the tools they require (e.g. reliable data, comparable metrics and transparent processes) to guide their decisions in a timely manner (OECD 2020[62]). The importance of data is crucially important given the importance of non-financial aspects of corporate action, and that have to do with

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sustainability however, ESG data quality is a concern due to inconsistencies regarding data accessibility, possible mistakes and the lack of comparability across different sources. 

AI or big data can be utilized for ESG investment to (i) examine the quality of data from companies (issuer records); (ii) assess non-company data, and (iii) evaluate the reliability and consistency of ratings in order to comprehend the underlying factors behind scores. The claimed benefit of AI is the fact that it could help make better-informed decisions by limiting cognitive bias and subjectivity that can result in traditional research, while reducing the amount of noise that is present in ESG data, and using unstructured data. Particularly, NPL can be used to analyze huge amounts of unstructured data (geolocalisation and social media,) for sentiment analysis and to identify patterns and connections in these data. The results of such analyses can be used to assign value to the qualitative information in order to calculate sustainability parameters, using AI methods (Bala and al. 2014[63]).

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Alternative ESG rating providers have emerged, providing ratings that are based on AI with the intention to offer an impartial and outside-in view of company’s sustainable results (Hughes, Urban and Wojcik 2021[64]). The application of AI to generate ESG ratings can reduce the risk of corporate greenwashing, where companies use business-as-usual strategies disguised as sustainable in the sense that they reveal inaccessible information about the practices of companies and actions that are related to sustainability. 

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