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Finance Managers Email List

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Relevance and relevancy of the data FM email id list

A key ‘V’ in big data as defined by the industry is veracity, i.e. the degree of uncertainty of authenticity of the data (IBM 2020[11[11, 12]). This uncertainty could result from the uncertainty of source reliability or insufficient quality or the insufficient quality of data utilized. In the case of big data, the veracity of the data could be affected by certain behaviors (e.g. social networks) and unreliable or biased systems to collect data (e.g. sensors IoT) could not be enough to limit the effects of disparate factors.

Relevance and representativeness of data offer more specific attributes to the data used in AI applications, compared to veracity of data. The first is about whether the data utilized provide a complete representation of the population being studied as well as a the correct representation of all subpopulations relevant. In financial markets, this could prevent over/under-representation of groups of operators, and enhance more accurate model training. In the area of credit scoring, it may help to promoting financial inclusion of minorities. Data relevance is the inclusion of the data used to explain the issue at hand, but without

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including exogenous (misleading) information. For instance, when it comes to credit scoring, the relevance of information pertaining to the behavior of natural persons or reputation (for legal entities) must be assessed carefully prior to its inclusion and use of the algorithm. The evaluation of the data utilized on a case-by- individual basis to increase accuracy and the quality of the information used could be time-consuming because of the huge amount of data required, and it could reduce the efficiency that are generated by the use of AI. FM email Profile

3.1.2. Privacy and confidentiality of data

The sheer volume, ubiquitousness and the continuous flow of the data used in AI systems could raise a variety of privacy and data protection issues. Apart from the standard issues with the use and collection of personal information, there are potential problems arise when it comes to the use of AI in particular, due to the capacity of AI to draw inferences from large datasets; the questionable viability to implement practices of “notification and consent” that provide privacy protection in models using ML and also concerns about data connectivity and international flow of data. The latter is a consequence of the importance connection to data in the financial sector, and the vital importance of the capability to collect, store and process data across borders to support the development of the financial sector, while ensuring the proper data governance safeguards and guidelines (Hardoon 2020[65[65]).

The merging of several datasets could provide new users of big data with opportunities to combine data, but it also gives an opportunity to tackle analytical issues. Data that is collected under diverse conditions (i.e. different regimes, populations, or sampling techniques) offer new possibilities for analysis that are not accomplished by relying on single data sources. In the same way this combination of different environments (or even regimes) can lead to new analytical challenges and pitfalls, such as confounding sampling selection, sizing or the possibility of cross-population biases (Bareinboim and Pearla 2016[66[66]). FM email Profile

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Risks to cyber security, the possibility of hacking, as well as other operational risks that are seen all over the world of financial services and products directly impact the privacy and security of data. Although the use of AI doesn’t open the potential for new cyber attacks however, it can exacerbate existing ones, by for example linking false data and cyber-attacks, resulting in new threats that could alter the operation of the algorithm via the introduction of fake data into models or modification of existing models (ACPR 2018[33[33, 34]).

Financial and non-financial information is increasing being used and shared with little or no understanding and their informed consent (US Treasury 2018[8[8.]). Although informed consent is legally required for any data use but consumers aren’t necessarily informed about how their personal data is used and how it is used and their consent may not be educated. The increased tracking of online activities using advanced methods of tracking can increase these risks and also the sharing of data by third-party companies. The data that is not shared by the user such as geolocation information or data about transactions made with credit cards are some of the most common examples of data susceptible to possible violations of privacy policies and lawful protection of data. FM email id list

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Innovative approaches are being proposed by the industry to protect non-disclosive computing, which helps protect the privacy of consumers, such as using the creation and usage of customized synthetic datasets, which are created to aid in ML modeling or using Privacy Enhancing Technologies (PETs). PETs aim to keep the general properties and attributes of the original data , without divulging information about particular data samples. PETs are characterized by the use of differential privacy, federated analysis homomorphic encryption, and safe multi-party computing. The differential privacy feature offers mathematical guarantees regarding the level of privacy desired and provides better accuracy when compared with synthetic data. The alleged benefit of these methods is that models trained using synthetic data rather than real data do not exhibit an enormous loss in performance. Concerning the security of data in models, methods for data anonymization don’t provide a thorough security measures, particularly when considering the inferences drawn by AI-based models. FM email id list

The application of large amounts of data produced by AI-powered models can extend the range of data that is sensitive, since such models could become highly adept in identifying individual users (US Treasury 2018[8[8]). The technology of facial recognition as well as other data inferred from it like customer profiles could be utilized in the models to FM email Profile

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determine the identity of users or other traits, like gender, when combined with other data. AI models can achieve recognising anonymised databases linking publicly accessible databases and reducing matches, thereby attribution of sensitive data to specific individuals ( 2020[67[67]). Furthermore the higher dimensionality in ML datasets, i.e. the capability to consider the infinite number of variables as opposed to traditional statistical methods, boosts the chance of sensitive data being considered when analyzing.

Regulators are refocusing their attention on the protection and privacy of data in the wake of increased digitalization of our economy (e.g. EU GDPR) and have set out to improve protection of consumers across markets and rebalance the power relations between individuals and corporations and shift control back to consumers, and ultimately improve transparency and trust in the way businesses use data from consumers. Protection of consumer data and Privacy is among the Principles of the G20/OECD High-Level Principles on Financial Consumer Protection (OECD, 2011[68[68]). The protection of personal information when using for the development of AI within finance an integral part of the Monetary Authority for Singapore’s guidelines to promote fairness, integrity accountability, and transparency (MAS 2019[69[69]). FM email leads

From a business perspective from a business perspective, one of the biggest barriers to improving data management for firms in the financial sector is the perceived dispersion of supervisory and regulatory oversight of data, as well as to whom institutions are responsible for the implementation of the best practices in data governance in fields such as data quality standards, definitions, architecture and deduplications among others. This fragmentation is amplified in cases of cross-border transactions.

The economics of using data are changing as the speed of deployment of models based on ML in finance. A handful of alternative data providers have emerged, leveraging the growing demand for data sets that aid in AI methods, but with only very little visibility and oversight of their operations at this point. The acquisition and use of these datasets by small-scale niche database companies may pose risks for their legal purchase as well as use by banks and financial services providers. A rise in compliance costs associated with regulations that aim to safeguard consumers could change the economics behind making use of large data by financial market service providers and, consequently how they approach their use of AI or big data.

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3.2. Data concentration and the threat of competition in AI-powered financial services and products

The power and the nature of competitive advantage generated by advancements in AI may impact the efficiency and effectiveness of market structures when consumers’ capacity to make informed choices is limited by significant concentrations among market participants (US Treasury 2018, 2018[88). In the event that use of AI and proprietary models provide the advantage over competition which could lead in a lower participation rate for smaller financial services companies that might not have the capacity and resources for them to implement internal AI/ML strategies or utilize large data data sources. Inequal access to data and the possible dominance of the production of data from big sources by only a handful of large BigTech particularly, may hinder smaller players to compete on the market for AI-related products and services. FM email leads

The possibility of network effects is a further factor that increases the dangers of dependence and concentration on a few major players, which can lead to the development of new players that are important to the system. BigTech is the best example of this potential danger and the fact that they are beyond the regulatory boundaries creates additional challenges. This is caused by the use and access to data through BigTech and amplifies with making use of AI methods for monetizing these data. A growing number of other data providers have a hand in the development of data storage, and there is some possibility of concentration within that market. buy FM email database

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When it comes to data-driven barriers to entry into barriers to entry into the AI marketplace, small businesses might be liable for disproportionate costs in the implementation of these technologiesdue to the need for high-priced additional assets like advanced data-mining software and ML and physical infrastructures, like data centers, whose investment is dependent on the effects of economies-of-scale. The capacity of algorithms to discover new patterns and relationships in behaviour require access to wide range of data gathered by multiple different sources which results in economies of scale. Smaller companies who lack the assets necessary to complement their business or are not operating in multiple markets could encounter barriers to entry that prevent their development of algorithms that are able to effectively  buy FM database online apply the pressure of competition (OECD 2016a).

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Competitiveness within the market for AI-based financial services is essential for companies to to fully benefit of AI, particularly in the areas of investing and trading. Utilizing outsourced or third party vendor models may negate the advantages of these tools for businesses that are adopting them, and cause one-way markets as well as consumer behavior that is influenced by herding or the convergence of investment strategies and trading by finance professionals. FM email leads

3.2.1. The risk of collusions with tacit

The wide-spread use of AI-based algorithms could cause competition problems by making tacit collusion17 more attainable without formal agreement or interaction with humans (OECD 2017[35[35]). In a context of tacit collusion it is achieved by each party choosing its own strategy to maximize profits independent of the other players (OECD, 2017[35]).18 In other terms, the use algorithms make it much easier for market players to maintain profit levels that exceed the competitive level without having signed an agreement that replaces explicit collusion with tacit coordination.

Although tacit collusion usually occurs in markets that are transparent and have very few market participants There is evidence to suggest that collusion could become easier to endure and more likely to be detected when algorithms are employed in markets that are digital and characterized by large transparency and frequent interaction (OECD 2017[35[35]).

The capacity to be dynamically adaptive of deep and self-learning AI models could pose the possibility that the model recognizes interdependencies between them and adjusts to the actions and behaviours of market participants or similar AI models, potentially leading to the result of collusion without human intervention, and possibly without even knowing it (OECD 2017, 2017[3535). Although collusions of this kind aren’t necessarily unlawful from a competition legal perspective, concerns are asked about whether and how enforcement actions could apply to the model and its users in the cases where this is the case. FM email listing

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3.3. Riss of bias and discrimination

Based on the way they are utilized, AI methods have the possibility of preventing discrimination based on human interaction or increase biases, discrimination and unfair treatment in the field of financial services. When they delegate the human-driven aspect of decision-making to an algorithm, the person using the AI-powered model can avoid the human-driven biases. In the same way the use of AI applications could expose them to discrimination or bias due to the possibility of enhancing existing biases in the data through the training of models using such biased data or the identification of false correlations (US Treasury 2018, 2018[88]).

Incorrect or insufficient data could lead to biased or inaccurate decisions in AI systems. Insufficient quality data could cause discriminatory or biased decision-making via two different avenues. ML models that are trained using insufficient data could produce flawed results even when they are provided with quality data. Also, ML models that are built on top-quality data are likely to produce unsatisfactory output if fed data that is not suitable regardless of the well-trained algorithm. Thoughtfully designed ML models can inadvertently result in biased conclusionsthat discriminate against certain classes of people (White and Case 2017[46[46]). Incorrect or inaccurate (e.g. inadequately labeled, insufficient) or even fake information in ML models is a risk of garbage in garbage out’ as well as the accuracy of data is the accuracy of the output. FM email listing

The biases could also be present in the variables used in the model and, since the model is trained on data from outside sources that might already have incorporated some biases, it can perpetuate old biases. In

In addition, discriminatory or biased decisions made by models using ML can be a result of a lack of intention and could occur even when using high-quality, well-labelled data via inference and proxy methods, or due to the fact that the correlations between sensitive and non-sensitive variables can be difficult to discern in huge database (Goodman and Flaxman 2016, [70In addition, biased or discriminatory decisions made by ML models can be difficult to detect (). Because big data contains huge volumes of data that reflect the society, AI-driven models may simply perpetuate biases that already exist in the society and are evident in these databases. buy FM email database

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The labelling and structuring of data is an essential and tedious task required for ML models to carry out. AI can only discern the signals from the noise when it can effectively identify and recognize the characteristics of a signal and the models need to be labeled with clear data in order recognize patterns within them (S&P 2019[48[48]). To achieve that models that are supervised learning (the most commonly used kind of AI) require the feeding of software stacks with pre-tagged data that are classified in a consistent way until the AI is able to recognize the category of data on its own. buy FM database online

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However, the right labels for data may be difficult to discern from a simple set of data points. The process of labelling and identifying data can be a labor-intensive process that requires the analysis of huge quantities of data. The process is currently believed to be contracted out to specialist companies or scattered employees (The Economist, 2019, [71(71). Data analysis and labelling by humans offer the chance to find errors and biases in the data being used but, as per some, they could accidentally introduce additional biases due to the subjective nature of decisions.

In the course of data processing, cleansing and labelling can be susceptible to human error. Fortunately, a variety of solutions that involve AI themselves are beginning to be developed. Questions about the accuracy of data as well as the level of its representation will help to avoid biases that are not intended in the final output. FM email listing

Furthermore, due to the high degree of dimensionality of data, those using ML models should be able to discern the characteristics from the dataset that can be relevant for the scenario that are being evaluated through the model. There are various methods being developed to minimize the amount of irrelevant characteristics or noise in the data and to improve the performance of ML models. A promising alternative is to use synthetic or artificially-generated datasets that have been created and utilized to accomplish this in addition to for validation and testing purposes (see section 3.5).

This note does not apply to unsupervised models of learning that detect certain patterns that haven’t been identified by humans.

Source: (The Economist, 2019[71[71]) (S&P 2019[48[48]), (Calders and Verwer 2010, 2010[72(Calders and Verwer, 2010, [72]).

Humans’ role in decision-making processes influenced by AI is crucial finding and correcting biases that are built into the data or the design of the model. In addition, to understand the results from the algorithm, however the degree to which possible is an open issue (US Treasury 2018[8[8.]). The human parameter is critical both at the data input stage and at the query input stage and a degree of scepticism in the evaluation of the model results can be critical in minimising the risks of biased model output/decision-making. FM email database

The development of an ML model as well as its verification can increase the degree of confidence in the reliability of the model with regard to avoiding biases that could be. A poorly designed and controlled AI/ML models are at chance of enhancing or reforcing existing biases, while also making discrimination more difficult to detect (Klein 2020[73]). The mechanisms for auditing the algorithm and of the model which sense-check the results from the models against data from baseline can ensure that there isn’t discrimination or unfair treatment by the machine (see section 3.4.1). In the ideal scenario, supervisors and users

Should be able to evaluate scoring systems to verify their accuracy and fairness (Citron and Pasquale 2014[50[50]). Tests could also be conducted using the principle that the classes that are protected can be identified from other characteristics in the data, and various techniques can be used to detect or rectify discrimination within models based on ML (Feldman and co. 2015 [74]). Control of AI/ML models and the responsibility that is assigned to the human element that the program is crucial to protect prospective lenders against unfair biases. When evaluating potential biases It is important to avoid comparing decision-making based on ML to an imaginary unbiased state however, it is important to use real-world benchmarks, such as comparing these methods with traditional methods of decision-making based on statistics and human judgment that aren’t completely untruthful.
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Perhaps the most well-known difficulty faced by models using ML is the difficulty in separating the outputs of a model to the fundamental reasons for its decision-making that is, knowing the reasons and the way the model produces outcomes. This issue of justifying or rationalizing the model’s decisions and outputs is usually described as ‘explainability”. AI-based models are extremely complicated due to the nature of the technology used. The possibility of deliberate concealment by the market players of the mechanisms of AI models to protect their intellectual property can only increase the difficulty of understanding. Due to the widespread gap in technical literacy for the majority of end-users, accessing the code isn’t enough to understand the mechanism that the models operate. This is further exacerbated by the inconsistency between the complexity of AI models as well as the requirements of human-scale reasoning, or styles of interpretation that are compatible with human mind (Burrell 2016[75[75]). buy FM database online

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The perception of distrust among the supervisors and users of AI applications could be due to the inability of explanation for ML models. AI-powered finance solutions are becoming increasingly opaque and even if the mathematical principles behind these models are explained, they lack the ‘explicit declaration of knowledge’ (Holzinger 2018, 2018[76]). Improving the explainability levels of AI applications can therefore contribute to maintaining the level of trust by financial consumers and regulators/supervisors, particularly in critical financial services (FSB, 2017[77]). As a governance and internal control point of view the minimum amount of explanation must be guaranteed for an AI model committee in order to understand the model that is presented before the panel and to be at ease with its implementation.

The absence of explanationability may be incompatible with the existing regulations which requires the explanation of the logic or the disclosure of that logic. For instance, regulations may require that algorithms be understood and explicable throughout their entire life cycle (IOSCO 2020[78(IOSCO, 2020[78]). Other policies could grant citizens the right to explain regarding algorithmic decisions as well as information about the reasoning involved, for instance, the GDPR of the EU19 that is used to make credit decisions or in the calculation of insurance prices for example. Another instance is the possible application of ML to the calculation of regulations (e.g. risk-weighted assets , or RWA in the case of credit risk) when the current regulations stipulate that the model must be easily explicable or at the very least under the supervision of a human judgment (e.g. Basel Framework for Calculation of RWA for credit risk Models used 36.33).20 FM email database

The lack of explanationability in the ML-based models utilized by participants in the financial markets could be a macro-level threat in the absence of proper supervision by supervisors of micro-prudential supervision since it is becoming difficult for firms as well as supervisors to determine the impact of models on market conditions (FSB 2017 [79]). Particularly, AI could introduce or increase the risk of systemic risk due to procyclicality due to the an increased chance of herding and convergence of strategies among users of off-the-shelf third-party model providers. Without an appreciation of intricate mechanisms that underlie a model, users aren’t able to determine the way their models impact market conditions, and if they are a source of market-related fluctuations. Users are also not able to alter their strategies in times of low performance, or during situations of stress, which can lead to the possibility of episodes of increased market volatility, as well as bouts in liquidity when there is intense stress, which could trigger flash crashes of the type that can occur. Risks of manipulating markets (e.g. Spoofing See Section 2.2) or collusions that are tacit (see Section 3.2.1) can also be possible without an appreciation of fundamental mechanism of the model.

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Market participants who use AI-powered models are under increased scrutiny of the explainability of the model. In part, due to increased interest, a lot of market participants are striving to enhance the explanationability of these models in order to to understand their behavior during normal market conditions as well as during times of stress and also to deal with the risk. In contrast to the post hoc explicability of a single decision the ability to explainability through the design of the model, i.e. built in the AI mechanism is more difficult to attain because (i) the user may be unable to comprehend the reasoning behind it; (ii) certain models cannot be fully understand (e.g. certain neural networks) and (iii) the complete disclosure of the algorithm is comparable to giving the IP away. FM Email

An intriguing debate relating to explainability is whether and in what way AI explainability is any different from that which is required for the use of other complicated mathematical models used in finance. There is a possibility of AI applications are required to meet a higher standard and therefore subject to a more stringent explanationability requirements in comparison to other technology, with negative consequences for the development of new technologies (Hardoon 2020[80[80]). The purpose of the explainability evaluation at the committee level should concentrate on the fundamental dangers that the model could expose the company to, and whether they are manageable, as opposed to the fundamental mathematical potential.
Given the trade-off between explainability and performance of the model, financial services providers need to strike the right balance between explainability of the model and accuracy/performance. A degree of understanding of the model’s workings and the fundamental theory behind the model along with the reasoning used to make its decisions, will prevent models from being viewed as ‘black boxes’ . This could allow companies to be in compliance with regulations as well as build confidence with customers. Certain countries do not allow models that are black boxes, and in cases where there is no level of explanation attained (e.g. Germany). email marketing database FM

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It is important to note that there isn’t any requirement for a one-size-fits-all or a one-size-fits-all method for explaining ML models. The ability to explain will be dependent to a large degree on the context (Brainard Lael 2020 [81]) (Hardoon 2020[80Hardoon, 2020[80]). In assessing the ability of the model to be understood one must consider what the person asking, and the predictions made by the model. Additionally, ensuring explanationability of the model doesn’t in itself ensure an accurate model. accurate (Brainard Lael 2020[81(Brainard Lael, 2020[81]). The contextual alignment of explainability with the intended audience should be combined with shifting the emphasis on ‘explainability of risk’ i.e. understanding the risk that results by using the model rather than the model’s methodology. Recent guidance from the UK Information

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Commissioner’s Office suggests the use of five contextual factors to assist in determining the level of explanation required for the model: impact, domain of the model, the data used, urgency and the public (see the box 4.3) (UK Information Commissioner’s Office 2020[82[82]). FM email database providers

Box 3.2. The explanation of decisions made using AI Guidance from the UK Information Commissioner’s Office

The UK Information Commissioner’s Office has provided guidance for information dissemination regarding AI-based decision making which includes five context-specific factors that affect the reason why people would like more explanations for these decisions.

These context factors comprise:

domain is the place or the area of activity

impact – the impact of the decision person;

data – the data used to develop and test the model data, which could determine whether a consumer will either accept or challenge an AI-based choice; FM Email

the urgency of the decision: how long the consumer is allowed to reflect on their decision; and

the audience to whom are the people the company is explaining an AI-driven decision to that determines what kind of information is important to them.

A guideline was also given on the priority of explanations for AI-assisted decision-making, and stressed the importance of fostering an understanding and appreciation of AI use in the general public.

Source: (UK Information Commissioner’s Office, 2020[82]).

3.4.1. The auditability of AI algorithm and model FM Email

The inherent complexity of these black box models poses issues for regulators regarding the transparent and accountable operation of the models for a wide range of financial service usage cases (e.g. lending) (US Treasury, 2018[8]). It can be difficult and, in some cases, impossible to conduct an audit on an ML model when it is impossible to break down the results of the model to its core drivers. The lack of explanation makes it difficult for the supervisor to understand the process which led to the model’s output, thus limiting an audit’s possibility. Many laws and laws in certain areas are based on an expectation of auditability and transparency, but this is not always accomplished when AI-powered models are employed. Audit trails are only maintained if they can provide proof of the order of processes or activities, and this is hindered by the lack of interpretation of certain AI models. Because the decisions made by such models are no longer an orderly process and since the models themselves have insufficient interpretability, there is an urgent need to discover ways to increase the clarity of AI results, while also providing accountability and strong governance in AI-based systems.

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Research efforts aimed at improving the readability of AI-driven apps and rendering models of ML more suitable for ex-ante and ex-post inspection are being conducted both within the academic world (Vellido Martin-Guerrerrero, Vellido, and Lisboa 2012[83(Vellido, Martin-Guerrerrero and Lisb) and also by the industry. email marketing database FM

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3.4.2. Disclosure FM  lists

In accordance with in the OECD AI Principles In accordance with the AI Principles of the OECD, “there ought to be transparency and responsible disclosure of AI technology to make sure that users understand AI-based decisions and have the ability to contest the results’ (OECD, 2019[5]). It is believed that the opaqueness of algorithms could be tackled through the requirement of transparency, which will ensure that precise information is made available regarding how the AI technology’s abilities and weaknesses (European Commission 2020[84[84]). In addition, consumers should be informed about the usage of an AI system for the production of a product, as well as their interactions through an AI system, instead of humans (e.g.

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robo-advisors). These disclosures can also help customers to make informed decisions about which products to purchase.

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At present, there is no standard practice regarding the amount of information that should be made available to consumers of financial services and investors and the potential proportionality of such information. As per market regulators there must be a distinction regarding the level of transparency, based on the kind of investor (retail or institutional. institutional) as well as the place of application (front and back office vs. back office) (IOSCO 2020[78]). The requirements for suitability, like those that apply to the selling of investment products, could help companies determine if potential customers have a clear understanding of how the application of AI impacts the supply of the product or service.
Financial institutions must record in writing the particulars of operations and design aspects of financial models were in place prior the introduction of AI. The documentation of the reasoning of the algorithms, in the sense that it is possible, is currently being utilized by regulators as a method to ensure that the outcomes generated by the algorithm are explicable and reproducible (FSRA 2019[85[85]). The EU is one example. It is examining requirements regarding disclosure of programming documentation and training methods, processes and methods used to construct, test and verify AI systems, as well as documentation of what the algorithm does (what the model should be optimized for, what weights are designed according to certain parameters before the model is created, and so on.) (European Commission, 2020[84]). It is reported that the US Public Policy Council of the Association for Computing Machinery (USACM) has suggested an ad hoc set of rules that target transparency and auditability when it comes to the algorithmic use, suggesting that data, models as well as decisions, algorithms and other data should be recorded in a way that they can be available for audit when there is a suspicion of harm (ACM the US Council for Public Policy 2017[86[86]). The Federal Reserve’s guidelines regarding model risk management an explanation of the development of models as well as validation sufficient to enable anyone unfamiliar with a model to know how it functions and its limitations and its key principles

Financial service providers face difficult to document the model algorithm used by AI-enabled models to supervise (Bank of England as well as FCA 2020[88[88]). The difficulties in explaining the mechanism of the model result in difficulties documenting these complicated models, regardless how large the company. Certain jurisdictions have suggested an approach that is two-pronged for AI models’ supervision. (i) Analytical: using an analysis of source code as well as of the data using methods (if possible , based on standard) to document AI algorithms as well as predictive models and datasets as well as (ii) empirical: using methods that provide reasons for a particular decision or the algorithm’s behavior using two methods for testing an algorithm in an untested black box such as challenger models (to test with the model being tested) and benchmarking datasets both of which are curated by auditors (ACPR 2020[89[89]). FM  lists

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In addition to explainability-related challenges, AI-based models require the setting of a wealth of parameters that have a significant effect on model performance and results. This kind of parameterisation can be seen as subjective and arbitrary because they could be built on intuition, not verification and are dependent on the creator of the model. The transparency of the parameters chosen within the model may help solve the problem in that the method by which the model is able to work with these parameters is complicated to explain.

3.5. The robustness and durability of AI models Training and testing results FM mailing lists

AI systems should function in a stable, secure and secure manner throughout their lifetimes and risks that could arise should be constantly assessed and monitored (OECD 2019[5[5]). The security that AI systems have AI technology can be enhanced through carefully preparing models for training, and also by testing the effectiveness of models in accordance with the purpose for which they were designed.

3.5.1. Training AI models by validating them and test their capabilities

To capture higher-order interactions (i.e. non-linearity) models might require training using bigger datasets since the effects of higher orders are more difficult to identify. Therefore, the data that are used to train models must contain enough data to record non-linear relationships as well as tail events within the data. This is difficult in reality, as tail events are extremely rare and the data may not be sufficiently robust to ensure optimal results. In the same way the use of ever-growing sets of data to train models can lead to static models which, in turn could reduce the efficiency of the model as well as its ability to learn. email marketing database FM

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It is difficult for the financial industry to develop models using datasets that contain tail events has created a serious security risk for finance, reducing the credibility of these models during times of uncertainty and crises, and rendering AI an instrument that can be employed only when the market is stable. Models using ML run the possibility of over-fitting when an ML model is trained to perform extremely excellently on the sample used for training , but performs poorly on samples that are not known to the model, i.e. the model isn’t generalised effectively (Xu and Goodacre 2018 [90]). To avoid this risk modelers divide the data into a training set and a test/validation sets and employ this set of training data to create models that are (supervised) model that has various model parameter settings and the test/validation set is used to test the model that was trained and test its accuracy, and optimize the parameters. The validation set includes data with provenance known However, the classes are not yet known to the models, so predictions based on the validation set permit the modeler to test its accuracy. Based on the error on the validation set most optimal parameter set for the model is identified by using the one that has the lowest error in validation (Xu and Goodacre 2018[9090]). FM email database providers

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The performance measured by validation models was once regarded by researchers as an impartial estimation of the performance of these models, however, a number of recent studies have proven that this notion is not always the case (Westerhuis and co. 2008[91]), (Harrington, 2018[92[92]). Based on these studies, having an extra unblinded test set that is not utilized during the validation and selection process is essential to get an accurate estimation of the generalisation capabilities that the model. These validation procedures extend beyond the basic test of a model based on historical data to evaluate its post-hoc predictive capabilitiesand to ensure that the outcomes of the model are repeatable. FM mailing lists

Synthetic datasets are produced to serve as test sets to validate and offer an intriguing alternative since they provide endless quantities of simulated data and are a possible cost-effective way to increase the predictive power of models and improving the quality of ML models, particularly in situations where real data is expensive and scarce. Certain regulators require, in certain cases an evaluation of outcomes produced by AI models in testing scenarios designed by authority in charge of supervision (e.g. Germany) (IOSCO, 2020[78]).

Monitoring and confirming models throughout their lifetime is crucial to the risk management of any kind or model (Federal Reserve, 2011, [87(87.) (see box 3.4). Validation of models is conducted following model training to verify that the model has been properly implemented, and also it ensures that the model utilized and is performing as it was it was intended. It consists of activities and processes designed to determine whether models are in accordance with their intended objectives and their business needs, as well as making sure that models are reliable. This is accomplished by identifying possible shortcomings and assumptions, and the assessment of the potential consequences. Every component of the model that include processing, input, and reporting, must be tested for validity This applies equally to models that are developed in-house as to models that are outsourced or supplied by third-party providers (Federal Reserve, 2011, [93]). Validation should be conducted periodically to identify any limitations in the model and discover any new ones particularly in times of financial or economic stress conditionsthat may not be included within the model training sets. FM mailing lists

Testing continuously for ML models is vital in order to detect and rectify’model drifts’ in the shape of data or concept drifts. Concept drifts are the old ideas (Widmer 1996[94Widmer, 1996[94]) and refer to situations in which the characteristics of the statistical variables that the model is studying are altered, which alters the fundamental concept of the model’s ability to predict. For instance concepts of what constitutes fraud may change over time as new methods of conducting an illegal activities, and such changes could result in the concept being drifted. FM quality email lists

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Data drifts are triggered when the variables in the data input change, which impact the model’s ability to predict. The significant shift in consumers’ attitudes and preferences toward digital banking and ecommerce is a great illustration of these data changes that were not accounted for by the initial data set that the model was built and cause performance decline.

Continuous monitoring and verification of models based on ML is an efficient way to stop and deal with such drifts, and standardised methods for monitoring could help in improving model resilienceand identifying when the model is in need of adjustments, redevelopment or replacement. As a result the importance to have a solid framework in place that permits models to be swiftly updated with new data as the distribution of data changes to minimize the risks of model drift. email marketing database FM

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Box 3.3. Guidance on managing risk in models across Europe, the US and EU which applies to AI models

Supervision and the regulatory letter SR 11-7 that was issued from the Federal Reserve in 2011 provides an unambiguous, technology-neutral guideline for modeling risk management that has endured over time and certainly is helpful in managing the risks associated model-based AI (Federal Reserve, 2011, [87(87). FM address lists

The letter offers guidelines on the development of models, their implementation , and use by banks institutions. It also examines (i) the development of models and implementation; (ii) model validation and usage; and (iii) the governance of policies, and controls. FM quality email lists

Recently in the past, it was reported that the European Banking Authority (EBA) issued guidelines on monitoring and origination of loans as well as rules for proper managing model risk. The EBA seeks to ensure that the guidelines are both future-proof and technologically neutral (EBA 2020[95[95]).

Alongside ongoing review and monitoring of the model or code used, certain regulators have mandated the existence of “kill switches’ or other control mechanisms that send alerts in the event of high risk situations. Kill switches are an example of control mechanisms that are able to quickly end an AI-based program in the event that it ceases to perform as per its intent of the system. In Canada for instance, companies are required to incorporate “override” functions that can automatically stop the the system or permit the firm to shut down the system remotely, if it is required (IIROC 2012, 2012[96[96]). These kill switches must be monitored and tested in order to ensure that companies are able to rely on them in times they require.

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There could be a need to strengthen the current risk management functions and processes that are based on models, to anticipate new risks or unintended outcomes that may arise from modeling using artificial intelligence. For example, the effectiveness of models could require testing in extreme market conditions to avoid the risk of systemic vulnerabilities and risks that could arise during situations of stress. The information used to train the model might not accurately reflect market conditions that are stressed or the changes in exposures, behavior or activities, thereby leading to model limitations and possibly diminishing the model’s performance. The current use of these models also implies that they are not tested in dealing with risk in changing financial conditions. It is crucial to utilize a variety of scenarios to test and back-testing, to permit analysis of changes in market behavior and other developments, which could reduce the chance of underestimating risk in these scenarios (FSB 2017[77]). FM quality email lists

Research indicates that explanations that are “human-related” can greatly affect the user’s perception of the system’s accuracy, independently of the actual accuracy that is observed (Nourani and co. 2020[97]). If explanations that are less meaningful to humans are given in the explanation, the reliability of the system that doesn’t use a rationale that is easily understood by humans can be less correctly evaluated by users.

3.5.2. Correlation without causation , ineffective learning

The relationship between causal inference with ML has become an growing area of research (Cloudera 2020[98Cloudera, 2020[98]). Understanding cause-and-effect connections is a crucial element of human intelligence which is lacking FM consumer email database

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From pattern recognition from pattern recognition systems. Deep learning researchers are becoming more aware of the significance of such issues, and using them to guide their research, even though this kind of research is very much in the early stages. FM address lists

The users of ML models are at risk of interpreting the patterns of correlation that are observed in activity as causal connections which could lead to questionable model outputs. The transition from the concept of correlation to causality is essential in determining the reasons why the model could fail, since it helps us to determine what we can expect from the pattern to in the future to be predictive. Causal inference can also be crucial in the ability to replicate the empirical results of a model in new settings, environments, or groups (i.e. external accuracy for the output of the model). The capacity to transfer causality effects that were that were learned from the test dataset to a fresh collection of data that only observational studies are able to conduct, is referred to as transportability. It is a crucial factor in the effectiveness and reliability of ML models (Pearl and Bareinboim 2014[9999). It is beneficial for supervisors to have a basic knowledge of the assumptions that casual AI models make to understand the possible risk.

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The outputs of ML models must be assessed in a proper manner and the human judgment is crucial in this regard, particularly in the issue of causality. If not treated with a degree of caution or suspicion and without causation, the correlations that are discovered in the patterns generated created by AI-based models could lead to false or biased decisions. Research suggests that AI-based models are constrained to develop suboptimal strategies when they fail to take into consideration the advice of humans and, perhaps, unexpectedly, even when human’s choices are less precise than those of the model (Zhang and Bareinboim 2020[100[100]). purchase FM email lists

3.5.3. Risks associated with tail and AI: The case of COVID-19’s crisis

While AI models are adaptive in that they change as they learn from fresh data sources, they might not be able to function in the face of unique single-time events that haven’t had before like that of the COVID-19 crises, do not reflect in the data that are used to train the model. Since AI-managed trading systems are built upon dynamic models trained on long-term time series, they are likely to be successful so long as the market conditions have some degree of consistency with the past. The results of a study that was conducted by UK banks indicates that approximately three-quarters of bankers have experienced an adverse impact on the performance of ML models during the time of the pandemic (Bholat, Gharbawi and Thew 2020[101]). This could be due to the fact that the pandemic has caused significant changes in macroeconomic variables including rising unemployment, and mortgage forbearance, both of which necessitated the ML (as and the traditional) models adjusted.

Events that are unexpected such as the recent pandemics, can lead to discontinuities in the data that in turn cause model drifts that reduce the model’s predictive power (see section 3.5.1). Events that cause tails can trigger unexpected changes in the behavior of the variable the model is trying to predict, as well as previously undocumented modifications to the structure of the data and the underlying patterns of the data utilized by the model as a result of changes in the market dynamics in these occasions. These changes aren’t detected by the original dataset from where the model developed and could cause performance decline. Synthetic datasets created to train models may be able to incorporate tail similar events and include information from the COVID-19 period and could be used to redeploy models that were previously used. purchase FM email lists

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Continuous testing of models using validation datasets that contain extreme scenarios, and constant monitoring of model drifts is crucial to reduce the risk of being exposed during situations of stress. It is worth noting that models that are based in reinforcement-learning, in which the model is taught in scenarios that simulate the conditions, are expected to perform better during times of tail risk events that are one-off because they are more easy to train using scenarios that involve extreme market conditions that might not have been seen previously.

3.6. The governance of AI systems and the accountability

A solid governance framework and transparent accountability mechanisms are essential when AI models are used in high-value decision-making applications (e.g. when deciding who has access to loans or how portfolio allocation is determined). Organizations and individuals who are creating using, deploying or operating AI systems must be accountable for their correct operation (OECD 2019[5in determining who gets access to credit or how investment portfolio allocations are determined). Furthermore human oversight during creation of the product and throughout the lifespan of AI product or system might be required as a security measure (European Commission 2020[84[84]). FM consumer email database

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Presently, participants in the financial markets who use AI are relying on existing oversight and governance frameworks to make use of these technology, since AI-based algorithms aren’t considered to be significantly different from the conventional models (IOSCO 2020[78]). The existing governance frameworks that apply to models could be the foundation for the development or modification of AI activity, since some of the concerns and risks th