Balancing Regulation while Experimenting and Adopting AI
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How do you learn to swim while standing on the shore?
Conventional wisdom suggests that you can’t learn to swim just by taking lessons on the side and watching others swim. The same wisdom seems to guide the financial services industry looking to apply and adopt AI for commercial applications. Customers, shareholders & board members of top financial services companies are all asking how they see AI to be adopted in their setting as a competitive advantage. And the response to those expectations has come in the form of many companies making public announcements of their AI projects and partnerships.?
In parallel, while regulators may have arrived late at the scene for ring-fencing Crypto-world, they want to ensure that they don’t miss the bus for regulating AI. So the Securities Exchange Commission proposed new rules on July 26, 2023, that, if adopted, would regulate conflicts of interest associated with broker-dealers’ and investment advisers’ use of predictive data analytics (PDA) and artificial intelligence (AI) technologies. Gary Gensler, Chair, Securities and Exchange Commission, said, “There’s a risk that the crisis of 2027 or the crisis of 2034 is going to be embedded somewhere in predictive data analytics.” src
The total effect of these opposing pressures is yet to play out. So, momentarily, what the firms publicly say versus privately do about applying AI can be expected to be different stories. Fast-following leading adopters may be an excellent strategy to control risks and costs. However, “Fast-following with experimentation & very selective production use” may deliver the anti-proverb of “learning to swim from the shores without risk of drowning.”
How do you manage your AI-bets and fast-follow with continuous experiments?
At the risk of oversimplification, we believe that firms who will be successful on adopting AI use case (@ scale) while managing AI regulations are the ones who can :
This article will cover how firms do early explorations ( #A above) and build data expertise that many AI-Tech giants and smaller AI startups (like Theoremlabs.io) use to safely use case discovery without getting drowned in natural and PII data complexities.
Solution-Path: Synthetic Data:-
Link to the Dashboard: https://shorturl.at/qBNQW
Understanding Synthetic Data Generation:
Synthetic data is an artificial data type, crafted to mirror genuine data's statistical attributes and patterns. It emanates from sophisticated algorithms and techniques designed to replicate the traits of real-world datasets. The central purpose behind generating synthetic data is to offer a secure, privacy-compliant alternative to authentic data, ensuring it remains valuable for analysis, modeling, and training AI systems.
The Differences Between Real and Synthetic Data:
Real data is collected directly from individuals or organizations and contains personally identifiable information (PII) such as names, addresses, and financial details. On the other hand, synthetic data is generated using algorithms and contains no personal information. This key distinction allows synthetic data to be shared, analyzed, and used for training models without privacy concerns or legal constraints.
Another significant difference is the availability of data. Real data is limited by the amount of data collected, while synthetic data can be generated in unlimited quantities. This scalability is particularly beneficial for machine learning applications requiring large datasets to train accurate models.
Benefits of Using Synthetic Data in Finance:
The use of synthetic data in the finance industry offers several significant benefits:
1. ? Privacy Protection: Synthetic data eliminates privacy concerns by ensuring no personal information is present. This enables financial institutions to share and collaborate with partners, fintech companies, and regulatory bodies without compromising data confidentiality.
2. ? Data Sharing and Collaboration: Regulations such as GDPR and CCPA restrict sharing of real financial data. Synthetic data allows financial institutions to overcome these barriers, facilitating industry data sharing, collaboration, and innovation.
3. ? Rare Event Prediction: Synthetic data generation tools can create artificially balanced datasets, especially useful for predicting rare events like fraud. By generating synthetic instances of fraud, machine learning models can be trained more effectively to identify and prevent fraudulent activities.
4. ? Simulations and Stress Testing: Financial institutions must often test their strategies and models under extreme conditions, such as market crashes or system failures. Synthetic data can be used to simulate such scenarios, providing valuable insights and helping organizations develop robust strategies to mitigate risks.
5. ? Improved Model Accuracy: Machine learning models require large amounts of data to achieve high accuracy. Synthetic data generation techniques can help augment the size of the dataset, leading to improved model accuracy. Additionally, synthetic data comes with predefined labels, eliminating the need for manual data labeling and reducing the risk of errors.
Synthetic Data Generation Tools in Finance:
Now that we understand the benefits of synthetic data in the finance industry let's explore some of the top synthetic data generation tools that are transforming the way financial institutions handle data.
Mostly AI offers an AI-powered synthetic data generation platform that learns from the statistical patterns of the original dataset. The AI then generates synthetic data that conforms to these learned patterns. With Mostly AI, financial institutions can create entire databases with referential integrity, enabling them to build better AI models and enhance data privacy.
It is a leading synthetic data generation platform trusted by numerous companies for AI initiatives. It allows users to specify data requirements using a YAML configuration file and run data generation jobs as part of a data pipeline. With a generous free tier, it provides an opportunity for businesses to experiment and evaluate their capabilities.
YData's synthetic data generation tool enables the generation of various data types, including tabular, time-series, transactional, multi-table, and relational data. By leveraging YData's AI and SDK, financial institutions can overcome data collection and quality challenges, facilitating the development of innovative solutions while preserving data privacy.
Gretel AI offers powerful APIs to generate unlimited amounts of synthetic data. Its open-source data generator provides flexibility and transparency, allowing financial institutions to fine-tune the generation process according to their needs. With Gretel AI, organizations can easily integrate synthetic data into their workflows and accelerate their AI initiatives.
Copulas is an open-source Python library that enables the modeling of multivariate distributions using copula functions. By generating synthetic data that follows the same statistical properties as the original dataset, Copulas helps financial institutions overcome data limitations and privacy concerns. It is particularly useful for modeling complex relationships and dependencies within financial data.
CTGAN, another open-source Python library, generates synthetic data from single-table accurate data. It identifies patterns within the dataset and generates synthetic data resembling the original distribution. CTGAN is widely used in the finance industry for creating balanced datasets and training machine learning models for fraud detection and risk assessment.
DoppelGANger leverages Generative Adversarial Networks (GANs) to generate synthetic data, particularly for time series data. With its open-source Python library, DoppelGANger provides financial institutions with a powerful tool to create realistic synthetic data for various applications, including market simulations and forecasting.
Synth is an open-source data generator that allows users to create realistic data according to their specifications. It supports generating real-time series and relational data, making it a valuable tool for training machine learning models in the finance industry. Synth is database-agnostic compatible with SQL and NoSQL databases, providing flexibility for integrating existing data infrastructure.
Synthetic Data Vault is an MIT-backed software project offering multiple synthetic data generation tools. These tools include Copulas, CTGAN, DeepEcho, and RDT, all implemented as open-source Python libraries. It provides financial institutions with a comprehensive suite of options to generate synthetic data based on their specific requirements.
Tofu is an open-source Python library that generates synthetic data based on UK Biobank data. While other tools focus on generating data based on existing datasets, Tofu generates data that resembles the characteristics of the UK Biobank study. This unique capability enables financial institutions to explore specific use cases related to phenotypic and genotypic research.
TwinifyTwinify is a software package that produces synthetic data with identical statistical distributions to real data. By twinning sensitive data, Twinify provides financial institutions with a privacy-preserving alternative for testing and analysis. Twinify is an open-source tool that can be used as a library or command-line tool, making it accessible and customizable for various data generation needs.
Datanamic offers data generation tools that help financial institutions create test data for data-driven and machine-learning applications. With customizable data generators and support for various databases, including Oracle, MySQL, MS Access, and Postgres, Datanamic ensures referential integrity in the generated data. This capability is crucial for testing and validating financial models and applications.
Benerator is a versatile software tool for data obfuscation, generation, and migration. It allows financial institutions to generate realistic test data using XML descriptions. With support for various databases and a command-line interface, Benerator helps streamline the testing and training process, ensuring the availability of high-quality data for financial applications.
Solution-Path: Anonymization:-
Link to the Dashboard:?https://shorturl.at/fhlP4
Data anonymization is the meticulous process of modifying or eliminating personal information from a database to prevent the direct identification of individuals, thereby safeguarding their privacy. The primary goal is to render the data so that associating it with its source becomes virtually impossible or, at the very least, exceedingly difficult. By doing so, the data can be harnessed for analysis, research, and various other endeavors without jeopardizing individual privacy or breaching data protection standards. Techniques often employed in this process include data masking, pseudonymization, shuffling, and generalization.
Techniques for Anonymizing PII Data:
Organizations employ various techniques that modify or remove personal information from datasets to achieve effective data anonymization. These techniques include:
Data Masking
Data masking involves replacing sensitive information with fictitious or obscured data while retaining its format and structure. This technique ensures that the data remains usable for analysis and testing purposes while protecting the privacy of individuals. Common data masking methods include encryption, tokenization, and data substitution.
Pseudonymization
Pseudonymization is the process of replacing identifiable information with a pseudonym or a unique identifier. Unlike data masking, pseudonymization allows for the reversible transformation of data, facilitating the re-identification of individuals if necessary. However, the pseudonyms used should be sufficiently secure to prevent unauthorized re-identification.
Data Shuffling
Data shuffling involves reordering the values of individual attributes within a dataset, making it difficult to link specific information with individuals. Shuffling the data obscures the original relationships and patterns, further enhancing privacy protection.
Generalization
Generalization involves aggregating or summarizing data to a higher level of abstraction. This technique reduces the granularity of the data, making it less specific and identifiable. For example, data may be generalized into age ranges or categories instead of storing exact ages.
These anonymization techniques can be used individually or in combination to achieve a desired level of privacy protection while preserving the utility of the data for analysis and research purposes.
Popular Data Anonymization Tools in Finance:
Several data anonymization tools are available in the market that cater specifically to the finance industry's needs. These tools offer a range of anonymization techniques and functionalities, allowing organizations to implement effective privacy protection measures. Some popular data anonymization tools in finance include:
μ-ARGUS
μ-ARGUS is a free and open-source data anonymization tool based on the R programming language. It is designed to support statistical analyses and can create safe micro-data files. With its comprehensive anonymization techniques, μ-ARGUS enables organizations to protect sensitive financial data while ensuring data integrity and utility.
Anonimatron
Anonimatron is another open-source data anonymization tool that specializes in pseudonymization. It allows organizations to generate pseudonymized datasets for performance testing or bug identification. By leveraging Anonimatron, financial entities can ensure the security and privacy of their production data without compromising its usability.
ARX
ARX is a commercial data anonymization tool that offers a variety of anonymization techniques, including generalization, suppression, and perturbation. It also provides users with features to assess the risk of re-identification, allowing organizations to fine-tune the anonymization process based on their specific requirements. ARX is a comprehensive solution for financial entities seeking robust privacy protection for their sensitive data.
Clover DX's Data Anonymization Tool
Clover DX's Data Anonymization Tool is a commercial solution that enables the anonymization of structured and unstructured data. With a wide range of anonymization techniques and the ability to generate synthetic data, this tool empowers financial organizations to protect PII data while maintaining the usability and integrity of their datasets.
Docbyte's Real-Time Automated Anonymization
Docbyte's Real-Time Automated Anonymization tool allows financial entities to anonymize data in real time. It offers various anonymization techniques and allows organizations to customize anonymization rules to meet their needs. This tool ensures that sensitive financial data is always protected, even during live data processing.
Amnesia
Amnesia is a commercial data anonymization tool for structured and unstructured data. It provides a comprehensive set of anonymization techniques, including data masking, pseudonymization, and generalization. Amnesia also offers the ability to generate synthetic data, further enhancing privacy protection in financial organizations.
sdcMicro
sdcMicro is a free and open-source data anonymization tool designed to support statistical analyses. It enables the creation of safe micro-data files for research purposes, ensuring that sensitive financial data is protected while preserving data utility.
g9 Anonymizer
g9 Anonymizer is a commercial data anonymization tool specializing in anonymizing large datasets. It offers a variety of anonymization techniques, allowing financial organizations to choose the most suitable approach for their specific data privacy requirements. g9 Anonymizer also provides the flexibility to customize anonymization rules, ensuring compliance with regulatory standards.
Teradata Data Anonymizer:
Teradata Data Anonymizer is a commercial tool designed to anonymize vast datasets. Catering to structured and unstructured data across various formats like CSV, XML, and JSON, it offers multiple anonymization techniques like generalization, suppression, and perturbation. Its customizable rules and real-time capabilities bolster privacy protection, decrease data breach risks, and ensure regulatory compliance, such as with GDPR and CCPA.
Dataiku Data Anonymization:
Dataiku Data Anonymization is a user-friendly commercial tool offering diverse anonymization techniques and synthetic data generation. Key features include a broad array of techniques like generalization, suppression, and perturbation; customizable rules; synthetic data creation; and a visual interface for ease of use. It aids in enhancing privacy, mitigating data breach risks, ensuring compliance with regulations like GDPR and CCPA, and automating anonymization, boosting organizational productivity.
RapidMiner Anonymization:
RapidMiner Anonymization is a user-friendly commercial solution designed for diverse data anonymization needs, including generating synthetic data. Its features encompass techniques like generalization, suppression, and perturbation; customizable rules; synthetic data production; and an intuitive visual interface for streamlined workflows. The tool bolsters privacy protection, reduces data breach risks, aids regulatory compliance like GDPR and CCPA, and enhances productivity by automating anonymization tasks. Use cases include anonymizing customer, employee, patient, financial, and network traffic data before sharing or publishing. RapidMiner Anonymization is essential for organizations prioritizing data privacy and regulatory compliance.
Implementing Anonymity Tools for Compliance and Security:
Regulatory Compliance
Financial institutions must comply with various regulations and standards, such as the SEC's reporting requirements and data protection regulations like the General Data Protection Regulation (GDPR). Anonymity tools play a vital role in meeting compliance obligations by providing security measures to protect personal and PII data. Implementing these tools demonstrates a commitment to privacy and helps avoid penalties and reputational damage associated with non-compliance.
Internal Data Governance and Risk Management
Effective data governance and risk management are vital to protecting personal and PII data in finance. Anonymity tools enable financial institutions to implement robust data protection policies, enforce access controls, and monitor data usage. Financial institutions can mitigate the risk of data breaches and unauthorized access by establishing clear guidelines and procedures for handling personal and PII data.
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Proactive Security Measures
Anonymity tools go beyond compliance requirements and contribute to proactive security measures. Financial institutions strengthen their overall security posture by implementing encryption, tokenization, and anonymization techniques, making it significantly harder for cybercriminals to gain unauthorized access to sensitive data. Proactive security measures can help identify and mitigate potential vulnerabilities before they are exploited.
Solution-Path: Tokenization:-
Tokenization is a data security technique that replaces sensitive data with unique identification symbols called tokens. These tokens retain the necessary information of the original data while ensuring its security. Unlike encryption, which transforms data into an unreadable format that can be reversed with a decryption key, tokenization replaces the data with randomized characters in the same format. This process makes it virtually impossible for hackers to retrieve the original information from the tokens alone.
The tokenization process can be implemented through two main approaches: vault-based tokenization and vault-less tokenization. In vault-based tokenization, a token vault is a secure database that maps sensitive data values to their corresponding tokens. The original data values are replaced with tokens in a database or data store, and only the token vault holds the key to mapping the tokens back to their original values. On the other hand, vault-less tokenization uses algorithms to generate tokens, eliminating the need for a secure database. In this approach, the original data is typically not stored in a vault, as the tokens can be reversed using the employed algorithm.
Implementing Tokenization in the Finance Industry:
For effective tokenization in finance, institutions must adhere to a structured approach aligning with their unique requirements and compliance needs. This involves:
Data Assessment and Classification: Institutions should comprehensively evaluate their data to pinpoint sensitive PII, such as credit card numbers and social security details. Classification facilitates prioritization for tokenization implementation.
Tokenization Strategy Development: Post data identification, institutions need a detailed tokenization plan, encompassing tokenization scope, token types, and target systems/processes. Integrating this with existing systems ensures a smooth, uninterrupted transition.
Tokenization Implementation: This step replaces sensitive data with tokens. Institutions can opt for vault-based or vault-less methods based on their needs, ensuring the process is safe and tokens are secure against unauthorized breaches.
Data Storage and Management: Establishing robust data storage and management protocols is crucial. This entails stringent access controls, encryption, regular audits, and data retention and disposal protocols, adhering to privacy laws.
Ongoing Monitoring and Maintenance: Tokenization demands persistent monitoring. Institutions should regularly review their systems, stay updated with the latest techniques, and proactively combat emerging threats.
Challenges and Considerations:
While tokenization offers significant benefits for data security and compliance in the finance industry, financial institutions must be aware of potential challenges and considerations:
1. Key Management
The secure management of encryption keys is critical for the effectiveness of tokenization. Financial institutions must implement robust critical management practices to protect the keys to encrypt and decrypt tokens. This includes employing secure fundamental storage mechanisms, regular key rotation, and strict access controls to prevent unauthorized access to encryption keys.
2. Integration Complexity
Integrating tokenization into existing systems and applications can be complex and time-consuming. Financial institutions should carefully plan and allocate sufficient resources for integration to ensure a smooth and seamless implementation. This may involve working closely with IT teams, third-party vendors, and other stakeholders to ensure compatibility and minimize disruptions to business operations.
3. Tokenization Scope
Financial institutions must determine the appropriate scope of tokenization based on their specific data protection requirements and compliance obligations. This includes identifying the systems, processes, and data elements that should be tokenized. Striking the right balance between tokenizing sensitive data and preserving its usability for legitimate business purposes is crucial to maximizing the benefits of tokenization.
4. Compliance with Regulatory Requirements
While tokenization assists in meeting data privacy regulations, financial institutions must ensure that their tokenization processes align with the specific requirements of relevant regulatory bodies. This includes complying with industry standards such as the Payment Card Industry Data Security Standard (PCI DSS) and adhering to jurisdiction-specific regulations like the General Data Protection Regulation (GDPR). Financial institutions should stay updated with evolving regulations and regularly assess their tokenization practices to maintain compliance.
Data Tokenization Use Cases:
Tokenization has found wide-ranging applications in the finance industry. Let's explore some of the critical use cases of data tokenization.
Tokenization is widely used for PCI compliance in the finance sector. Payment card data, such as credit card numbers, are highly sensitive and prone to theft. By tokenizing this data, organizations can reduce the risk of data breaches and ensure compliance with PCI standards. Tokens can be used in payment processing systems, enabling transactions to be carried out without exposing actual cardholder data.
Tokenization plays a vital role in fraud prevention and risk mitigation. Organizations can minimize the risk of unauthorized access and data breaches by replacing sensitive data with tokens. Tokens have no intrinsic value, making them useless to cybercriminals even if they are intercepted. This significantly reduces the potential impact of a security incident and helps protect the organization and its customers from financial losses.
Tokenization enables organizations to perform data analytics and derive valuable insights while maintaining data privacy. With tokenized data, analysts can conduct detailed analyses without accessing or exposing actual sensitive information. This allows organizations to leverage the power of data analytics while ensuring compliance with privacy regulations.
Data tokenization helps organizations comply with data privacy regulations, such as the General Data Protection Regulation (GDPR) and the California Consumer Privacy Act (CCPA). These regulations require organizations to implement strong data protection measures and limit the exposure of sensitive information. Tokenization provides an effective way to protect PII data while still allowing organizations to perform necessary business operations.
Solution-Path: Federated Learning:-
Federated learning is a decentralized machine learning framework that enables collaborative model training across multiple devices or organizations without sharing raw data. Unlike traditional ML approaches that rely on central data repositories, federated learning allows individual devices or organizations to train local models using their data and then aggregate the model updates to create a global model.
The federated learning workflow involves several key steps:
1.? ?Model Distribution: A central server distributes the initial model to participating devices or organizations.
2.? ?Local Training: Each device or organization trains the model locally using its data, keeping the data secure and private.
3. ??Model Update Exchange: Only the model updates or parameters are sent back to the central server after local training, ensuring privacy and data protection.
4. ? Aggregation: The central server aggregates the model updates to create a global model that captures insights from the entire dataset.
Federated learning allows organizations to collaborate and train AI models collectively, harnessing the power of distributed datasets while maintaining data privacy and sovereignty.
Federated Learning Workflow:
To fully grasp the technical aspects of federated learning in finance, let's delve into this privacy-preserving approach's workflow and architectural models.
Aggregation Server Model:
The Aggregation Server model involves a central aggregator and distributed training nodes. The workflow consists of the following steps:
1. ? Model Distribution: The central aggregator distributes the initial global model to participating nodes.
2. ? Local Training: Each node trains the model locally using its dataset, ensuring data privacy and security.
3. ? Model Update Submission: Each node submits its model updates or parameters to the central aggregator after local training.
4. ? Aggregation: The central aggregator combines the model updates to create an improved global model that captures insights from all participating nodes.
The Aggregation Server model is well-suited for scenarios where financial institutions need to train their models on non-communicating nodes, such as clients or partner organizations.
Peer-to-Peer Model:
The Peer-to-Peer model eliminates the need for a central aggregator and relies on direct node communication. The workflow involves the following steps:
1. ? Initialization: One node initiates the model training process and shares its initial model with others.
2. ? Local Training: Each node trains the model using its data without exchanging raw data with other nodes.
3. ? Hyperparameter Exchange: Nodes exchange their model's hyperparameters to benefit from the network's collective intelligence.
4. ? Aggregation: Each node aggregates the received hyperparameters to create an updated model that reflects the insights from the entire network.
The Peer-to-Peer model is suitable when all nodes participate in the training process and act as trainers and users of the final model. This architecture promotes decentralized collaboration and data privacy.
Privacy-Preserving Techniques in Federated Learning
Privacy is critical to federated learning, especially when dealing with sensitive PII data in the finance industry. To ensure data protection, federated learning employs various privacy-preserving techniques. Let's explore some notable techniques applied in federated learning:
1. Data Anonymization
Data anonymization involves masking or removing sensitive attributes, such as personally identifiable information (PII), from the dataset. This technique aims to prevent the identification of individuals within the modified dataset. However, data anonymization should strike a balance between privacy guarantees and utility, as excessive masking or removal may reduce the discriminative power of the data.
2. Differential Privacy
Differential privacy adds random noise to the true outputs or model parameters to prevent identifying individual data points. By introducing controlled noise, differential privacy ensures that the presence or absence of a specific data point does not significantly impact the model's output.
3. Secure Multi-Party Computation (SMC)
Secure multi-party computation enables multiple parties to jointly compute a function without revealing their inputs. Each party can retain control over its data while contributing to the computation process. SMC ensures no party can access or infer information about other parties' inputs.
4. Homomorphic Encryption
Homomorphic encryption allows computations to be performed on encrypted data without decrypting it. This technique enables data privacy during computation, as the data remains encrypted. Homomorphic encryption allows financial institutions to collaborate on training models without sharing raw data.
By implementing these privacy-preserving techniques, federated learning ensures that PII data in finance remains protected while enabling collaborative model training across multiple organizations.
Use Cases of Federated Learning in Finance
Federated learning holds immense potential for various use cases in the finance industry. Let's explore some specific scenarios where federated learning can drive innovation and enhance operational efficiency:
1. Fraud Detection and Risk Scoring
Federated learning can significantly improve fraud detection and risk-scoring models in finance. By collaboratively training AI models on distributed datasets from multiple financial institutions, patterns, and anomalies associated with fraudulent activities can be identified more accurately. Moreover, by leveraging diverse datasets, risk-scoring models can become more robust and provide more accurate creditworthiness assessments.
2. Personalized Financial Recommendations
Federated learning enables financial institutions to leverage customer-specific data without compromising privacy. By training AI models on distributed datasets containing customer preferences, spending habits, and financial goals, personalized recommendations for financial products and services can be generated. This enhances customer satisfaction and facilitates targeted marketing strategies.
3. Anti-Money Laundering (AML)
The fight against money laundering requires comprehensive and up-to-date data from multiple financial institutions. Federated learning allows institutions to collaborate and collectively train AML models without sharing sensitive transactional data. By analyzing patterns of suspicious activities across distributed datasets, federated learning can enhance the detection and prevention of money laundering activities.
4. Regulatory Compliance
Federated learning can aid financial institutions in ensuring regulatory compliance while training AI models. By collaborating on compliance-related modeling tasks, institutions can collectively learn from distributed datasets without violating privacy regulations. This approach enables the industry to stay at the forefront of regulatory requirements while maintaining data privacy and security.
Conclusion:
A mature data-handling sophistication forms one of the three pillars we introduced at the beginning to safely experiment and launch AI use cases in a controlled manner. Multiple solution paths, when combined, will deliver the best results based on the use case and context of the problem at hand. In part 2, we will attempt to get into the depths of the next pillar –Evidence Archival Infrastructure and processes.?
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