For example, the EU AI Act represents a significant legal stake in the ground, requiring the use of AI technologies to adhere to principles of justice, transparency, and accountability. This law, along with growing public and government pressure to take on more responsibility in adopting technology, will trigger industry-changing shifts in the world of finance.

It’s important for financial services IT and data leaders to take stock of their ability to effectively manage what is happening in and around the industry, from increasing data breach risks, competition pressures posed by disruptive fintech start-ups, and the demand for personalized and trustworthy AI apps. These are some of the issues we’ll discuss in a two-part series, detailing the challenges and how the Pentaho+ platform is well-positioned to support financial institutions in their efforts to manage complexity, make data-based decisions, and achieve compliance and visibility in their business.

Regulatory Crackdowns on Algorithmic Bias

In 2023, a U.S. bank was fined $25.9 million for applying a credit-scoring AI model that rejected minority applicants. Post-event research indicated a lack of bias-checking in the training data and a lack of transparency in decision-making.

Challenges:

• Bias Prediction: AI models often overestimate the historical bias in data and will produce biased decisions. Also, with data models being designed by humans, there needs to be a way to check data to avoid unconscious bias that might creep in with data selection processes.
• Accountability: Regulators expect to see evidence of bias mitigation and fairness evaluations. This includes safeguards across the board, from how the model was designed to the data that the models are trained on, and the new data sources that the models are being supported by in ongoing analysis.

How Pentaho Helps:

• Automatic Bias Audit: Pentaho Plus uses machine learning algorithms to continually scan datasets for bias and report anomalies in real-time.
• Data Lineage Tracking: All data sources, transformations, and decision points are traced to enable regulators to have comprehensive reporting.
• Easily Explainable AI Dashboards: Clients and auditors can visualize decision pathways for transparency and confidence.

Potential Industry Shift: Let’s say a bank can not only conform but actively promote its AI fairness solutions. Displaying a fair lending model provides a competitive edge and appeals to more socially aware customers.

Banking’s Rising Systemic Risk

The failure of a few mid-sized banks in 2023  exposed real flaws in risk modeling and stress-testing techniques. Supervisors are pushing for more accurate, faster reporting of risk exposure to ward off failures in the system.

Challenges:

• Data Separation: Risk data is often separated into silos, which can be challenging to map holistically. There is also the issue of how to blend structured and unstructured data to gain a clearer and more accurate picture of risk.
• Rapidity and Precision: Real-time reporting and predictive capabilities are crucial to find the weakness. Many banks still rely on manual processes, introducing time lags and errors that can weaken risk analysis and create the conditions for failures.

How Pentaho Helps:

• Unified Risk Data Model: Pentaho integrates disparate data, including structured, semi-structured, and unstructured data, into one holistic understanding of institutional risk.
• Predictive Analytics: With real-time simulations, firms can simulate stress conditions and predict the effects on liquidity and solvency.
• Real-Time Reporting: Automated, real-time regulatory reports ensure teams can meet evolving requirements.

Potential Industry Shift: What if banks leveraged real-time risk dashboards with regulators and stakeholders? This level of transparency could transform the relationship with the banking industry and define the norm.

ESG Reporting and Green Finance

ESG (Environmental, Social, Governance) continues to find support across the globe, and the EU’s Sustainable Finance Disclosure Regulation (SFDR) makes it mandatory for financial institutions to report on portfolio sustainability. In 2024, one major asset manager was criticized for greenwashing and misreporting ESG data.

Challenges:

• Data Integrity: Validating ESG data and being auditable. Even larger organizations have not prioritized clear processes on how to collect and verify ESG data. There is tremendous reliance on manual data collection and Excel spreadsheets in ESG reporting that introduce errors that limit reporting accuracy and reliability.
• Standardization: Reporting is inconsistent across ESG frameworks. The EU leads in this area; however, even if an organization is headquartered in other regions, after a certain financial threshold, they are held to the same reporting standards as EU HQ organizations. Without established policies and data collection protocols, financial institutions will struggle to meet these standards.

How Pentaho Helps:

• Golden Source ESG Data: Pentaho provides a central database for ESG data that remains consistent across all reports and analyses.
• Automated Data Validation: Algorithms within Pentaho validate ESG data against external benchmarks and standards, detecting discrepancies.
• Configurable ESG Dashboards: These allow portfolio managers to track and optimize sustainability metrics in real time.

Potential Industry Shift: Suppose you could not only be ESG compliant but also launch data-based green finance products. By delivering clear ESG performance metrics, institutions might lure eco-conscious capital and redirect funds to sustainable investments.

In our next blog, we’ll explore the issues around fraud detection and prevention, data sovereignty, cross-border compliance, and safely scaling innovative uses of AI in finance.

Discover how Pentaho supports financial services.

The post Unlocking the Future: Application Case Studies on How the Financial Services Landscape Is Changing – Part I first appeared on Pentaho.

The EU’s evolving AI laws, including the new AI Act, prioritize fairness, transparency, and accountability. These laws will disrupt the way AI is already implemented, requiring banks to redesign the way they manage, access, and use data. Yet, as we’ve seen with other regulations, meeting these acts can provide an opportunity.  As banks evolve to meet these laws, the resulting improvements can increase customer trust and position the banks as market leaders in regulated AI adoption.

Meeting the EU AI Act Moment

There are a few key areas where banks should invest to both adhere to the EU AI Act and reap additional benefits across other regulatory and business requirements.

Redefining Data Governance for the AI Age

Strong data governance sits at the heart of the EU’s AI legislation. Banks must ensure the data driving AI algorithms is open, auditable, and bias-free. Good data governance moves compliance from the status of being a chore to one that is proactively managed, establishing the basis for scalable, ethical AI. They can achieve this through technology that delivers:

Unified Data Integration: The ability to integrate disparate data sources into a centralized, governed environment ensures data consistency and eliminates silos. A comprehensive view of data is essential for regulatory compliance and effective AI development.

Complete Data Lineage and Traceability: Tracking data lineage from origin to final use creates full transparency throughout the data lifecycle. This directly addresses regulatory requirements for AI explainability and accountability.

Proactive Bias Detection: Robust data profiling and quality tools allow banks to identify and mitigate biases in training datasets, ensuring AI models are fair and non-discriminatory.

Building Ethical AI From the Ground Up

Moral AI is becoming both a legal imperative and a business necessity. The EU’s emphasis on ethical AI requires banks to prioritize fairness, inclusivity, and transparency in their algorithms. This demands continuous monitoring, validation, and explainability, all of which can foster stronger customer relationships and differentiate banks as pioneers in responsible AI through:

Real-Time AI Model Monitoring: Integrating with machine learning platforms enables teams to monitor AI models in real-time, flagging anomalies and ensuring adherence to ethical standards.

Explainable AI (XAI): AI explainability is supported by tools that visualize decision-making pathways, enabling stakeholders and regulators to understand and trust AI outcomes.

Collaborative AI Governance: Facilitating collaboration between data scientists, compliance officers, and business leaders ensures that ethical considerations are embedded across the AI development lifecycle.

Streamlined Regulatory Compliance

Regulatory compliance often involves extensive reporting, auditing, and data security measures. Technology that simplifies these processes helps banks navigate the complex EU AI regulatory framework while driving down costs, boosting productivity, and empowering banks to innovate while maintaining adherence to regulations.

Automated Compliance Reporting: Customizable reporting tools generate regulatory-compliant reports quickly and accurately, reducing the burden on compliance teams.

Audit-Ready Data Workflows: A platform with built-in audit trail features documents every step of the data process, providing regulators with clear and actionable insights.

Privacy-Centric Data Management: Support for data anonymization and encryption ensures compliance with GDPR and safeguarding customer information.

Transparency and Accountability: The Hallmarks of Leadership

AI is transforming financial services, but customers’ confidence matters. Banks must be transparent and accountable to generate trust in AI decision-making. When banks treat transparency as a path to redefining relationships, they can transform customer interactions.

Customer-Centric Insights: Intuitive dashboards that allow banks to explain AI-driven decisions to customers, enhancing trust and satisfaction.

Stakeholder Engagement: Interactive visualizations and real-time analytics enable banks to communicate compliance metrics and AI performance to regulators and stakeholders.

Collaborative Transparency: Collaborative features ensure that transparency and accountability are integral to every AI project, from design to deployment.

Leveraging Pentaho for Compliant AI

To fully adopt a strategic approach to AI compliance, banks can capitalize on Pentaho’s capabilities to:

• Develop a Unified Governance Framework
  Use Pentaho to create a centralized data governance model, ensuring alignment with EU standards and global best practices.
• Prioritize Data Lineage and Quality
  Leverage Pentaho’s data cataloging and profiling tools to ensure that all datasets meet compliance requirements and ethical standards.
• Foster Collaboration Across Teams
  Involve compliance officers, data scientists, and business leaders in AI governance, using Pentaho to enable cross-functional workflows.
• Monitor AI Continuously
  Implementing Pentaho’s real-time monitoring and reporting features can proactively address compliance risks and optimize AI performance.
• Communicate Compliance Effectively
  Use Pentaho’s visualization and reporting tools to provide stakeholders with clear and actionable insights into AI processes.

The Path Forward to Robust AI Compliance and Performance

Imagine a world where banks don’t just tackle compliance problems but also use them as strategic growth engines. Pentaho’s full-spectrum data integration, governance, and analytics products empower financial institutions not only to adapt to change but to drive the way in ethical AI practice. This openness helps them not only meet regulatory standards in the present but to set the direction of AI use with due care in the future.

Pentaho is well positioned to help transform finance industry systems into intelligent and compliant AI engines, especially ahead of the new AI regulations coming from the European Union. This is a time of significant change for banks where the right combination of modern technology and enabling regulation can re-energize client trust – an approach Pentaho is looking to lead.

Ready to make compliance your competitive advantage? See how Pentaho powers ethical AI for the financial services industry.

The post What Banks Need to Know About EU AI Act Compliance and Ethical AI Governance first appeared on Pentaho.

What’s driving new privacy and data protection efforts? Several factors.

Global data flows: Trade data increasingly migrates across borders and will demand more international cooperation and coordination with data-protection laws. If I buy a sweater from a vendor in Ireland and live in California, there are two different regulations at work in just that one transaction.

Growing awareness of and expectations of data privacy and demands for greater transparency and accountability will push organizations to improve their data operating practices.

Technological evolution: Developments in computer science, including artificial intelligence, the Internet of Things, and biometrics, have changed attitudes around what needs to be protected. This poses new privacy challenges to the old ways of organizing dataflows, which simply do not work in today’s interconnected world, especially with personally identifiable data sitting in massive global data clouds.

Regulatory evolutions: As new attitudes and technologies like GenAI emerge, governments and regulation authorities will be constantly evolving legislation to address new privacy problems and safeguard individuals. This requires constant monitoring and adjustments by organizations to stay ahead of fines and reputational damage.

Foundational Regulations Every Organization Must Understand

Multiple core legislations already significantly influence the global privacy landscape, including:

GDPR (General Data Protection Regulation) (EU): GDPR is a harmonized data privacy law and an enormous piece of human rights-based change. As of 2018, all data controllers are required to comply when using the personal data of all EU citizens. This pushes organizations to adhere to strict privacy by consent, data minimization, and data deletion requirements.

Data Protection Act 2018 (UK): The UK Data Protection Act implements GDPR and provides further detail on the information rights of individuals and the responsibilities of organizations when handling personal data that must be considered.

California Consumer Privacy Act (US): This California law, effective as of 2020, grants certain rights to consumers for their personal information (e.g., right to know, right to delete, and right to opt-out)​

Here, ‘personal data’ means any information relating to an identified or identifiable natural person (‘data subject’) by references such as a name, an identification number, location data, and online identifiers or factors specific to the physical, physiological, genetic, mental, economic, cultural or social identity of that natural person.

There are also ‘special categories of personal data’ related to racial or ethnic origin, political opinions, religious or philosophical beliefs, or trade union membership, genetic data, biometric data processed to uniquely identify a natural person, data concerning health or data concerning a natural person’s sex life or sexual orientation.

LGPD (General Data Protection Law): This is the Brazilian law equivalent to GDPR that protects Brazilian citizens’ personal data. It defines the rights and obligations of organizations collecting personal data from citizens, on and offline.

Personal Data Protection Act 2010 (India): This law, while perhaps a much less developed version of GDPR, does provide a regulatory framework on which a better-articulated regime can be built.

The Balancing Act Around Critical Use Cases

Data is everywhere and informs so much of our lives. This has put a larger burden on organizations at every level of society to understand their potential exposure to compliance risks and consistently apply policies and technology to safeguard data

Medicine: Patients’ health data (e.g., medical records, genetic data) must be kept private to ensure physical well-being and avoid misuse related to areas like insurance, employment and receiving benefits.

Finance: The number of rules and regulations in this industry match the level of collection and management of customer data that takes place every second of every day. Fraud protection, anti-money laundering and ethical practices are all regulated and support the consumer trust and confidence that is the lifeblood of financial institutions.

E-commerce: A retailer necessarily collects great amounts of personal data to match buyers and sellers, and even facilitate transactions without friction.

Marketing and Advertising: Ideally, advertisers will gain the ability to target messages very sharply. Striking a balance between the ability to curate experiences and the protection of consumers’ privacy is crucial, especially when crossing international borders into the EU and needing to consider where data is stored and how it is used.

Social Media: Social media companies collect and process immense volumes of data related to user behaviors. Unethical use of data is a high risk in these platforms given their ubiquity and how many users cross different age groups and geographies.

Looking Ahead  

For each part of the global privacy matrix – flagship legislation, use-case categories, and local, regional, and global differences – attention to the whole is required. Only then can organizations deploy strategies that stake out a defensible position where privacy interests are balanced against service and commerce goals while also building and sustaining stakeholder trust.

To explore how Pentaho can help enable your organization to become data-fit and manage regulatory compliance data challenges, request a demo.

The post The Key Legislations That Define the “New” Global Privacy Landscape first appeared on Pentaho.

These banks will need to make huge investments. Those will come in the human resources and IT infrastructure required to implement DORA and detailed technical plans to identify, measure, and mitigate ICT risks. These will involve everything related to cybersecurity, using robust incident response plans and 24/7 monitoring.

Traditionally, mid-sized banks have struggled to adapt to changes across a range of asset sizes. While larger banks have more resources, mid-sized banks have smaller budgets and teams that prevent them from fully complying with many regulations.

The technicalities of these standards add an additional layer of complexity. In many cases, confusion can arise as the regulations are unclear and difficult to read and implement for many banks.

In this blog, we’ll dive into unique issues across asset classes, providing an outline of how mid-market banks can tactically optimize their ICT risk management programs to meet regulatory requirements and create resilience to attack in a ever-changing digital age.

Asset Class: $10–$50 billion

Regulatory Adherence Requirements:

• ICT Risk Management: Create a governance process with clearly defined ICT risk oversight roles and functions.
• Exceedance: DORA issues general guidelines, but not precise recommendations to smaller institutions for the exact risk levels and criteria that must be applied for ICT risk.
• Banks’ Incident Reporting: Banks must notify the authorities of large ICT incidents in specific time periods (e.g., 72 hours in EU regulations).

Key Limitations:

• Resources Shortages: Smaller banks lack ICT resilience teams which causes them to take longer to respond and rectify. They also usually lack powerful monitoring and are unable to deliver incident detection and notification times.
• Uncertainty About Testing Requirements: DORA calls for resilience testing but hasn’t articulated what the minimum acceptable conditions should be for mid-sized banks, leaving room for interpretation that could result in audit collapses.
• Regulatory Ambiguities: DORA’s small institution ICT governance guide does not define the right amount of manual versus automated processes, which causes inconsistencies in the compliance methodologies. It is also not fully explored on a technical level for incident reporting best practices (form, content, detail) making it difficult for regulatory tests.

Asset Class: $50–$150 billion

Regulatory Adherence Requirements:

• Third-Party Risk Control: Banks need to control risks from important third-party providers. DORA emphasizes third-party risk monitoring, but it provides no common evaluation methods for vendors.
• Operational Resilience testing: DORA requires annual resilience testing of ICT infrastructures to prevent disruption. Hybrid ICT environments (legacy + cloud) make testing more difficult since DORA doesn’t provide any guidance on how to connect legacy systems to the new frameworks.

Key Limitations:

• Oversight of Vendor Risk: Mid-sized banks are dependent on 3rd party service providers, but DORA lacks explicit responsibility requirements for failures in such relationships.
• Resources Availability: Mid-tier banks don’t have the economies of scale to shop for specific services from vendors in compliance with DORA.
• Regulatory Ambiguities: DORA’s requirements for ICT resilience scenario testing are general and do not contain detailed scenarios for mid-sized banks’ operational risk, so their testing frameworks are not aligned. The act does not explicitly define what constitutes “critical” third-party services, so under-preparing for compliance reviews might be an issue.

Asset Class: $150–$250 billion

Regulatory Adherence Requirements:

• Data sharing: Financial organizations will need to participate in shared resilience measures such as sharing of information about cyber threats and events. Small banks are left out of mature information-sharing systems run by large banks, which is a zero-sum game.
• Disaster Recovery: DORA establishes pre-established disaster recovery objectives (RPO/RTO). Legacy systems are difficult to align with today’s RPO/RTO due to technical debt and inflexible regulatory benchmarks on banks.

Key Limitations:

• Higher scrutiny: Banks of this asset type are subject to more regulatory scrutiny than large banks, but not the same resources.
• Complex ICT Infrastructure: There is a big challenge with resilience in multi-cloud and hybrid environments because DORA doesn’t specify integration frameworks.
• Regulatory Ambiguities: DORA’s definition of “significant operational impact” is fuzzy, leading to reports of incidents being under- or over-reported during regulatory exams. Minimum compliance requirements for cybersecurity resilience standards (e.g., sophisticated threat management, machine learning analytics) are too general to apply consistently.

Regulatory Uncertainty and Cross-Asset Challenges / Regulatory Inaccuracy:

1. Incident Reporting:

• Ambiguity: DORA sets dates but not details about the level of detail that an incident report should contain. Banks can fail the compliance tests if the incident reports are not complete.

2. ICT Risk Assessment:

• Ambiguity: The act in principle establishes a risk management process but leaves blanks for the minimum acceptable risk levels. Banks can build systems that don’t meet regulatory standards in examinations.

3. Testing Frameworks:

• Ambiguity: Annual resilience testing is required but no one clearly specifies what tests are allowed (i.e., penetration vs. red team exercise). Banks run the risk of missing the compliance exams due to unintended testing requirements.

4. Third-Party Management:

• Ambiguity: DORA sets no standard of what is considered “critical” vendors. Banks may focus on the wrong vendors and miss real risks.

5. Cybersecurity Standards:

• Ambiguity: DORA will require strong security, but doesn’t meet certain international (e.g., ISO 27001) requirements for smaller banks. This can lead to gaps in cybersecurity controls that are implemented adequately.

Recommendations for Addressing Limitations

1. Collaborate with Regulators:

• Get involved with regulators, proactively, and clear confusion about compliance metrics, testing requirements, and reporting.

2. Leverage Industry Standards:

• Implement ICT infrastructures based on existing, widely understood frameworks like NIST CSF, ISO 27001 and COBIT to plug the holes in DORA’s recommendations.

3. Invest in Automation:

• AI-powered incident detection, third party risk management and reporting to optimize compliance and reduce resource consumption.

4. Strengthen Vendor Relationships:

• Add explicit resilience criteria into vendor SLAs and audit regularly to ensure you are compliant with DORA requirements.

5. Scenario-Based Testing:

• Design and run specific test scenarios based on bank size, process and systemic impact.

Final Thoughts

The Digital Operational Resilience Act (DORA) offers mid-tier banks more business stability and provides a way to mitigate cyber risk and disruption. But mistakes and vagueness in the act can be compliance headaches.

One of the best ways for mid-tier banks to overcome these challenges is to be proactive with regulators. That means finding regulators, knowing what they expect, and executing accordingly. Standards and best practices will be a legal requirement and drive efficiency.

Operational risk is better managed with preparation. Modern technology investments like cybersecurity and data backups aren’t just a suggestion, it’s necessary. Smart integration will automate processes, mitigate impact, and enable compliance, giving your bank an operational rock-solid foundation.

By engaging with regulators, executing on international best practices, and taking the lead in technology, mid-size banks will not only have better chances of DORA compliance but also set themselves apart from their competitors in a rapidly changing financial landscape. It’s the future-forward thinking that can make your bank strong and competitive.

Learn more about Pentaho for Financial Service.

The post DORA Compliance Strategies for Mid-Tier Banks by Asset Category first appeared on Pentaho.

After years of running Pentaho Data Integration Community Edition (CE) on a single machine, a leading organization found itself at a critical juncture. Its financial data operations were straining under the weight of growing regulatory requirements, expanding data sources, and cloud adoption strategies. The move to Pentaho Data Integration Enterprise Edition (EE) on AWS would be more than just an upgrade – it would be a complete re-architecture of their data integration framework.

The Challenge: Securing and Scaling Financial Data Pipelines

The organization had been using CE for financial data extraction, transformation, and reporting, but as workloads increased, several challenges surfaced:

• Lack of governance and security controls over sensitive financial data.
• Inefficient execution of ETL workloads, leading to performance bottlenecks.
• No native cloud scalability, restricting data movement between on-prem systems and AWS.
• Manual encryption and decryption workflows, making vendor file ingestion cumbersome.

In short, the existing architecture had reached its limits, and a once manageable system had become a high-risk, high-maintenance bottleneck.

The Migration: From CE to Enterprise-Grade ETL on AWS

The move from CE to Pentaho Data Integration Enterprise Edition was not just about software – it was about enabling the organization’s cloud-first financial data strategy. The project focused on three key areas: deployment, security, and workload efficiency.

1. Architecting a Secure, Cloud-Native Deployment

The first step was lifting CE off a single machine and deploying it as a scalable, enterprise-ready solution. The new architecture introduced:

• Pentaho Data Integration EE deployed across DEV and PROD environments on AWS EC2, ensuring redundancy and failover protection.
• A centralized repository using AWS RDS (PostgreSQL) to replace the file-based artifact storage of CE.
• SSL encryption enforced across all Pentaho instances, securing financial data at rest and in transit.

This transformation eliminated single points of failure and set the foundation for a scalable, governed ETL framework. 

2. Automating Secure File Ingestion & Data Encryption

A critical aspect of the migration was handling encrypted vendor files – a common requirement in financial data processing. The existing process required manual decryption before loading data, creating compliance risks and operational delays. With Pentaho Data Integration EE, encryption and decryption were fully automated using GPG-based secure key management.

• Keys were centrally managed, ensuring controlled access and compliance with financial data security policies.
• PDI transformations were designed to decrypt vendor files automatically, removing manual intervention.
• End-to-end encryption was enforced, securing the data from extraction to reporting.

This shift not only streamlined file ingestion but also reduced human error and compliance risks.

3. Optimizing ETL Performance in AWS

 With the deployment stabilized, focus shifted to optimizing financial data processing workloads. Key improvements included:

• Parallelized job execution, eliminating bottlenecks in ETL workflows.
• Direct integration with AWS services, including Redshift and S3, enabling faster data movement and transformation.
• Implementation of Pentaho Operations Mart, allowing real-time ETL performance monitoring and logging.

By optimizing how jobs were distributed and executed, processing times dropped by up to 40%, ensuring faster financial reporting cycles.

The Result: A Cloud-Ready Financial Data Platform

The migration to Pentaho Data Integration Enterprise Edition on AWS delivered tangible improvements across security, efficiency, and scalability.

• Significant reduction in ETL processing time, with parallelized execution and optimized job scheduling.
• Automated file encryption and decryption, removing security gaps in vendor data ingestion.
• Cloud-native architecture, enabling seamless data movement between on-prem and AWS.
• Stronger governance and auditability, ensuring compliance with financial reporting regulations.

Pentaho Data Integration Enterprise Edition for Financial Data

For organizations dealing with sensitive financial data, the transition from Pentaho Data Integration CE to EE is not just an upgrade – it’s an operational necessity. By leveraging AWS for scalability, automating encryption, and optimizing ETL performance, this organization built a future-proof financial data pipeline that ensures governance, security, and speed.

As financial data landscapes continue to evolve, Pentaho Data Integration Enterprise Edition provides the scalability and compliance enterprises need to stay ahead. This robust integration offers both stronger governance and auditability while aligning with financial reporting regulations, making it an invaluable upgrade for any business. If you’re interested in exploring how, contact Pentaho Services to learn more.

The post Scaling Financial Data Operations with Cloud-Ready ETL first appeared on Pentaho.

One of the world’s largest financial institutions was facing growing complexity in its data integration infrastructure. Their existing ETL framework, while initially effective, was struggling to scale with increasing regulatory demands and evolving cloud architectures.

Their goal: lay the groundwork for a resilient and future-proof data infrastructure with contemporary containerized architectures while upholding rigorous governance standards. The move: Pentaho Data Integration Enterprise Edition (EE) with Kubernetes-based execution.

The Drive for Secure and Scalable Data Processing for Financial Operations

The institution’s existing ETL architecture relied on a mix of traditional processing, backed by a large Pentaho Data Integration Community Edition footprint and manual deployment processes. As data volumes grew and regulatory oversight increased, several key challenges emerged:

• Security and Compliance Gaps: The existing system lacked granular access controls and containerized security measures, which posed significant compliance risks. Additionally, the data logging and observability features were insufficient for effectively tracking job execution history.
• Operational Complexity: Managing multiple environments – including on-premises, hybrid cloud, and Kubernetes clusters, all without a centralized orchestration strategy – increased operational complexity. This led to inconsistent ETL workload balancing, causing inefficiencies during peak processing periods.
• Scalability Limitations: With increasing data volumes, the need for efficient parallel execution became evident. However, the existing framework was not optimized for containerized job execution. An incomplete Kubernetes migration left legacy components dependent on outdated execution models, hindering scalability.

The organization embraced a Pentaho Data Integration (PDI) EE-based solution that would seamlessly integrate into their containerized, cloud-first strategy while modernizing their data pipeline execution model.

Deploying A Secure, High-Performance Data Pipeline Architecture

The proposed Pentaho architecture was designed to modernize execution workflows, improve governance, and enhance operational efficiency. The approach focused on three core pillars: security, scalability, and observability.

1. Strengthening Security & Governance

To secure financial data pipelines while maintaining regulatory compliance, the new architecture introduced:

• Kubernetes-native security with isolated Pods for ETL job execution, ensuring process-level security and container control. Role-based access controls (RBAC) and LDAP integration were implemented to enforce granular security permissions at both the job and infrastructure levels.
• Advanced observability and auditing through a new Pentaho plugin for real-time tracking, historical logs, and performance analytics. The execution history storage would allow compliance teams to audit job performance and access logs as part of governance requirements.

2. Optimizing Performance with a Composable ETL Framework

The legacy processing model limited parallelization and execution speed. The proposed Kubernetes-aligned framework introduced a more dynamic and efficient approach to workload management, allowing for better resource allocation, improved fault tolerance, and seamless scaling.

• Tray Server & Carte Orchestration: Tray Server dynamically allocates workloads across multiple Kubernetes clusters instead of relying on static worker nodes, ensuring optimal resource utilization and enhanced execution efficiency. The Carte API enhancements allow for real-time execution monitoring and job prioritization that improves overall system responsiveness.
• Containerized Job Execution: ETL jobs executed in independent, process-isolated containers reduces memory contention and allows jobs to scale elastically based on demand. The introduction of a proxy job mechanism ensures efficient job initiation within Kubernetes, optimizing resource allocation and execution speed.
• Push-Down Processing with Spark Integration: The new PDI execution framework leverages Spark for distributed processing, which optimizes large-scale transformations. The architecture supports Pentaho’s continued development of a Spark-based execution model, ensuring a future-proof migration path that enhances performance and scalability.

These innovations collectively ensure a robust, scalable, and high-performance data pipeline, ready to meet the demands of modern data processing.

3. Enabling Observability & Real-Time Execution Monitoring

Real-time execution visibility is crucial to ensuring immediate detection and swift remediation of job failures and performance bottlenecks. Advanced analytics and alerting mechanisms were integrated to enhance system management, reducing downtime and improving reliability for a resilient and responsive data infrastructure.

• Custom Observability Plugin: A new custom observability plugin was developed to provide real-time execution logs, historical tracking, and system-wide performance insights. Execution metrics are stored in a history server, enabling compliance and engineering teams to track job performance over time.
• Kubernetes-Native Job Execution Monitoring: Kubernetes-native job execution monitoring was integrated directly into the Tray and Carte execution APIs, allowing for automated alerting and remediation. The new OpsMart dashboard would provide a single-pane-of-glass view into all ETL executions, facilitating easier oversight and operational efficiency.

With these enhancements, the institution is now poised to leverage improved observability for a more secure, scalable, and efficient data pipeline.

The Power of a Secure, Scalable, and Observability-Driven Data Pipeline

The proposed Pentaho Data Integration Enterprise Edition architecture delivered significant improvements across security, scalability, and operational efficiency.

• Stronger governance and compliance with LDAP-based authentication and detailed execution auditing.
• Scalable, containerized ETL execution ensuring dynamic workload balancing across Kubernetes clusters.
• Enhanced job monitoring and logging, allowing real-time failure detection and historical performance tracking.
• Optimized data movement, with push-down processing reducing bottlenecks in large-scale data transformations.

Delivering Secure Enterprise Data Pipelines at Scale

In today’s current regulatory environment, financial institutions must secure and optimize data pipelines for regulated, high-volume data. The shift to Pentaho Data Integration Enterprise Edition with Kubernetes integration offers the scalability, governance, and security financial services required to stay ahead in a rapidly evolving regulatory landscape. By implementing containerized execution, real-time observability, and enhanced governance controls, this institution is well-positioned to drive their financial data operations into the future.

Is your financial data pipeline equipped to meet the next generation of compliance, performance, and security demands? Discover how you can prepare by contacting Pentaho Services today to learn more.

The post Securing and Optimizing Financial Data Pipelines first appeared on Pentaho.

Where exactly are mid-tier banks struggling, and what’s the best path forward?

Infrastructure Limitations

Mid-sized banks are held up by legacy, closed systems that inhibit agility and scale. Transitioning from these legacy infrastructures to cloud-based platforms or data models is important for modern data demands but requires significant technology and talent investments.

Mid-tier banks usually lack the in-house resources to support emerging data management strategies such as data lakes, real-time analytics, and integration with third-party apps. Cloud adoption, for example, is especially difficult for smaller banking organizations when facing vendor lock-in, compliance challenges, and the high costs of transitioning to a cloud-based environment.

Data Governance and Reasoning Gaps

Data governance is a daunting mid-tier bank challenge. There are a host of regulations across the globe – including GDPR, PSD3, and the Bank of International Settlements’ BCBS 239 – that require strong data governance platforms to achieve compliance. Unfortunately, mid-level banks don’t have robust organizational frameworks that ensure data quality, consistency, and availability for reliable governance and compliance.

Data rationalization (i.e., getting rid of duplicate, out-of-date data) introduces more problems. Rationalization can reveal problems with how data is gathered and stored. Without effective governance, it’s almost impossible to avoid the penalties of non-compliance that come with data that is not managed in a correct and traceable manner.

Missing Out on AI’s Potential

Artificial intelligence provides a tremendous opportunity for banks to enable better decisions, enhanced customer experiences, and reduced operational cost. But AI can be heavily constrained in mid-market banks due to resource scarcity. Where big banks have access to in-house AI talent and scale to experiment, mid-size banks are left relying on outside suppliers or ready-made tools that aren’t entirely equipped to tackle their specific challenges.

Every AI-based system requires high-quality data. With existing governance and data management challenges, it’s hard for these banks to deliver what AI needs to be effective. Then there are the ethical and compliance issues to consider related to implied discrimination or data privacy and security violations that can come with mismanaged AI, which mid-tier banks aren’t well positioned to address.

Risk and Liability in the Face of Global Laws

A top three headache for middle-level banks is risk management and compliance. These institutions must follow both local and constantly evolving sets of complex international regulations, from AML and Know Your Customer requirements to the adherence to foreign banking regulations.

Compared to international banks with entire departments dedicated to compliance and risk, mid-market banks can only afford small groups, forcing them to prioritize some regulations over others. This increases exposure to risk, where deficiencies in compliance systems could slip through the cracks, leading to costly fines and negative reputational impacts.

The Compounding Effect of Limited Resources

Lack of budget puts mid-market banks in a bind, facing a choice between continuing investment in legacy solutions, or in AI and data modernization, or in robust governance and compliance systems.

Talent attraction and retention issues exacerbate the situation. Lower and mid-market banks don’t have the same types of compensation packages or growth paths as larger banks, and struggle to attract the kind of talent they need to upgrade data systems. Lack of competent people to do data engineering, AI and regulatory compliance also limits flexibility in which issues they can address at any one time.

Overcoming The Obstacles

Mid-tier banks need to strategically modernize their data and governance frameworks to handle regulatory pressures and evolving security threats. A strong option for many is adopting low-cost, scalable solutions in the form of closed-source data platforms designed for compliance. These platforms offer robust controls that align with regulatory standards while providing cost efficiency, flexibility, and scalability – crucial for banks operating within budget constraints. Choosing a compliance-focused vendor who also offers the option of a Data-as-a-Service (DaaS) or fully managed solution through a partner can further support these banks, helping to balance modernization with budget limitations.

Integrating AI-driven tools within closed-source environments can enhance risk management and streamline compliance. Tailored AI solutions, designed for the specific regulatory and operational needs of mid-tier banks, offer critical insights by identifying transaction patterns and pinpointing potential compliance risks. This approach significantly reduces the burden on smaller compliance teams by automating routine checks and flagging anomalies without the need for broad, generalized AI models.

Standing at the Corner of Data, AI and Compliance

Mid-tier banks have hit a data management dead end. There are large demands on time to modernize data centers, drive strict governance, leverage AI, and manage risk in a world where everyone is looking to be compliant.

For sustained success in a heavily regulated, data-centric world, mid-tier banks should take a measured, resource-conscious approach to modernization. By choosing technology partners who deliver compliance-ready, closed-source solutions and fostering a culture focused on data governance and agility, these banks can address regulatory demands efficiently, innovate responsibly, and stay competitive in a rapidly shifting market.

Learn more about how Pentaho is uniquely positioned to help mid-tier banks solve their data governance, quality, and compliance challenges here or request a demo.

The post Bridging The Gaps: Helping Mid-Tier Bank IT Teams Minimize Risk and Reach Data Modernization, Compliance and AI Goals first appeared on Pentaho.

BFSI (banking, financial services, and insurance) companies must ensure that their data quality controls are working, clear, and consistent with business and regulatory needs in a more challenging regulatory environment where global standards are constantly evolving.

Below, we consider the drivers behind BFSI data quality challenges and needs and its role in facilitating stronger risk management and compliance practices.

Defining Data Quality for BFSI

Data quality is a broad umbrella term for any quality of data: it encompasses all data properties, including accuracy, completeness, consistency, timeliness, validity, and reliability. Because BFSI is a field where data not only determines the course of trade but also strategic business decisions, data quality must be monitored and validated across all these segments.

For example, having the correct customer information for both Know Your Customer (KYC) and correct transaction information for Anti-Money Laundering (AML) reviews. Data that’s incomplete or outdated biases risk calculations, and inconsistent data sources can wreak havoc on financial statements. For these reasons and many more, BFSI companies require a high-level data quality infrastructure.

What Does Data Quality Mean for Risk Management?

BFSI is a very high-risk sector where bad data can make financial institutions unable to quantify and mitigate risk, exposing them to:

• Credit Risk: Incomplete credit or finance records result in bad credit reports that lead to more NPLs (Non-Performing Loans).
• Operational Risk: The bad data may lead to errors that impact customer satisfaction and business performance. Data issues also generate false predictions, resource issues, and outages in basic banking operations.
• Market Failure: Incorrect data can cause erroneous market risk estimations, which will defraud the organization of funds.
• Data Governance Risk: Bad data quality leads to non-compliance with mandatory reporting, KYC, or AML, which can result in large fines and reputational damage.

Regulatory Compliance and Data Quality

Regulators across the globe are setting strict limits on how BFSI data is stored. Mandates such as the Basel III Accord, the General Data Protection Regulation (GDPR), and the FinCEN requirements require BFSI organizations to provide transparency, accuracy, and accountability with regard to data quality.

Basel III agreement: Implores strong data quality control to secure sufficient capital reserves and bank-based credible risk calculations.

GDPR: Although GDPR is mostly a data privacy law, it also refers to data accuracy, particularly in the case of personal information. GDPR data-error fines and reputational costs could reach billions of euros.

AML FinCEN: Financial institutions must report suspicious transactions pursuant to anti-money laundering laws. AML algorithms are successful and precise only with reliable data.

Bad data can mean penalties, suspension, or reorganization. Regulatory violations can have more than just financial consequences: business disruption, brand erosion, and damaged customer confidence can also be at stake.

International Data Quality Standards and Industry Standard Specifications

In compliance with regulatory and risk management policies, BFSI providers have to adhere to global data quality standards. ISO 8000 standard, for instance, is the industry standard in BFSI, that defines the requirements for data quality across critical attributes. This, along with compliance with FINRA and DMBOK standards, is what BFSI embraces more and more in terms of data governance. Through these metrics, BFSIs can harmonize data quality activities with global standards to be more efficient in their operation and competitive in their position.

While those challenges and risks are very real, having a strong approach to data quality can bring BFSI many benefits.

BFSI and Digital Transformation

The BFSI industry is experiencing digital transformation through advanced analytics, AI, machine learning, and big data technology. Digital transformation might challenge data quality as the volume and variety of data increases. At the same time, it is also an opportunity for data quality improvements by leveraging automated data checks, real-time monitoring and anomaly detection.

Some banks, for example, use machine learning models to recognize suspicious transactions and spot fraud. AI tools can also automatically correct errors in data that will keep data integrity from decreasing as the data quantity increases. As BFSI organizations transition to digitalized operations, data quality will play a key role in the success of digital transformation and ensuring that technology investments are secure against data breaches.

Data Quality Leads to Better Business Processes and Reduced Operating Costs

High quality data can save significant amounts of money in data storage, error correction and regulatory reporting. Data reconciliation, validation, cleansing, and data cleansing is often the cost incurred by BFSI companies because of poor data quality. Through a proactive data quality approach, they can be automated, redundant processes eliminated, and operating costs mitigated.

Data governance practices such as data validation, real-time quality control, and identifying data issues ahead of costly errors will sustain this. Data quality can also enhance cross-functional operations such as compliance, risk, and customer service. Once the data is safe, workers are more intelligent — they focus on contributing value, not rectifying data.

Enhanced Customer Trust and Experience

The BFSI market success is based on customer belief and faith. Quality data is what drives personalized, accurate and timely services. Poor quality data causes service interruptions, transaction errors, and customer complaints. With proper data quality, BFSI companies can offer personalized financial products, enhanced communication and optimized experiences. Also, data quality supports customer data safety and ethical usage per data privacy regulations such as GDPR.

Establishing Data Quality Management Systems for BFSI

There are a few key things to consider when designing a comprehensive data quality system, including:

• Data Governance: Data stewards, data custodians, and compliance officers handle the data standards across the organization, and they rely on a data governance framework with policies, roles, and responsibilities.
• Data quality monitoring solutions: BFSI companies must adopt data quality testing software to identify false positives, anomalies, and real-time failures for proactive maintenance.
• Data Lineage and Traceability: Data lineage and traceability allow data source, transformation, and use to be accountable and transparent to regulatory authorities.
• Audits & Monitoring: A data quality system must be continuously monitored and audited on a regular basis to identify any emerging data quality issues and enable BFSI institutions to quickly act.
• Employee Training & Awareness: Employees should be trained and informed on the importance of data quality and how to keep updated with the data standards to implement a successful data quality program.

Final Thought

BFSI data quality is a strategic imperative for risk, regulatory compliance, customer confidence, and efficiencies. When it comes to a digital economy in which data is both a gift and a curse, data must be high quality for BFSIs to flourish.

Considering evolving regulations, data quality will always remain at the core of BFSI resilience and competitive advantage. BFSI organizations that invest in data quality will be able to join the world’s standards, stay on-side, and scale.

The post BFSI Data Quality: Implementing World Class Risk and Compliance Measures first appeared on Pentaho.

DORA compliance is especially challenging for mid-market banks. In contrast to larger peers, many lack the resources and dedicated compliance teams to fully respond to DORA’s requests. That leaves them especially vulnerable to DORA, which requires banks of all sizes to demonstrate digital resilience that’s durable, ongoing, and adaptive.

While this is a looming regulatory burden, it presents a real opportunity for smaller and mid-sized banks. While meeting DORA will likely force action, with the right data management platform, banks can efficiently address these needs and position themselves as competitive and compliant. This is where closed source platforms enter the picture.

Here, we’ll explore the various reasons closed source platforms are critical to DORA compliance and how to provide a foundation of a strong, forward-looking operational platform for mid-tier banks.

Why Closed Source Platforms are Best Built for DORA

Closed source platforms offer several advantages and capabilities when developing a DORA-compliant architecture.

1. Security: Private Security Against New Attacks

   Closed source platforms don’t allow code to be hacked like open-source products. They’re outfitted with proprietary security and kept up to date for emerging threats – matching DORA’s stringent cybersecurity standards.

   For instance, a closed source solution usually has encryption, access controls, and full data masking built in. These protections are carefully monitored by the platform’s in-house engineers, meaning middle-market banks can provide maximum security without extensive customization. And closed source solutions with AI-powered cybersecurity provide a high level of protection that can detect and respond to the threat in real-time.

   To DORA’s benefit, this security-first thinking positions a bank to be protected and ready – with robust defense mechanisms that are tailored to the financial sector.

2. Stability and Consistency: The App That Just Works

   Reliability is a requirement in the financial sector, and closed source platforms are often designed for consistency. While some open-source solutions can be susceptible to issues with compatibility or unexpected downtime due to mismatches in code, closed source solutions are maintained by engineers. They undergo stringent quality assurance to ensure that they scale and work well across use cases.

   For a mid-tier bank who wants to stay online forever, a trusted closed source platform is key. Data is always available, true, and compliant, even under extreme operational stress. This reliability underpins DORA’s requirements for continuity and ensures banks can continue to function without a concern of unanticipated outages or inconsistencies in data that could pose a compliance risk, while benefiting from efficiencies in areas such as data retention, archive, and disposal.

3. Compliance-Ready Features and Proactive Support

   Perhaps the most important feature of a closed source platform is its compliance model. DORA requires continuous monitoring, compliance, and incident reporting – all of which can bog down small compliance teams. Closed source solutions also often include automated audit trails, real-time alerts, and incident tracking features, all specifically focused on financial compliance.

   DORA’s requirement to report incidents can feel overwhelming for mid-market banks. Closed source solutions typically have built-in incident management processes that allow banks to automate incident detection, response, and reporting. In addition, closed source solutions have their own support teams, who are well-versed with the regulatory environment. This experience helps banks make smarter decisions in terms of compliance, using best practices and current guidance. Thanks to this proactive backing, mid-tier banks can better adapt to evolving regulations while establishing an active compliance culture, continuously scanning for risks before they become severe.

4. Control and Data Integrity: Making Data a Trusted Asset

One of the essential parts of DORA compliance is data integrity. With a closed source system, banks can execute data lifecycle tasks with a unified point of control in a secure environment for tasks such as data validation, governance, quality assurance, and storage. Through their own infrastructure, banks can impose strict access controls, limiting access to the data only to those authorized to see it.

With such control, mid-sized banks can maintain data integrity from entry to retention. It backs DORA’s focus on high quality, transparency, and traceability, helping banks to develop a database that is strong and dependable.

How Mid-Tier Banks Can Move Toward DORA

Mid-tier banks should consider the following strategic moves to take advantage of the potential of a closed source data management platform:

• Adopt a Scalable, Modular Platform

  A closed source platform with modular deployment allows banks to start with core compliance capabilities and efficiently expand capabilities as needed, providing a scalable roadmap toward resilience.

• Embed Conformity in Organization DNA

Utilize a closed platform’s compliance tools to drive a culture of ongoing compliance. With automation, it is possible to create a compliance culture across the bank that supports DORA’s vision for resilient operations.

• Leverage Specialized Services for Regulation Strategy

Closed source solutions are supported by compliance professionals. Mid-tier banks must tap this well, working with platform teams to craft bespoke regulatory strategies that not only comply with DORA, but also predict the future.

• Prioritize Data-Centric Security

As DORA’s focus on data integrity shifts to banks, a data-based security approach is imperative. End-to-end encryption, security access controls, and audit trails ensure better security, customer privacy, and compliance.

• Prepare for Future Regulatory Trends

DORA could soon have an impact on the rules beyond Europe. By choosing a platform with regulatory scalability, mid-market banks can set themselves up for jurisdictional regulatory convergence and become a digital resilience benchmark.

Closing Thoughts

DORA compliance for mid-market banks isn’t a regulatory tick box; it’s a key opportunity to reinvent resilience in a fast-moving digital world. Adopting a closed source data management solution is an innovative move that can meet DORA’s rigorous standards while providing the highest levels of security, reliability, and compliance. This strategy gives banks a firm base that will not only serve to keep up with today’s requirements, but also stay flexible enough to meet tomorrow’s needs, in an environment where regulatory pressure is only mounting.

In a highly trusted industry, selecting a closed source platform means ensuring customer data, digital assets, and a strong operating infrastructure. In the case of mid-market banks, it isn’t a matter of meeting current needs, but of securing a sustainable infrastructure that fosters trust, security, and flexibility that will make for the most successful banks in the coming decade.

Contact our team to learn more about Pentaho for mid-tier banks.

The post Why Mid-Tier Banks Require a Closed Source Data Management Solution to Meet DORA Compliance first appeared on Pentaho.

Across a wide spectrum of the financial industry – from credit unions to fintech companies and data brokers – now have new data access, privacy, consent, lineage, auditability, and reporting requirements. Compliance with these new CFPB requirements will be a massive operational and technical issue for most companies.

Below is a breakdown of the unique issues that arise with the new CFPB guidelines and how impacted organizations need to rethink their data lineage, privacy controls, automation, and auditing strategies.

The Personal Financial Data Rights Rule (Open Banking) 

The Personal Financial Data Rights Rule from the CFPB seeks to enable consumers to manage, access, and share financial information with third-party providers. Financial institutions have to offer data access, portability, and privacy protection with total control over who has seen the data and when.

Key Challenges and Strategies: Data Access and Portability

Banks and financial institutions must allow consumers to migrate their financial information to third parties. Institutions will need to demonstrate when, how, and why consumer data was passed. They must also protect consumer information and only share the consented data. 

Automated ETL (Extract, Transform and Load) can help institutions collect consumer financial information across diverse sources (CRMs, payment systems, loan management systems) and turn it into common formats for easier management and tracing. This will also support lineage, crucial to providing regulators a full audit trail. Integration with Open Banking APIs and being able to integrate data with third parties directly will be essential.

Role based access is an important control to ensure only authorized users and systems are accessing defined data, and being able to mask or encrypt PII helps when making consumer data anonymous when it is provided to third parties.

The New Data Broker Rules 

The CFPB’s revised data broker rules expand the scope of the Fair Credit Reporting Act (FCRA) and includes Credit Rating Agencies. Data brokers who purchase, sell, or process consumer data now have to respect consumer privacy, consent, and deletion rights.

Key Challenges and Strategies: Data Deletion Requests 

Under this new rule, brokers will need to comply with consumer data deletion requests.  Data brokers must guarantee only explicit consent to share consumer data. Regulators are now demanding an audit trail of who and with whom consumer data was shared. 

Automating data deletion workflows helps organizations automatically detect and delete every reference to a consumer’s data in databases, data warehouses, and third-party data lakes. Being able to purge workflows on request ensures that databases are automatically cleansed, duplicates removed, and consumer records deleted when CFPB requests data deletions. 

Marking and categorizing consumer data and grouping it according to privacy policies and access levels enables data to be more easily managed and deleted when needed. Also, data masking blocks access to non-PII data from third parties to support access and anonymization requirements.  

Being able to track data as it is processed across databases and APIs provides the ability to demonstrate with certainty to regulators how, where and when data was used. All of these capabilities support the regular reporting that can be submitted directly to the CFPB.

Supporting Data Privacy, Consent, and Portability

Both CFPB regulations are focused on consumer consent, privacy management, and data portability. Businesses must now allow consumers to have control over their data and know where it is being shared.

Key Challenges and Strategies: Consent Tracking 

Consumers must be able to cancel their consent to sharing data. They need to have access to and the ability to export their personal data in common formats. This means multiple data silos Data must be synchronized with new consumer consent.  

Visualizing consumer consent data and monitoring change requests over time will be crucial for compliance and reporting.  Organizations will need to have clean data change logs supported by data lineage metadata to provide a full audit trail.

Having data management tools that integrate with REST APIs will make it easier to export consumer data to other banks or fintech providers as needed. The ability to export data in multiple formats, such as CSV, JSON, or XML, allows integration with third-party programs. It will also be important to sync consent updates between multiple data warehouses so that consumer data is removed from the system when consent is revoked. 

Assuring Perpetual Compliance with CFPB Audit & Reporting Requirements. 

In the long term, CFPB compliance will require businesses to consistently be transparent, demonstrate compliance, and issue regulators demand reports. This means organizations must adopt audit-friendly data lineage, be able to produce reports on-demand that capture a wide variety of variables, and be able to spot errors early to triage mishandling, validate missing or incorrect data, and proactively address the issues before auditors discover them.  

Meeting The Consumer Data Privacy New World Order Head On 

The new CFPB data privacy, consumer consent, and broker practices are significant hurdles for financial institutions. Compliance requires data governance, real-time audits, and data sharing. Pentaho’s entire product portfolio — from Pentaho Data Integration (PDI), Pentaho Data Catalog (PDC), and Pentaho Data Quality (PDQ) — meets these issues through data privacy, portability, and auditability.

With Pentaho’s data integration, lineage management, and consent management functionality, financial companies can meet the CFPB’s regulations and reduce the risk of non-compliance fines. Contact our team to learn more! 

The post New CFPB Data Compliance Requirements Will Test the Limits of Financial Data Management Strategies first appeared on Pentaho.