Endless data collection. Complex, ever-changing regulations. Mounting pressure from regulators like BCBS, APRA, CCAR, and others. Compliance reporting isn’t just a box to check—it’s a resource-draining burden that exposes your bank to massive risk.
Eliminate errors in financial and regulatory reporting with a single source of truth.
Reduce manual effort and reporting delays with built-in validation and lineage tracking.
Ensure transparency and accountability with complete data traceability.
Meet diverse global and regional regulatory requirements from a unified platform.
Fraud. Money laundering. Terrorist financing. Financial criminals are getting smarter, and the pressure on banks to detect and prevent illicit activities has never been higher. CDOs, how much of your AML strategy is reactive instead of proactive? Are outdated systems, data silos, and false positives slowing your team down while real threats slip through?
Eliminate manual errors, reduce false positives, and streamline compliance by ensuring clean, consistent, and high-quality data for AML and fraud detection.
Detect suspicious activities instantly, prevent fraud and financial crime before they escalate, while minimizing operational delays.
Fill critical data gaps by ensuring complete customer identification and transaction details, enhancing AML system accuracy and reducing blind spots.
Maintain high-performance AI/ML fraud detection by preventing data drift, improving precision, and reducing costly false positives.
Is your credit risk strategy truly optimized? Or are poor data quality, fragmented financial insights, and outdated risk models putting your institution at risk? Incomplete borrower profiles, inconsistent credit scoring, and sluggish risk analysis can lead to bad lending decisions and regulatory penalties.
Ensure accurate credit risk assessments by eliminating inconsistencies, reducing manual errors, and improving data reliability for better decision-making.
Strengthen credit evaluations with predictive insights, balancing risk and return while minimizing potential loan defaults.
Identify and prevent bad data from distorting risk assessments, maintaining data integrity and improving credit risk forecasting.
Streamline data checks, enhance efficiency, and adapt to evolving regulatory requirements without increasing operational burden.
Reinforce compliance and minimize financial losses by ensuring consistent, high-quality data across all lending and credit risk processes.
Disjointed trading data, inconsistent portfolio records, and regulatory complexities are creating unnecessary risks and missed opportunities. Without accurate, centralized, and real-time data, investment decisions become reactive rather than strategic.
Connect and streamline trade data from multiple sources, eliminating silos and enabling more efficient market strategies.
Detect and correct discrepancies in trade records and portfolio data, eliminating errors and reducing operational risk.
Leverage high-quality, real-time data to enhance portfolio analytics, optimize investments, and mitigate financial risks.
Ensure full compliance with evolving regulations by maintaining clean, standardized trade and portfolio records.
Fragmented customer data is costing your bank. Missed cross-sell opportunities, disjointed customer experiences, and incomplete risk profiles. Without a real-time, unified customer view, how well do you really know your customers?
Consolidate customer data post-mergers and across business units, ensuring a single, accurate source of truth for all customer interactions.
Eliminate inaccurate customer records with automated data quality, ensuring consistency and compliance across the organization.
Automate data processing to remove inconsistencies and deliver clean, reliable insights into upstream systems.
Manage and structure customer data efficiently, providing a seamless, high-quality data foundation for all financial products and services.
Legacy system upgrades and migrations to modern core banking and cloud platforms are critical for finance institutions, but poor data quality leads to inefficiencies, compliance violations, and customer service issues during the transition.
Ensure high-quality, structured data before moving to new core systems or cloud platforms.
Prevent errors, duplicate records, and data mismatches post-migration.
Maintain data integrity to meet industry regulations during migrations.
Speed up migration timelines with clean, validated data from day one.
Banks are adopting AI for product innovation, employee efficiency, and customer personalization, but without high-quality, well-structured data, AI models produce unreliable insights, increasing financial and compliance risks.
Improve AI-driven fraud and credit assessments with clean, structured data.
Enable AI to detect anomalies more effectively with consistent, reliable datasets.
Personalize finance offerings using AI trained on high-quality customer data.
Ensure AI models align with ethical and regulatory standards by eliminating data bias and inconsistencies.
Catch and remediate issues early, and maintain baseline data quality for ML training data.
Banks rely on operational reports to track trade and portfolio data, policy trends, and regulatory metrics. Poor data quality results in reporting errors, delays, and misalignment between departments, impacting strategic and financial planning.
Make data-driven decisions with complete, reliable reports.
Ensure reports align with industry standards and compliance requirements.
Reduce manual data corrections and reconciliation efforts.
Maintain consistency in reporting across investments, compliance, and customer-facing teams.
Banks handle massive amounts of data across customer portfolios, credit risk data, and compliance reporting. Without automation, these tasks become inefficient, error-prone, and costly, limiting scalability and business agility.
Automate data validation and approvals to reduce turnaround times.
Eliminate data quality errors for better risk assessment.
Streamline reporting and auditing processes with rule-based automation.
Free up resources by reducing repetitive, manual tasks in credit processes, and compliance workflows.
When banks merge or acquire new entities, they must consolidate data from multiple legacy systems. Without a structured integration process, discrepancies in customer records, policies, and credit data can cause compliance risks, inefficiencies, and reporting challenges.
Standardize and consolidate customer, credit, and regulatory data.
Reduce time to operational efficiency with automated data mapping and cleansing.
Maintain compliance across jurisdictions with validated, auditable records.
Minimize disruptions by ensuring data consistency across merged entities.