Improve the Performance of Your Bank’s Risk Models

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AI is reshaping retail and commercial banking bringing opportunities for greater innovation and operational efficiency. It also brings intensifying regulatory oversight and a growing reliance on robust model management. As regulatory requirements evolve and oversight increases, banks must ensure their risk models are accurate, transparent, explainable, and continuously monitored—but fragmented, stale, and low-quality data remains a major barrier.

The proliferation of data sources—internal, external, structured, and unstructured—creates new challenges in data lineage, governance, and traceability. Fragmented, stale, and low-quality data not only undermines model performance, but also complicates financial and regulatory reporting, making it harder to meet compliance standards and respond to market shifts. A unified data layer provides accessible high-quality data with strong lineage and governance, allowing banks to deliver accurate, automated insights across high-stakes areas like AML/KYC/fraud, credit decisioning, liquidity management, stress testing, balance sheet management, and regulatory reporting.

• Increased fraud and compliance risk: GenAI-enabled fraud schemes are projected to drive losses of up to $40B by 2027.1 Manual AML/KYC processes and fragmented watch-lists slow response times and increase exposure.
• Poor model performance: Risk models trained on outdated or inconsistent data suffer from drift and reduced predictive accuracy.
• Missed risk signals: Siloed data limits the ability to detect emerging threats, such as synthetic identity fraud and market volatility.
• Operational inefficiencies: Manual data aggregation slows credit decisions, impairs risk management, and stifles scalability.
• Lack of consistent view: Data silos and duplication prevent unified risk and customer intelligence, making it difficult to optimize revenue through share of wallet to meeting regulatory demands.

Transforming risk models isn’t just about compliance—it drives business value by facilitating faster decisions, reducing operational costs, optimizing the use of capital, and protecting and growing revenue. Evolving models require diverse data, yet disparate sources and latency slow development and deployment. Performance monitoring is equally difficult without continuous access to high-quality data in real time. In fact, all aspects of model lifecycle management are impacted by a poor data architecture.

Smart data fabric architectures overcome these challenges by unifying data across internal and external systems—without requiring data movement. They embed analytics and machine learning directly into the data fabric, enabling real-time insights and automation.

A smart data fabric helps banks improve decision quality and business performance by eliminating data duplication, reducing data latency, and unlocking enterprise-wide intelligence. It provides a single source of truth for data that is accurate, complete, and current.

InterSystems Data Studio™ enables firms to easily create and maintain a smart data fabric architecture that connects data across disparate systems. It automates data management workflows such as loading, transforming, normalizing, reconciling, and validating data, performs data quality checks, offers, and cataloging metadata management—all in one easy-to-use interface. It also offers native AI capabilities to facilitate retrieval augmented generation (RAG), and seamlessly connects to downstream systems and applications, such as modeling environments or BI tools, via a bi-directional API—accelerating and improving the entire risk management process.

InterSystems IRIS® delivers high-performance data processing and AI model execution, ideal for use cases requiring low latency and high throughput—such as fraud detection, credit scoring, and real-time payments intelligence. With InterSystems IRIS, banks can run advanced analytics and machine learning directly on the data layer, enabling real-time insights.

Together, these solutions streamline critical banking risk management workflows:

• AML/KYC/fraud: Integrates real-time transactions and watch-lists for anomaly detection and fraud prevention—automating compliance to speed responses, cut costs, minimize risks, and protect revenue.
• Credit decisioning: Combines structured and unstructured data for real-time analytics and dynamic scoring—boosting accuracy, shortening approval times, reducing costs and risks, and optimizing lending revenue.
• CCAR & CECL: Unifies fragmented data without duplication for auditable CCAR stress testing and real-time CECL analysis for precise provisioning—shortening reporting cycles, lowering costs, mitigating regulatory risks, and enhancing revenue.
• Liquidity management: Provides lineage, secure exchanges, and real-time aggregation for intra-day liquidity reporting—reducing costs, minimizing compliance risks, and financial costs.
• Balance sheet management: Unifies fragmented data for accurate asset-liability matching, forecasting, and optimization—reducing EUC reliance, cutting capital costs, improving resource allocation, and lowering regulatory violation risks (e.g., in FR Y-9C reporting).
• Regulatory reporting: Automates workflows to eliminate manual processes and fragmented data flows, ensuring accuracy, timeliness, and traceability for filings such as FR Y-9C, CCAR, CECL, and other regulations.
• Lineage and governance: Aligns with BCBS 239 Principles 1-7 and 10 by ensuring robust governance, data architecture, accuracy, integrity, and completeness via lineage and audit trails. Supports timeliness and adaptability through real-time aggregation and upholds reporting frequency and accuracy.

1 - Deloitte, “ Generative AI is expected to magnify the risk of deepfakes and other fraud in banking”