AI Governance for Financial Services

ModelOp’s AI Governance software helps leading financial institutions, banks, investment firms, insurance companies, and regulatory bodies get visibility in their AI initiatives across large enterprises, mitigate risk, comply with regulations, and deliver value-generating models at scale
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Customer Stories

Fidelity Investments

AI Model Operations at Scale

“We didn’t want to stifle the creativity of our data scientists, both professional and citizen. Our AI orchestration platforms enable us to deliver robust, value-generating models at speed and keep them that way. We aim to monitor hundreds of AI models in production."

– Paul Howard, Head of Compliance Analytics Architecture, Fidelity

Royal Bank of Canada Capital Markets

RBC Capital Markets optimizes bond trading with AI and ModelOp Center

50% reduction in time to market

AI Use Cases for Financial Services

Financial institutions have long used decision-making models to drive banking, credit, and investment decisions, and as such, these organizations have robust Model Risk Management teams and processes in place. As AI technology rapidly advances, these organizations seek to enhance security, improve customer interactions, and increase operational efficiency. They also need to move carefully to balance the risk and rewards of AI for important initiatives like customer service, anti-money laundering (AML), anti-bribery, know your customer (KYC), and customer decisions, in which bias or errors can lead to legal violations or lawsuits.

Fraud Detection and Prevention

AI analyzes transaction patterns to detect and prevent fraud in real-time. By learning from historical fraud data and continuously updating models with new transactions, AI systems can quickly identify anomalies that suggest fraudulent activities, such as unusual spending patterns or geographic inconsistencies in card usage.

Credit Scoring and Risk Assessment

AI is used to create more accurate and dynamic credit scoring models. These models can process a broader set of data, including non-traditional sources such as mobile phone usage or rental payment histories, to assess a person's creditworthiness.

Algorithmic Trading

AI enables financial firms to execute high-frequency trading strategies that can adjust to market conditions in milliseconds. These algorithms analyze large volumes of market data to make trading decisions that aim to maximize profits based on predictive analytics and historical patterns.

Personalized Banking

AI powers chatbots and virtual assistants that provide personalized financial advice (robo advisors) and customer support around the clock. These tools can handle a range of functions, from basic inquiries about account balances to more complex queries regarding investment advice, with the aim of automating and improving customer service.

Regulatory Compliance

AI can help financial institutions comply with regulatory requirements by automating the extraction and analysis of data required for reporting. AI systems can also monitor compliance in real-time, alerting institutions to issues that could pose regulatory risks, such as non-compliance with anti-money laundering (AML) and anti-bribery laws.

Robotic Process Automation (RPA)

AI-driven RPA is used extensively to automate routine tasks such as data entry, transaction processing, and compliance checks. This not only increases operational efficiency but also reduces errors and frees up human resources for higher-value work.

How ModelOp Helps Financial Services Firms

AI governance in financial services is crucial for ensuring that the deployment of AI technologies adheres to ethical standards, regulatory compliance, and operational integrity.  Firms doing business in the US must follow the Federal Reserve and US Office of the Comptroller of the Currency's (OCC) SR 11-7 guidance.

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Regulatory Compliance and Reporting

ModelOp helps financial organizations adhere to and enforce regulatory requirements, including US OCC SR 11-7 and those related to consumer protection, anti-money laundering (AML), know your customer (KYC) regulations, and more. ModelOp’s software ensures that AI initiatives are auditable and provides real-time reports to support compliance attestation.

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AI Visibility

ModelOp provides a comprehensive governance inventory that delivers real-time insights into the performance, health, risks, and value of all models. Full visibility into production systems allows for rapid remediation of issues.

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Growth Acceleration

ModelOp’s automated model lifecycle and validation workflows accelerate time to value while ensuring AI systems are developed and deployed in an ethical manner. ModelOp enforces compliance with regulations and policy and provides 25+ out-of-the-box governance templates.


ModelOp Customers Rapidly Establish and Effectively Address AI Governance

Frequently Asked Questions


How does ModelOp ensure compliance with financial regulations?

ModelOp comes pre-packaged with templates for many common regulatory requirements such as SR 11-7, Dodd-Frank Act, GDPR, and the EU AI Act. Furthermore, ModelOp provides regular updates to these templates as policies change and new regulations are enacted.  Second, ModelOp provides the ability to enforce requirements on metadata, documentation, testing, ongoing monitoring, configuration, peer review, and approvals.  Together, this enables our customers to quickly “become compliant” and easily “stay compliant."


How does ModelOp help manage and mitigate bias in AI models?

Identifying and mitigating bias in a model is the responsibility of all stakeholders, and ModelOp enforces requirements on metadata, documentation, testing, serving configuration, peer review, approvals and process gates at any and all stages of the model life cycle. ModelOp’s automated reports, controls, and workflow engine improves effectiveness and efficiency, while removing redundancy and friction from a model’s “path to production.”


How does ModelOp handle data security and privacy?

ModelOp addresses data security and privacy with a three-pronged approach.  First, ModelOp is designed from the ground up to never store model data, but instead, it stores references to those datasets. This ensures all platform-specific security enacted by the data owner remains in place and enforced. Second, ModelOp leverages modern best-practice application design principles. When data does need to be accessed (e.g. to calculate a metric), ModelOp follows the mandates set by data owners and information security stakeholders. Third, ModelOp’s internal engineering team adheres to modern DevSecOps principles to ensure ModelOp is always as secure as possible.

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