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Supervisory Reporting Analytics

Dynamic Graph Analytics and Machine Learning for Micro- and Macro-Prudential Supervision

Supervisors require continuous, comprehensive, and flexible tools to monitor the modern financial system.

Traditional methods utilizing static spreadsheets or dashboards fail to map complex direct and indirect exposures. Without the ability to visualize interconnectedness and leverage massive amounts of granular and dynamic data, financial authorities struggle to identify hidden vulnerabilities, structural similarities, and potential contagion channels before systemic crises occur.

Supervisory Reporting Analytics applies dynamic and interactive graph analytics to vast amounts of granular supervisory data.

FNA helps transform static regulatory reports into interconnected knowledge graphs, allowing regulators to monitor the individual soundness of institutions while simultaneously mapping system-wide direct and indirect exposures. By automating analytical processes, authorities can efficiently understand, quantify, and supervise systemic risks.

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Comprehensive System Overview: Consolidates vast amounts of granular, dynamic supervisory data into a single, interactive dashboard to monitor industry-standard measures of financial health at aggregate and individual levels.

Micro-Prudential Analysis: Ingests granular, institution-centric data to examine financial statements, related parties, and direct exposures, accurately monitoring the soundness of specific institutions.

Macro-Prudential Analysis: Leverages graph analytics to map direct and indirect exposures across the ecosystem, identifying super-spreaders and monitoring contagion channels caused by counterparty and liquidity risk, and institutional coupling.

Process Automation & Scope Expansion: Automates complex analytical processes on granular, dynamic supervisory data for real-time monitoring while easily incorporating new data sources to extend oversight to non-banking entities.

Supervisory Reporting Analytics Use Cases

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Track direct exposures and related-party risks with granular precision

Ingest detailed, institution-centric financial statements to continuously monitor the soundness of specific entities. Also, actively track counterparty risk from interbank and corporate lending, while monitoring liquidity risk arising from major depositors or borrowers— in real-time.

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Map systemic contagion to expose hidden structural vulnerabilities

Transform complex direct and indirect exposures into a dynamic, ecosystem-wide knowledge graph. By visualizing these interconnected systems, supervisors can proactively pinpoint hidden "super-spreaders" and structural weaknesses caused by counterparty risk and institutional coupling.

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Visualize and communicate the cascading impacts of market shocks

Instantly model how the sudden failure of a major institution or a common market stressor will propagate through the financial system. This enables regulatory teams to visually quantify knock-on effects and clearly communicate systemic threats to policymakers during crises.

Gain unprecedented insights from granular supervisory data

Get in touch with the team to learn more about how FNA can help your organization dynamically monitor contagion and automate complex regulatory analytics.

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The platform visualizes knock-on effects in financial networks from common shocks and individual institution failures, making them visible, easily interpreted, and communicated to your stakeholders.
Yes. The platform uses machine learning models to map the financial system based on common exposures (e.g., in lending, borrowing, and investment portfolios) and structural similarity, enabling authorities to monitor potential contagion arising from overlapping portfolios and institutional coupling.
No, it is specifically designed to augment aggregated reporting information with vast amounts of granular supervisory data, granting you unprecedented insights into your jurisdiction's financial ecosystem.