Opensee has been recognized by Chartis Research in their inaugural RiskTech AI 50 ranking and received a special award for best use of AI for risk data. These accolades highlight Opensee’s innovative approach to risk data management and analytics. In this blog post, we delve into the AI-assisted journey Opensee employs to enhance efficiency, ensure data integrity, and streamline decision-making processes in risk management.

The Foundation: Robust Data Management

Opensee's AI-assisted data journey begins with a comprehensive data management system designed to handle large volumes of raw data. This foundational step ensures that we have a repository of clean data, ready for complex analytics. By performing simple queries to ultra-fast aggregations, we prepare the data for further processing, ensuring its readiness for advanced analysis.

Ensuring Data Quality

Data quality is paramount in risk management, especially with regulations like BCBS 239 requiring data to be unique, complete, exhaustive, and consistent. Opensee utilizes advanced statistical tools and AI-driven anomaly detection to maintain high data quality standards. Our system quickly identifies irregularities, ensuring the integrity of incoming data and accelerating problem detection.

Data Adjustments with Predictive Analytics

Upon detecting anomalies, Opensee’s tools allow for data adjustments through manual or automatic correction from previous days' data. Leveraging AI, we use predictive analytics to recommend 'predicted' values, proposing risk values without a full revaluation. Neural network-based predictions estimate risks and outcomes based on historical data and current market conditions, enhancing the reliability of our dataset.

Data Certification: Ensuring Trust

Certifying the data is a critical step in maintaining trust. Once adjustments are made, Opensee flags reliable data that is ready for use in analytics. This certification process is crucial for ensuring that the data used in subsequent analyses is accurate and dependable.

Advanced Data Exploration

With clean and certified data, we proceed to data exploration. This involves slicing and dicing data, decomposition, drill-downs, and creating dashboards. Opensee’s Smart Driller tool, powered by AI, enhances manual searches, quickly identifying key dimensions (and combination of dimensions) that explain metric movements. Driver Analysis delves deeper into the factors behind these changes, providing profound insights.

Predictive Analytics and Scenario Testing

Predictive analytics is a cornerstone of Opensee’s AI journey. By running what-if scenarios and using neural network-based predictions, we estimate the outcomes of new trades or actions. A practical application involves identifying a problem, correcting it, and cross-referencing with outlier detection. For instance, a hedge fund required intraday/real-time VaR and PnL vector calculations. Using our platform, they achieved independence from traditional pricing proxies and enhanced their risk management.

Business Intelligence and Reporting using GenAI

Once the data is analyzed, it is visualized through BI reporting and dashboards. AI automation generates necessary reports, streamlining workflows and improving efficiency. Our Copilot feature revolutionizes data interaction, allowing users to ask questions in natural language, receive responses in the UI, and create reports seamlessly.

Example of a real-world application: Liquidity and Banking Book

By analyzing historical and real-time data on deposits, withdrawals, and loan disbursements, banks can forecast cash flows, identify potential liquidity gaps, and optimize their liquidity buffers.

As a Data Platform combining data management at scale and real-time analytics, Opensee offers data quality, data exploration, and versioning, which are crucial for regulatory compliance and auditability in banking book risk management. With our data versioning, Opensee enables comprehensive stress testing of the banking book, so users can define stress scenarios, run simulations, and analyze the impact on capital adequacy, liquidity, and profitability. This helps banks identify vulnerabilities and take proactive measures to mitigate risks.

This entire journey can be enhanced with our AI-assisted tools at various steps of the analysis.

Opensee’s AI-assisted data journey improves data integrity from the outset, accelerates problem detection, and enhances decision-making with advanced analytics and AI. By continuously improving data quality and leveraging cutting-edge tools, we transform raw data into valuable insights, driving better business outcomes.

To learn more, reach out to us here.