Custom Financial Risk & Scenario Analysis Platform for Smarter Banking Decisions
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Glacier operated under the weight of fragmented financial workflows and manual spreadsheet-driven processes that hindered decision-making. The client required a unified, browser-based environment to consolidate risk management, ALM analysis, and portfolio optimization. Their primary objective was to eliminate version mismatches and manual errors, ensuring that enterprise finance teams could operate from a single, reliable, and secure source of truth.
WebMob delivered a custom, cloud-ready financial risk and scenario analysis platform that replaces siloed tools with a centralized, high-performance ecosystem. By integrating complex computational engines with a modular frontend, we enabled the client to automate liquidity, collateral, and market exposure workflows. This enterprise-grade solution provides the scalability required for future expansion while maintaining rigorous security and compliance standards.
The client struggled with disconnected financial workflows and manual data management that created significant operational risks and hindered cross-departmental visibility.
Fragmented Risk Workflows
Risk data was scattered across disparate tools, preventing a unified view of financial health and forcing teams to work from inconsistent information.
We engineered a unified platform that integrates all analytical workflows into a single data layer, providing a consistent financial picture across all functions.
Spreadsheet-Driven Processes
Reliance on manual spreadsheets led to frequent version mismatches, human error, and an inability to audit or trace the origin of critical financial assumptions.
We replaced manual files with a structured, version-controlled system that ensures all calculations are automated, auditable, and based on verified data.
Limited Visibility Across Teams
Different departments lacked a shared platform, making it nearly impossible to maintain consistent visibility or collaborate effectively on financial data.
We built role-specific dashboards that provide each team with the precise views they need, ensuring everyone works from the same data at the same time.
Complex Financial Calculations
The client required high-performance computation for liquidity, margin, and exposure metrics, which could not be reliably handled by existing manual tools.
We implemented a robust Python-based backend that handles complex financial modeling, delivering accurate, high-speed computations within a browser-based interface.
Security and Compliance Requirements
Handling sensitive financial data required stringent security measures that could not be retrofitted, posing a risk to institutional compliance and data integrity.
We built the platform with enterprise-grade security from the ground up, including tenant isolation, WAF protection, and AES-256-GCM encryption for total data safety.
We integrated organization-based single sign-on to verify users before access. This allows IT teams to maintain full control over credentials without managing separate logins for the platform.
We engineered a centralized stress-testing screen that replaces scattered spreadsheets. This enables the client to search, filter, and maintain version-controlled records for all financial scenarios.
We provided analysts with direct access to historical market data and financial instruments. This eliminates the need for external cross-referencing by offering detailed price charts and records.
We built a comparison tool for classical and quantum-assisted optimization outputs. This allows the client to evaluate volatility and Sharpe ratios alongside advanced efficient frontier charts.
We developed a tracking module for collateral-backed lending that monitors credit exposure and LTV. It helps the client identify risk concentrations across various industries and currencies.
We created a derivatives screen that displays net MTM and initial margin. This enables the client to understand exposure without manually building models from raw position data.
We engineered a tool to evaluate hedging strategies and their margin impact. It provides the client with a structured way to model strategies using payoff-at-expiry charts.
We designed a detailed data table that highlights movements in strike prices and Greeks. This offers the operational detail required for the client to summarize strategy-level performance.
We built an ALM module for treasury teams to assess liquidity and funding metrics. It allows the client to compare baseline and optimized views to support balance sheet decisions.
We implemented trend analysis tools to monitor assets and liabilities over time. This enables the client to identify patterns and flag potential issues before they escalate into risks.
Scenario Analysis Platform is built on a secure, cloud-native financial platform architecture using modern frontend frameworks, enterprise-grade authentication, financial computation engines, and scalable cloud infrastructure.
React
TypeScript
Vite
PostgreSQLThe client transitioned from manual, error-prone processes to a unified, high-performance environment that ensures data integrity and operational speed.
| Capabilities | Before | After |
|---|---|---|
| Data Consistency | Multiple versions of spreadsheets across teams. | Single, unified data layer for all users. |
| Risk Analysis | Manual calculations prone to human error. | Automated, high-speed computational engine. |
| Security | Bolted-on security with limited control. | Enterprise-grade, secure-by-design architecture. |
Understood the business goals, user needs, existing workflows, and technical requirements.
Defined the system architecture, technology stack, integrations, and data flow required for the solution.
Built the core platform in agile sprints, integrating APIs, backend services, frontend experiences.
Launched the solution, monitored performance, resolved initial issues, and ensured a smooth transition to production.
Validated functionality, performance, security, and usability before preparing the solution for production.
The platform provides the client with a scalable, secure foundation that accelerates decision-making and reduces operational risk.
Eliminates manual data entry and spreadsheet reconciliation, allowing teams to focus on high-value analysis.
Provides a single source of truth, ensuring all departments work from verified and consistent financial data.
Supports the addition of new modules and datasets without requiring a full rebuild of the existing system.
Protects sensitive financial workflows with enterprise-grade encryption and robust identity management.
Scenario Analysis Platform transformed complex financial risk analysis into a centralized, secure, and scalable digital platform for modern financial institutions. By bringing scenario management, referential market data, portfolio optimization, Lombard credit monitoring, derivatives margin analysis, option strategy evaluation, ALM optimization, and ALM trend analysis into one connected system, we helped the client improve decision-making, reduce manual effort, strengthen operational control, and build a future-ready foundation for financial innovation.
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We use a decoupled architecture where a high-performance Python backend handles heavy computations, while a responsive React frontend provides real-time data visualization and interaction.
Yes, we built the system using a modular service architecture. This allows for the seamless addition of new datasets and functional modules without disrupting existing workflows.
We implement a multi-layered security approach, including AES-256-GCM encryption, tenant-based data isolation, and a WAF with OWASP CRS to protect against vulnerabilities.
This project was delivered in three months. Our agile approach focuses on core functionality first, allowing for rapid deployment and iterative improvements based on business needs.
Yes, we utilize WSO2 Identity Server and OpenID Connect to enable single sign-on, allowing your IT team to manage user access through your existing internal infrastructure.
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