Global Leader in Electronic Trading & High-Frequency Trading Systems | Hands-On Expertise & Executive Leadership in Market Infrastructure
My top 5 reasons why financial firms are still using batch systems in their Risk Management Systems—and what they must do to fix it 👇 Many trading firms and financial institutions still rely on batching systems to calculate risk. But why? It’s not because they don’t see the limitations—it’s due to complex legacy systems and the challenges of upgrading. Here’s why they’re stuck with batching, and what they can do to change it: 1️⃣ Legacy Systems Are Too Deeply Embedded Many firms have legacy systems that are so linked to operations that replacing them seems impossible without disrupting the whole business. 💡 Add Real-Time Layers on Top of Your Existing System Instead of overhauling everything, add modular real-time layers to your current system. This lets you move some functions to real-time processing without breaking the system. Over time, you can transition more areas without major disruptions. 2️⃣ Poor Connectivity Between Internal and External Systems Firms struggle to connect their risk management systems with market data or internal tools like OMS/EMS. This makes real-time updates difficult. 💡 Use APIs for Real-Time Data Integration Replace batch data with API-driven integrations to get real-time data from market feeds and internal systems. This keeps your risk team updated and enables quicker reactions to market changes. 3️⃣ Fear of Disruption and High Costs Switching to a real-time system seems expensive and risky, and firms worry about operational interruptions. 💡 Take a Phased Approach Start by moving critical risk functions like volatility-sensitive calculations or real-time exposure monitoring to real-time. This reduces disruption and shows the value of the shift. Gradually, expand real-time processing to other areas. 4️⃣ Overwhelmed by Too Much Data The sheer volume of data—market data, trades, and external feeds—can overwhelm batch systems designed for simpler data flows. 💡 Use Stream Processing Stream processing allows your system to handle data continuously, keeping your risk models updated in real-time and helping you stay on top of fast-moving markets. 5️⃣ Performance Bottlenecks During High-Volume Trading Batch systems may handle regular trading, but they slow down when trade volumes spike, leading to delays in risk calculations. 💡 Use Adaptive Scaling for Real-Time Risk To avoid slowdowns during market volatility, use adaptive scaling with cloud or distributed systems. This helps your RMS adjust to high volumes, keeping risk calculations fast and accurate even during market stress. If you have other interesting ideas, I’d love to hear them and discuss more. #RiskManagement #risk #RMS #financial #markets
Market Microstructure based Risk and Investment Management spanning all major global asset classes - Equity, Equity Index and FX Derivatives, Fixed Income and Commodity Markets.
1moWhen it comes to Risk Management, financial firms are a very broad categorization. The problem is different for different parts of the industry. In Banking and Insurance they are regulated by the Basel and Solvency regimes respectively. Here the legacy systems go back to the initiation of the Basel II/Solvency II era, and Points 1 and 2 that you have identified dominate. Even if Tech- Ops recommend upgrades, Risk Management itself will move to prevent it, because it is hard- almost impossible to dislodge sytems that have enabled the bank to clear regulatory audits in the past. Point no. 4 and 5 dominate on the Buy Side, where Risk Control dominates. Point 3 is common across all, but for different reasons.