Traditional LRM solutions built on top of RDBMS (such as Oracle DBMS) has 4 major pain points:
Large commercial and retail banks have tons of daily transactions, computing liquidity indicators like LCR takes long time, usually in T+1 manner, which poses as hurdle for more frequent system health checking, scenario simulation, or stress testing, and defers the decision-making process.
Without the capability of backtracing, banks will NOT know what factors contribute most to the changes of LCR, and what the paths of impact are. Without such information, it would be impossible for banks to gain clarity into their core assets and liabilities therefore affecting profitability.
Traditional RDBMS based LCR solutions calculate LCR indicator in a black-box fashion without any deep diving capability to explore any factors that offer in-depth understanding of a bank’s flowing liquidity risks and implications to its assets and liability management.
Not being able to simulate in real-time, for instance, how good or bad the situations would go when certain enterprise customers, industries, branches, or bank accounts have situational changes.
To achieve intraday LCR or even real-time business intelligence with LCR, you have to explore disruptive technologies like real-time graph computing. Ultipa LRM system has successfully helped a leading commercial and retail bank build its liquidity risk management graph system which won the "Achievement in Liquidity Risk Management” award in 2021 by The Asian Banker Award, it’s a worldwide first showcase of using graph technology to explore and manage liquidity risk with unprecedented speed, flexibility, and user-experience.
The below diagram shows that Ultipa Graph DB (the core engine that powers the LRM system) has on average 10,000x performance gain over Oracle’s relational DBMS. In other Asset Liability Management scenarios, similar performance gains are repeatedly achieved.
Oracle LRM vs. Ultipa LRM: Performance gain by 10,000x
Ultipa LRM allows user to drill down on the composition of daily liquidity indicator, such as LCR, to the finest granularity (per deal, per account, per customer, per segment, per branching center, ...).
|Liquidity Risk Management (LCR)||Ultipa LRM||Oracle LRM|
|Cluster Size (Instances)||4 (HTAP)||Unknown|
|LCR Computing Time||≤2 Seconds||≥3.5 Hrs (12,600 seconds)|
Graph computing offers great explainability. This is reflected in the way how data are aggregated and drilled down during the graph computing process underpinning any attribution analysis or scenario simulation. The transmitting paths shown on the dashboard visualizes everything in a white-box and highly explainable way.
Unlike traditional SQL based stress testing or scenario simulation process, Ultipa LRM offers real-time simulation, backtesting and stress testing with unlimited number of scenarios (combination of that many factors) made possible with the help of Ultipa Graph DBMS computing power and GQL’s unprecedented flexibility.
Ultipa offers end-to-end LRM solutions by leveraging Ultipa Server’s superior graph computing power and Ultipa Manager’s 3D visualization and highly interactive and business-personnel friendly dashboard, it’s a true one-of-a-kind killer app for banks to stay on top of their valuable assets and liabilities while satisfying domestic and international regulatory needs without hassles.
Ultipa LRM – Real-time LCR Management
Ultipa LRM empowers modern banks to go real-time with their liquidity risk management and monitoring so that not only to meet regulatory requirements but also to strengthen internal controls, boost productivities and achieve improved profitability. Previous LRM solutions like Oracle are slow, headless, black-box and without much attribution analysis capability, with Ultipa LRM, you get a lightning fast, fully interactive dashboard with white-box explainability and real-time attribution/contribution analysis all by your fingertips with celerity and unprecedented flexibility.