Banks have long been suffering from the pain points of traditional ALM system.
ALM has been traditionally a high-latency and low- frequency practice for most banks (or enterprises), mainly due to large volumes of data involved and computational complexity of attribution analysis, indicator computation or cross-indicator analytics.
Traditional ALM systems built on top of RDBMS, big-data or lake-house frameworks share a common pitfall – cartesian product due to the joining(s) of multiple tables. When multiple tables must be joined during the attribution analysis (or back-tracing, simulation, stress testing, etc.) process, these ALM systems tend to run extremely slow, making it impossible to penetrate or correlate data for value extraction. This widely shared common pitfall on the IT infrastructure level has seriously bogged down any enterprise’s capability to drill down on their valuable ALM data.
Basel III mandates a full spectrum of regulatory requirements that banks are having hard times coping with. Without end-to-end solution, banks are risking having difficult times managing their infrastructure, datasets, reporting mechanisms in a unified, timely and effective way.
Ultipa offers next-gen ALM/BI killer app solution that empowers banks, financial institutions and enterprise customers to have 360-degree overview of their assets and liabilities, and be proactive with their financial planning, pricing, budgeting, ROI, liquidity risk and indicator analysis, predication and management, including but not limited to deposits, loans, RWA, EVA, RAROC, LCR, Internet Rate, NII/NIM, capital, AUM, FTP, leverage-ratio and any other Basel-III compliant ALM indicators.
Powered with real-time graph computing, Ultipa ALM can be done with significant performance gain and augmented intelligence. The table below shows the main differences between Ultipa Graph ALM and traditional ALM systems:
|ALM Comparison Matrix||Ultipa Graph ALM||Traditional ALM Systems|
|Typical Latency||<2s||T+1 or longer|
|IT Architecture||Graph Computing||RDBMS/Big-data or Lakehouse|
|Data Granularity||Finest, single deal/trx||Aggregated|
|# of Indicators||2,000||<|
|Development Procedure||No-code||SQL + Excel|
|Explainability (White-Box)||Yes||No (Black-box)|
|Attribution/Contribution Analysis||Yes, real-time||No, or T+N|
|Back-tracing||Yes, real-time||No, or T+N|
|Stress-Testing/Simulation||Yes, real-time||No, or T+N|
|Cross-Indicator Analysis||Yes||No (impossible to implement)|
The diagram below illustrates why traditional RDBMS-centric IT infrastructure can’t handle ALM, because the sheer computing complexity of any attribution/contribution analysis prevents RDBMS from generating meaningful results in a timely manner:
RDBMS/DW vs. Graph DB in ALM data analytics
Overall system architecture of graph-powered ALM platform
Ultipa Graph ALM system is capable of managing all Basel III mandated factors, including, but not limited to:
Ultipa Graph ALM System with 3D highly interactive web GUI
Ultipa ALM offers off-the-shelf end-to-end solution to our bank customers to help comb through their infrastructure, data, and reporting mechanism, and fill in the gaps in between with our real- time, graph database empowered, white-box, highly visualized and highly intuitive solution. It goes way beyond the basic needs of regulatory compliance, but also satisfying business growth needs via deep correlation and 360-degree quantifiable insights of your crucial ALM data.