Use Cases
Liquidity & ALM Risk
Have a proactive understanding of stress testing and liquidity metrics (LCR, NSFR, NII, EVE…), analyse cash flows and simulate ALM exposures more accurately.
Data diving in action
Flip the cards and see how we address these common pain points
Limited access to granularity & history
for cash flows and liquidity exposures
Instant access to 100% of your cash flows
No compromise between history and granularity
Fragmented data sets
from ‘black box’ legacy systems
Capacity to create
very large data sets or cross data set analysis
Complexity of data hierarchy
Organisations not consistent with “economical” monitoring
Flexibility to build
specific and flexible views different from the hierarchy
Limited capacity to calculate
and simulate regulatory ratios on the fly
Data versioning
with full audit trail and pre-built UDF regulatory calculations (LCR…)
Long customised process
for liquidity stress testing and metric enhancements
Integrated Python UDF
for self-service analytics, including pre-built regulatory calculations
Very high infrastructure and running costs
due to the data set size
Horizontal scalability
using commodity hardware on-premise and/or on Cloud
Understand more granularly
liquidity and ratios changes
Integrate quickly
from multiple sources at granular level
Produce and certify autonomously
IRRBB metrics (NII, EVE) and liquidity (LCR, NSFR)
Better forecast of liquidity
ratios with forward views and calculations
Optimise liquidity buffers
resulting in the reallocation of locked liquidity
Cut up to 90% infrastructure costs
while increasing historical ranges
Open and see our other use cases
We have solutions for you
Contact us and find out how we can help with your big data challenges.
Click here to ask for a demo.
