Liquidity & ALM Risk
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.

Self-service analytics for financial institutions

Detail from Opensee Tool

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

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