Use Cases
Credit Risk
Thanks to Opensee, analyse your Credit Portfolio exposures and relevant metrics (IFRS, Stress Tests, RWA…) and track misbookings to manage your risks more accurately.
Self-Service Analytics for Credit Risk Analysis
Flip the cards and see how we address these common pain points
Limited drill capabilities
due to lack of granularity and history
Instant access to 100% of your trades
No compromise between history and granularity
Manual certification process
Inefficient and high operational risk
Smart certification
Automated error detection and raw-level certification
Slow and complex impact analysis
on calculations and regulatory metrics (CVA, IFRS, RWA...)
Integrated Python UDF
for self-service analytics, including pre-built regulatory calculations
Segmentation of data sets
(Between Market Risk, Counterparty Credit Risk, Risk & Finance)
Single data environment
No size limit and a flexible data model enabling: a single source of truth, completeness, and cross data set queries
High infrastructure costs
to keep history and portfolio granularity
Horizontal scalability
using commodity hardware on-premise and/or on Cloud
Maintaining Data Integrity & Auditability
during production process
Data versioning
by the users with full audit trail
Key benefits Opensee delivers
Comply with BCBS 239
on metrics production and understanding
Save more than 50% on the certification process
on top of exhaustivity and quality
Cut up to 90% infrastructure costs
while increasing historical ranges
Better manage, anticipate
RWA and IFRS charges
Report on demand granularly and frequently
Achieve fast go to Markets
on key projects (e.g loan origination)
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.
