Credit Risk
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)

We have solutions for you

Contact us and find out how we can help with your big data challenges.
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