Credit risk and lending
Retail and commercial lending teams need a consistent view of a borrower across applications, existing facilities, repayment behavior, and collateral. Separate extracts and conflicting definitions make it difficult to explain an exposure, assess a changing risk profile, or reproduce the evidence behind a lending recommendation.
The challenge behind the workflow.
Retail and commercial lending teams need a consistent view of a borrower across applications, existing facilities, repayment behavior, and collateral. Separate extracts and conflicting definitions make it difficult to explain an exposure, assess a changing risk profile, or reproduce the evidence behind a lending recommendation.
Connect customer, account, transaction, and finance data so operations and risk teams can work from consistent definitions. These workflows address fragmented systems, time-sensitive exceptions, and decisions that need a clear evidence trail.
Data to bring together
- Borrower applications and financial statements
- Loan exposures, repayment histories, and credit reference data
- Collateral valuations and approved credit policies
Source systems are examples of the data required. Confirm connector availability, permitted access, refresh timing, and the authoritative owner before implementation.
How Genedata supports the work.
Configure one connected workflow, then reuse its mappings, definitions, and review process as the business grows.
- 01
Connect and prepare
Connect permitted borrower and facility records, align reporting dates and currencies, and validate missing income, duplicate obligations, and stale collateral values before analysis. Preserve the source and effective date of each value.
Build the preparation as a reusable visual workflow or reviewed code, with explicit source mappings and validation checks. - 02
Investigate and validate
Apply versioned, bank-approved calculations and risk indicators to the reconciled dataset. Compare exposure, affordability inputs, concentration, and repayment changes; send contradictory evidence and policy exceptions to a credit analyst with the supporting records.
Keep shared definitions and source versions with the analysis so another team can reproduce the result. - 03
Publish and review
Publish a credit review workspace containing the analytical results, assumptions, policy version, and reviewer decisions. Reuse the approved dataset for subsequent portfolio monitoring while retaining the original application evidence.
Make the output available to the right people with appropriate access, ownership, and review evidence.
A traceable credit risk and lending review pack
Make the next run easier.
Credit analysts can reuse governed borrower views and calculation logic instead of rebuilding spreadsheets for each application. Visual preparation workflows and traceable exceptions let data teams resolve quality issues once for both lending and portfolio analysis.
Start from one approved example. Save the agreed mappings and definitions, publish the reviewed output, and let the next team follow the same evidence instead of rebuilding the preparation.
Measure your own improvement
Compare preparation time per credit review, unresolved input exceptions, and time required to reproduce a recommendation against the pilot baseline.
Record a baseline before the pilot and compare like-for-like work afterward. Results depend on source quality, configuration, and adoption.Review and responsibility
Authorized credit teams validate model suitability, fairness, assumptions, and policy exceptions. A generated score or AI summary does not approve, price, or decline a loan.
AI with professional judgment.
Use AI to summarize evidence and explain exceptions. Credit, account restrictions, and financial-crime decisions remain subject to authorized review.
Try the workflow with your team.
Use representative, approved sample data. Agree the expected output and a review owner before extending the workflow to a live process.