LenderLink Data - Predictive Power Assessment
LenderLink Data – Predictive Power Assessment
Introduction
The purpose of this document is to help potential LenderLink clients assess the results from historical back-testing. By understanding the key evaluation metrics and the methodology behind them, clients can make informed decisions about integrating LenderLink data into their credit assessment processes.
Evaluating Results
Hit Rate
Definition Hit Rate represents the proportion of a client’s borrowers for whom LenderLink was able to retrieve at least one corresponding record from its database.
Calculation
Interpretation example If a client has 10,000 loans and LenderLink data is available for 6,000 of them, the hit rate is 60%. This means that risk signals can be evaluated for 60% of the portfolio.
Information Value (IV)
Definition Information Value (IV) measures the strength of the relationship between a single explanatory variable and a binary outcome (e.g. good vs. bad). It assesses how well a variable distinguishes between outcomes based on the distribution of good and bad borrowers across predefined bins.
IV is primarily used for variable assessment and early-stage screening, rather than for evaluating overall model performance.
How IV is calculated
Divide the variable into bins (e.g. deciles or business-defined ranges).
For each bin, calculate:
Share of good borrowers
Share of bad borrowers
Compute Weight of Evidence (WoE).
Aggregate the bin-level contributions to obtain IV.
Interpretation guidelines
< 0.02
Not predictive
0.02 – 0.10
Weak predictive power
0.10 – 0.30
Medium predictive power
> 0.30
Strong predictive power
Example When analyzing a variable such as Overdue Amount, higher overdue ranges often contain a disproportionately larger share of bad borrowers. This divergence between good and bad distributions leads to a higher IV, indicating that the variable is informative for credit risk assessment.
Practical note IV is a univariate measure and does not capture interaction effects, incremental contribution alongside existing model features, or monotonic risk ordering. Variables with a clear monotonic relationship to risk may therefore show relatively low IV despite being predictive.
Gini Coefficient
Definition The Gini coefficient measures how well a variable or model separates outcomes across a population. It quantifies discriminatory power by evaluating how consistently risk changes across ordered values.
Calculation approach
Interpretation (Philippines context, single variables)
0–5%
Low predictive power
5–25%
Medium predictive power
25% and above
High predictive power
Example Using the same Overdue Amount variable, the Gini coefficient captures how consistently default risk increases as overdue balances rise. Even when Information Value is moderate, a clear monotonic trend can result in a higher Gini, highlighting stronger discriminatory power.
Gini for scores The same concept applies to scoring models. Gini can be calculated on predicted scores to assess the overall discriminatory power of a model, including when combining LenderLink-derived features with an existing internal score.
Recommended Evaluation Approach
LenderLink data reflects borrower behavior across multiple loans and lenders and should be evaluated for incremental predictive value, rather than in isolation.
Overlay (Challenger) Model
Keep the approved production model unchanged
Build a challenger model combining the existing score with LenderLink features
Evaluate incremental lift using:
Change in Gini / KS
Bad rate at constant approval rate
Risk Segmentation
Select an approved population or a narrow score band
Use LenderLink data to further segment borrowers
Assess:
Bad rate monotonicity
Risk separation across segments
Live Proof of Concept (POC)
Run LenderLink in parallel with production decisions
No impact on approvals or declines
Analyze outcomes after a sufficient observation period
Key Metrics to Monitor
Change in Gini / KS
Bad rate reduction at constant approval
Approval uplift at constant bad rate
Segment-level risk separation
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