# 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**
1. Divide the variable into bins (e.g. deciles or business-defined ranges).
2. For each bin, calculate:
* Share of good borrowers
* Share of bad borrowers
3. Compute Weight of Evidence (WoE).\
4. Aggregate the bin-level contributions to obtain IV.\
**Interpretation guidelines**
| IV Range | Interpretation |
| ----------- | ----------------------- |
| < 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)**
| Gini Range | Interpretation |
| ------------- | ----------------------- |
| 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
---
# Agent Instructions: Querying This Documentation
If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question.
Perform an HTTP GET request on the current page URL with the `ask` query parameter:
```
GET https://docs.lenderlink.ph/backtesting-guides/back-testing-process/lenderlink-data-predictive-power-assessment.md?ask=
```
The question should be specific, self-contained, and written in natural language.
The response will contain a direct answer to the question and relevant excerpts and sources from the documentation.
Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.