CREDIT SCORING FOR SME - Radix Analytics Pvt Ltd

Small and medium enterprises (SME) play a major role in the economies of developing countries.
However, about half of formal SMEs do not have access to formal credit. Access to finance is the key constraint on SME growth compared to large firms.
To address this, the World Bank has taken the initiative to support innovative finance and improved credit infrastructure.
This has resulted in generating increased level of fund for SMEs in the last decade.

In the context of growth in SME financing, there is a greater need on the part of lending institutions to mitigate risk.
SME credit analysis produces a score indicating the probability of default of the borrowing entity.
Having a credit rating not only helps SMEs go beyond obtaining a loan but also provides leverage to negotiate with suppliers for procuring durables, equipment, and raw materials.

A Bank in Indonesia had a newly acquired SME portfolio.
The scoring model would be used for SME loan origination decisions
This was a key component in scaling up operations in accordance with Indonesian government directives.

Solution

The history of default was not well established with sparse data since the SME portfolio was new.
The bootstrap method was used to overcome the limitation of a small sample.
Business risk, financial risk, and moral risk were considered in the model.
Reject-inference was successfully employed.
High model performance achieved

Objective

Benefits

A lender in the UK finances a lease of office equipment to SME
Rapid depreciation of leased assets does not make them a good choice for collateral.
The lender used to cherry-pick customers who seldom went bad.
The finance company wanted to expand its customer base while mitigating risk.

Solution

Various default criteria were tested.
Use of bureau data representing the status of SME (liquidation, insolvency, dissolution) to construct the default.
Analyzed the types of SME business groups and the quality of dealers.
Separate scorecards for full-account companies and micro entities
Checked the quality and coverage of two different bureau data to assess the indeterminates (Not high or low score so no clear indication of Good or Bad)

Objective

Benefits

Data-driven model replaced rule-based model
Reduce underwrite workload by restricting scrutiny to fewer applications which get mid-level scores and not extreme scores
Separate scorecard for micro entities ensured these entities are not penalized for lack of accounts data

A multi-finance company in Indonesia provided loans to SMEs.
The company did not have a fully developed data warehouse and loan origination system for SMEs.
The bank had a policy of strict data security, whereby no data was to leave their server for analytics work.

Solution

The data processing and modelling exercises were accomplished in-house with proprietary software for credit scoring.
Separate scorecards were developed for four portfolios
In the absence of a core banking system, webservice was created to input data and receive instant score.
A database was created to store newly entered application data.

Objectives

Multiple scorecards for various portfolios
Link the scorecard to the core banking system to produce an instant score at the time of application.

Benefits

Instant scoring of new applications improves efficiency in decision-making.
Process automation ensured consistency, decreased manual work, and increased accuracy
Monitoring of score performance from interactive reports generated from customized software