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Risk Modeling Scorecard

Outcome

Pinjam Modal

January 24, 2022 | Jonathan Pryer

How Pinjam Modal Boosted Profits and Reduced its Bad Rate by 60%

  • More accurate assessments of credit worthiness
  • Increased profiles and reduced losses, with a minimized bad rate
  • Able to access the right data, at the right time – with both traditional and alternative data available on demand

“The guidance and documentation provided by Provenir is top notch. They shared detailed documentation on how the model is built and met with us regularly to share their deep knowledge in developing risk decisioning models.”

Ichwan Peryana, Co-founder & CTO at Pinjam Modal

The Challenge

Pinjam Modal was looking to automate the entire channel and remove all manual processes, with the goal to reduce its bad rate while also boosting sales. The online lending platform was looking for a robust and accurate modeling algorithm that would be capable of more accurately differentiating good loan applicants from more risky ones.

Why Provenir

Pinjam Modal chose Provenir to create an end-to-end AI predictive scoring model, based on Provenir’s extensive experience and knowledge of the market. Many individuals in Indonesia are unbanked, making credit scoring using traditional bureau data difficult. Provenir proposed using both traditional data and alternative data including age, marital status, education, tenure of business, telco records and social media activity, in order to more accurately assess loan applicants. The partnership between the two companies will ensure that the AI models continue to accurately predict risk over time.

60% Reduction in Bad Rate

In implementing Provenir AI and its custom risk modeling scorecard, Pinjam Modal was able to reduce its bad rate by 60%, minimizing loss and increasing overall profits. The company plans to expand its loan offerings to a larger market in the future, with full confidence now in its decisioning capabilities.

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