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BSFI

Predicting Delinquencies of a Personal Loan Portfolio Ahead of Time

“The team deeply understands financial services and is highly accessible for guiding APAC on the platform's technology and features.”

Nikhil Bandi
CTO, APAC Financial Services private Limited

75%+ accuracy in predicting loan delinquencies

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75% to 82%

overall model accuracy

False Positive

rate of 2% to 6%.

False Negative

rate of 12% to 15%.

OOS Score

of 84%, indicating strong model performance.

Why this Global Bank Loves nanoBI

  • Automated data acquisition and reconciliation.
  • Machine learning-driven matching and validation.
  • Self-serve analytics for better insights.
  • Reduction in manual processing time and costs.

75% to 82%

overall model accuracy.

False Positive

rate of 2% to 6%.

False Negative

rate of 12% to 15%.

OOS Score

of 84%, indicating strong model performance.

Loved it? Ready to try nanoBI?

Book a Demo

Why this Fintech Loves nanoBI

  • An early warning system for delinquency prediction helps this fintech take timely mitigation strategies.
  • Uses macro and external variables for better accuracy.
  • Predictive insights for proactive risk mitigation.
  • Extensive variable library with 90+ factors for accurate scoring.

About the client

A new-age fintech company operating in the BFSI sector in India, managing a diverse personal loan portfolio. The client focuses on data-driven risk assessment and aims to enhance loan performance by predicting delinquencies well in advance.

Challenges Before nanoBI

  • No clear visibility into which loans might show stress 6 to 9 months ahead.
  • Existing models only used account-related variables, limiting predictive power.
  • Needed a better tagging system to classify delinquency risk and take timely action.

nanoBI Solution

  • nanoBI’s Early Warning Solution was implemented and tuned to meet the specific segment portfolio.
  • The solution scores all live accounts in the specified portfolio every month of their propensity to default or be delinquent over the coming six to nine months​.
  • The Early Warning Solution uses macro or external variables other than account or internal variables​.
  • 32+ variables (internal and external) were used to predict early warning delinquencies (the solution has a library of 90+ variables).​

“The team deeply understands financial services and is highly accessible for guiding APAC on the platform's technology and features.”

Nikhil Bandi
CTO, APAC Financial Services private Limited

How Things Have Changed

  1. nanoBI’s Early Warning Solution has helped the client achieve an overall model accuracy of 75% to 82%, significantly improving delinquency predictions.
  2. The false positive rate was kept as low as 2% to 6%, ensuring minimal incorrect flags, while the false negative rate remained within 12% to 15%, balancing risk prediction.
  3. The model’s OOS (Out-of-Sample) score of 84% further demonstrated its robustness in handling real-world scenarios.
  4. With early tagging of delinquent accounts, the client can now proactively apply mitigation strategies, reducing risk exposure and improving loan performance.

75%+ accuracy in predicting loan delinquencies

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