Company: LenddoEFL, a fintech company that provides credit scoring and verification solutions for emerging markets.

Challenge: LenddoEFL wanted to help financial institutions lend to underserved customers who lack formal credit history or identity documents. The company also wanted to reduce the cost and risk of lending by using AI to automate and improve the credit decision process.

Solution:

LenddoEFL used AI to develop a psychometric scoring model that uses behavioral and personality traits to assess the creditworthiness of potential borrowers. The model uses natural language processing (NLP) and machine learning to analyze the responses of applicants to a series of questions, such as:

• How do you plan your budget?
• How do you deal with stress?
• How do you handle conflicts?

The model then assigns a score to each applicant based on their answers, which reflects their likelihood of repaying a loan. The model also uses alternative data, such as social media activity, smartphone usage, and geolocation, to verify the identity and income of the applicants.

Outcome:

The AI-enhanced psychometric scoring model helped LenddoEFL provide access to credit to millions of people who were previously excluded from the formal financial system. Some of the benefits were:

• Increased financial inclusion: The model enabled financial institutions to reach new segments of customers who did not have traditional credit records or identity documents, such as low-income workers, students, and migrants.
• Reduced lending costs and risks: The model reduced the need for human intervention and manual verification, saving time and money on the credit decision process. The model also improved the accuracy and consistency of the credit assessment, reducing the default rate and the credit loss ratio.
• Enhanced customer experience and loyalty: The model provided a fast and convenient way for customers to apply for loans, without requiring any paperwork or collateral. The model also offered personalized feedback and advice to customers, based on their psychometric profile, to help them improve their financial literacy and behavior.

Conclusion:

This case study shows how a fintech company, like LenddoEFL, used AI to provide credit scoring and verification solutions for emerging markets. By using AI, LenddoEFL was able to help financial institutions lend to underserved customers who lacked formal credit history or identity documents, using behavioral and personality traits as alternative indicators of creditworthiness. LenddoEFL not only increased financial inclusion and reduced lending costs and risks, but also enhanced customer experience and loyalty by using AI to offer fast, convenient, and personalized credit services.

If you want to learn more about AI in finance, you can check out these articles:

• AI in Finance: Benefits, Real-World Use Cases, and Examples
• 10+ Generative AI Finance Use Cases in 2024
• Gartner Identifies 5 Top Use Cases for AI in Corporate Finance