Can your personality get you a credit score?

Maha Khan

Imagine you have just completed a job and are owed money. Your client offers you a delayed payment option where instead of receiving $14,000 today, you will receive $20,000 in six months. Which option do you take?

This is one among many behavioral and personality questions that a psychometric credit assessment asks potential borrowers. By asking questions that measure an applicant’s attitude, integrity, and performance, a psychometric credit assessment can generate a credit score. And, because everyone has a unique personality and characteristics, this type of assessment provides an alternative for thin-file loan applicants (i.e., zero or low credit history) seeking to obtain loans.

Beginning in 2006, innovative firms like Lenddo and Entrepreneurial Finance Lab (EFL) — later the merged company LenddoEFL — were among the first to pioneer psychometric assessments for lending in emerging markets. In early 2018, FiDA spoke with LenddoEFL to better understand their journey in developing psychometric assessments, and, in parallel, learn more about the experience of Juhudi Kilimo (JKL), a Kenyan microfinance institution, in employing the assessment. FiDA’s case study, “Delving into human consciousness: using psychometric assessments in financial services,” offers relevant experiences using psychometric assessments to financial service providers (FSPs) interested in leveraging non-traditional, alternative data to develop credit scores.

The opportunities and challenges of psychometric assessments

We were told EFL could accurately measure character so it was a test, and if it worked then we could start placing more emphasis on people’s character versus collateral.


JKL wanted to decrease their turnaround time to make credit decisions, and improve their acceptance rate. In 2016, supported by a Mastercard Foundation (MCF) grant, they turned to LenddoEFL and piloted their credit-score model, to explore an objective way of measuring a loan applicant’s character. As a result of the pilot, JKL improved their acceptance rate by 5% and increased the maximum loan amount available from 67% of collateral to 100% of collateral for high-scoring individuals. Moreover, new ‘high-scoring’ clients received, on average, $40 more (the average loan size is $300) than before the LenddoEFL model was implemented and were also offered access to clean energy loans.

However, JKL had to make the following structural, operational, and technical changes over the course of seven months before implementing this technology:

  • Obtain senior management buy-in
  • Recruit staff
  • Revise loan policy
  • Build infrastructure
  • Train test administrators and loan officers
  • Sensitize loan applicants to the technology

Much as JKL had to transform some components of their operations to implement LenddoEFL’s credit score, EFL (pre-merger), went through their own journey to develop the credit-score tool, outlined in detail in the case study. For example, LenddoEFL learned that developing a psychometric assessment tool requires iterations of data-driven models, customizing the test for the target audience, and complementing the model with multiple data sources.

Profitability requires scale and FSPs should have a clear use case for the technology

A key lesson that LenddoEFL learned is that reaching profitability requires scale. For the moment, LenddoEFL charges clients an integration fee, a one time scorecard fee (i.e., building a customized model for the client), and a price per score with a minimum number of scores purchased per month. The price per score decreases as volume increases and thereafter they charge a recurring fee that begins after the scorecard is built. The FiDA case study outlines the fixed and variable costs that FSPs and/or FinTechs should consider in building their own psychometric assessments; for instance, up to three teams to (a) model the test, (b) deliver the test, and c) integrate the test into the core banking system.

Both JKL and LenddoEFL have learned that FSPs must be very clear on what problem psychometric credit scoring could potentially solve. JKL notes that FSPs should, ideally, first assess their in-house credit appraisal system to understand (1) how effective current tools are in assessing an individual’s creditworthiness and (2) identify any gaps that psychometric assessments could fill.

Lastly, JKL encourages FSPs to conduct a cost-benefit analysis because a return on investment depends on the volume reached/scale of the program. Likewise, LenddoEFL recommends that FSPs plan to lend to at least 10,000 loan applicants a year in order to maximize the value of their tool.

FiDA is confident that the journeys presented in this case study provide a critical perspective on both the challenges and benefits of psychometric assessments in financial services.