Psychometric testing could help boost financial inclusion by providing an alternative method of predicting the willingness to repay among borrowers with no credit history, according to Daniel Schydlowsky of the Alliance for Financial Inclusion.

Innovation is not a term usually associated with financial regulation, but when it comes to financial inclusion, innovation is a driving factor and one regulators constantly take into consideration. In September 2015, members of the Alliance of Financial Inclusion will meet in Maputo, Mozambique, for the annual Global Policy Forum where innovation will be a key part of the agenda.

But what policy innovations are on the horizon that could impact upon the future of banking? One that could make a difference to financial access is psychometrics. 

Closer than ever

Financial inclusion means bringing the unbanked on to both sides of banks' balance sheets: making them depositors as well as users of bank credit. An essential part of the effort consists of bringing the banks to where the unbanked are, either geographically or sociologically. The great innovation of the 21st century in this regard is mobile banking. Almost everyone has a mobile phone these days, so essentially everyone can have access to a bank at their fingertips. Any geographical distance from the bank is thus overcome by electronics. 

Sociological distance is another matter, however; users must still be comfortable using a bank. That will most likely take a little longer. But it will happen fairly quickly, certainly if the younger generation leads the way.

A different challenge confronts the banks (and their regulators) in converting the unbanked into responsible users of credit. A core principle of lending is to know your client; and make sure that he or she will be willing and able to repay. How is a bank supposed to assess the creditworthiness of an unbanked individual for whom no credit history is available by definition? Microcredit lenders have confronted this problem for several decades and have developed techniques to ferret out the likely repayment behaviour of new clients.

However, these techniques are costly: they require making extensive enquiries among the business and social relations of potential clients, in order to accumulate information on their reputations. And not only is such a technique costly and time consuming, it is also hard to standardise. The information gathered will depend on the care and effort devoted to gathering it, and also on the sheer availability of such information. Hence, by its nature it will be relatively uncertain. As a result, lending to the unbanked is more risky and will require a higher interest rate to compensate, at least until information on repayment behaviour is accumulated. This is inherently inefficient and inequitable. If only it was possible to leapfrog to an indicator of the integrity of an unbanked individual...

The psychometric measure

It turns out that just such an indicator indeed exists. It is possible to develop a psychometric measure of an individual's characterological structure and hence of his or her disposition to repay a loan. Note that repayment depends on a willingness to pay and the ability to so do. But all things being equal, willingness to pay will predict repayment behaviour. A psychometric integrity measure, therefore, will do for the unbanked the same thing a credit score does for the long-time user of the banking system.

But how can such information be found? To find it, the unbanked sit for 20 or 30 minutes in front of a computer in order to answer a dynamically structured set of questions. Then an integrity score is computed. An inexpensive basis for a lending decision is thus created.

Why would a psychometric integrity measure work for lending decisions? It is not really surprising: hiring decisions have been made for many years on the basis of psychometric tests. What companies look for in employees is essentially integrity, even if it goes under different names, such as reliability, loyalty or good judgement. If it works for a hiring decision, why should it not work for a lending decision?

Some ramifications of applying psychometrics to lending can readily be anticipated. One route leads to assessing the qualifications of an applicant for a microcredit loan. Here a lender wants to know not only about the integrity but also the suitability to operate as a businessman. Another route leads to consumer lending. Here a lender just wants to know whether the loan extended to buy an item will be repaid. A third route leads to setting credit limits on credit cards. A typical strategy of credit card issuers is to be very liberal with the first tranche. But after that, criteria needs to be available to reduce risk on higher limits before the defaults are generated that provide a credit history.

This concept is intriguing enough that some initial experimentation is under way. International organisations such as the Inter-American Development Bank have already invested in small psychometric projects in Peru, while commercial banks are currently driving pilot initiatives across Africa and microfinance organisations have started to introduce the service in India.

Granted, all these projects are still small and remain on the fringes of financial inclusion, but they are there, they are growing and could one day become common practice. The potential of psychometrics is clear and its day will come: the unbanked need to be brought into the system and their potential realised. Psychometrics will help make this possible.

Daniel Schydlowsky is superintendent of banking, insurance and pensions in Peru, and chair of the steering committee of the Alliance for Financial Inclusion.


All fields are mandatory

The Banker is a service from the Financial Times. The Financial Times Ltd takes your privacy seriously.

Choose how you want us to contact you.

Invites and Offers from The Banker

Receive exclusive personalised event invitations, carefully curated offers and promotions from The Banker

For more information about how we use your data, please refer to our privacy and cookie policies.

Terms and conditions

Top 1000 2023

Request a demonstration to The Banker Database

Join our community

The Banker on Twitter