Use Of Psychometrics And Biometrics For Underwriting To Reduce NPAs Availability of credit is a major ingredient in economic success of individuals, corporations and nations. It is therefore a sin that such a large proportion of human population is being deprived of the very ingredient that can take them out of poverty

Over 2 billions adults globally do not have access to formal and affordable lending. While 515 million people have opened a bank account in the last six years, availability of credit still remains low to this group. The main reasons why people are considered credit "unworthy' by traditional banks and lending companies is because they have low or no banking transaction data to establish earning, which helps to determine "capacity' to repay. The one other big reason is that such individuals do not have past credit history (CIBIL score), which helps establish both capacity and character. The use of financial behaviour using banking transactions and credit history are very effective underwriting tools, where the data exists.

Availability of credit is a major ingredient in economic success of individuals, corporations and nations. It is therefore a sin that such a large proportion of human population is being deprived of the very ingredient that can take them out of poverty. When people come out of poverty, they can contribute a lot more to success and GDP of their nations and the world.

It is incumbent upon financial institutions to create mechanisms for underwriting excluded individuals rather than focussing only on established individuals. Though it is difficult, there are several ways of underwriting excluded individuals. The main thing is to identify and use data points that help understand an individual's ability and intention to repay.

Data shows that nearly two-thirds of unbanked people globally have mobile phones. Use of mobile phones and digital technology can be an effective way to reach and understand about financially excluded individuals. Many organizations have tried to extract data about users from their phones to understand more about them. The data used includes social media posts, messaging data, contact lists, applications used etc. While this type of data does help understand individuals, there are several gaps. Not everyone uses applications or social media extensively and in many households, several members share mobile devices. Therefore, a more effective method needs to be established to collect data about specific users which is sufficient to establish their ability and intent.

Two very effective ways of determining intention is the use of psychometrics and biometrics. Both these techniques help build a holistic profile of an individual using their behaviours. These techniques help go beyond financial behaviours, rather focussing on human behaviour which has a direct correlation to financial behaviour. This is similar to the days when loans were approved by banks based on interviews and intuition of bank managers. While the concept is that same, the delivery mechanism using digital media and use of artificial intelligence driven algorithms cam make the approach faster, consistent and truly effective eventually helping reduce the risk of non-performance (NPAs).

Psychometrics data can help establish financial behaviour with very good accuracy. The main idea is to establish personality traits that have a direct correlation to financial behaviour. Several approaches can be taken to do this. One good method is situation judgment test, where users are given hypothetical situations to determine their personality. An example of a situation may be you ask your friend to lend you some money and he gives you half of what you need. How will you react to this? Be grateful, be happy, be sad or be angry. The response selected can understand your personality. Another example is to create a game to climb levels. A user that opts for a difficult skill level to jump two levels up has a very different personality to someone that opts an easy skill level go move up one level at a time. Effectiveness of Psychometrics is well established in personality determination. Research showing correlation between personality and financial behaviour is also well established. By bringing these techniques in to the mainstream and making the experience user friendly through digital media, financially excluded individuals can be underwritten in a very effective manner.

Biometrics are already in use for several purposes. Unlocking a mobile device using face matching or thumb impression are a way of using biometrics for authentication. In India, use od thumb impression to consent use of Aadhar data is another way where biometrics are used. Several digital lending applications now use videos and face matching to authenticate identity and liveness. However, for the purpose of underwriting loans to excluded individuals a m lot more needs to be done. Consider a lie detector or a polygraph. It used pulse rate, blood pressure and sometimes change I body temperature to determine if a person is being honest. Newer research shows that pupil dilation and eye blinking can also establish honesty. Such tests can be used to underwrite loans in a user friendly manner. Imagine an individual looking at their phone while answering simple questions like the purpose of the loan they need, for how long, a bit about themselves and their families etc. Nothing that is not generally asked during a loan application. While answering such questions if a mobile device can capture their eye and pupil movement, check their pulse rate and body temperature. Collection of such data is with consent and user friendly, since this is done on a mobile device, the user is fully in control of what is being said, when and where. Such data collected in the background can help understand a parson's honestly and intention. Algorithms can be developed to establish clear correlation between the data collected and financial behaviour.

The above are two examples of how financially excluded individuals can be underwritten. Several other approaches can also be adopted. For this to happen, large financial institutions need to prioritise these methods either organically of through partnerships. Doing this will open up several opportunities for financially excluded individuals and for the financial institutions as well by enlarging their customer base and portfolio. The second order effects on economic growth and development will be the pudding on top. The approaches mentioned above may seem intrusive. But done in a proper manner, with proper privacy, consent and data security can prove to be very powerful. These techniques can solve very parge problems and most importantly, can lead to economic upliftment of millions of people around the globe.