Moreover, an increase in the age of the household head decreases the chances of being financially vulnerable. Being married and living with the spouse is also negatively associated with the probability' of financial vulnerability. Our results also highlight that, even after controlling for traditional household and regional characteristics, there is clear evidence that an increase in the size of monthly debt repayment raises household’s financial vulnerability, and an increase in the net-worth of the household decreases financial vulnerability. Lastly, regional characteristics such as living in an urban locality do not seem to have an impact on households’ financial vulnerability once we take into account the net w'orth of the household. First, as far as we know, this is one of the earliest studies which empirically investigates the subject of household financial vulnerability using an unsupervised machine learning technique. Most of the existing analyses are based on aggregate data and cross country analysis which have some significant shortcomings due to their inability to account for differences in distributions (Christelis et al.,