The Importance of Financial Inclusion in Credit Scoring Decisions
“The test of our progress is not whether we add more to the abundance of those who have much; it is whether we provide enough for those who have too little.”- Franklin D Roosevelt
In a world where the majority of the population remains unbanked, the expansion of financial services to all sections of society is of utmost importance to achieve inclusive growth and development. As of 2014, around 38% of the adult world population does not hold an account with a formal financial institution and the situation is even worse in developing countries.
Financial Inclusion - Engine of Economic Development
Financial inclusion has been widely recognized as one of the most important engines of economic development. It boosts economic growth by facilitating business creation and expansion, especially for small and medium enterprises. Its impact on social welfare reaches even further as it enables the poorest and most vulnerable in society to step out of poverty, thereby reducing income inequality in society.
Over the year, various governments and global institutions such as World Bank and IMF have taken significant steps towards achieving this objective and a certain level of optimism is warranted as the statistics have improved considerably. However, one key area where the principles of financial inclusion have historically been ignored is the credit scoring methodology used by banks.
Behaviour Credit Scoring
Most of the currently used credit scoring methods rely on the credit history of the user and aim to segregate the population into groups of ‘defaulters’ and ‘non-defaulters’. However, recent literature on the subject of credit scoring seems to suggest that this might be an oversimplification of the problem. This is especially pernicious for the millions of people who lack adequate financial history.
Behavioral credit scoring can be one viable solution to this predicament. It involves taking a holistic view of the personal attributes of the applicant to determine their eligibility for the loan they request. Behavioral credit scoring uses psychometrics to gain insight into an individual's overall thought processes and behavior patterns which helps in assessing their likelihood of repaying the loan. It takes into account factors such as attitude toward risk-taking, self-discipline, dependability and emotional stability.
As behavioral factors have traditionally been ignored in the credit scoring methods, behavioral credit scoring methods can be used in conjunction with the traditional methods to achieve a more holistic attitude towards lending.
Static nature of traditional credit scoring methods
Another factor plaguing the efficiency of the current credit scoring methodology is the static nature of these systems. It falls trap to one of the most common logical fallacy of believing that the past data is a true representation of future expectations. The explosion in the field of big data analysis and machine learning can help the banks to create a dynamic credit scoring model which makes use of sophisticated statistical methods such as scenario analysis and Monte Carlo simulations.
Rather than simply predicting the probability of binary outcomes namely ‘default’ and ‘non-default’, these methods help to simulate a range of borrowers states that can occur over a period of time. It is particularly helpful for analyzing a prospective borrower who has never had exposure to cheap finance. Both behavioral and dynamic credit scoring models rely heavily on the use of non-financial data, known as alternative data which makes use of information such as utility bills, telecommunication bills, rental payments and even social media footprints.
Non-financial data, more readily available
Since these data points are much more readily available as compared to the traditional financial data, it significantly increases the potential customer base for the banks. Many innovative technology firms have now to come to the fore who use highly predictive technologies and algorithms to generate insights from these data points and can assist the banks in making better decisions.
The application of behavioral and dynamic credit scoring models coupled with the use of alternative data can dramatically increase the number of individuals that are considered eligible for credit as they enable the lenders to see a complete picture of an applicant’s creditworthiness. Not only does the improved credit scoring method facilitate financial inclusion, they are hugely beneficial to the banks themselves. It helps them to accumulate a large amount of deposit funding from an increased customer base, which can help them to reduce funding costs and risks, and thereby become more stable.
In this era of intense competition, the banks can gain a significant edge by making use of this additional information and opening up to new avenues of revenue growth. Getting a deeper insight into an applicant’s profile can also help them in reducing the default rate. By achieving the dual objective of financial inclusion and increased profitability for banks, the use of these new models of credit scoring can be a huge boost to the economic development and the overall well-being of society.