The term “3 Cs of credit” was popularised in the 1960s, but the principles behind the concept date back much further. The three C’s are Character, Capacity and Collateral, and today they remain a widely accepted framework for evaluating creditworthiness, used globally by banks, credit unions and lenders of all types.
The way each of these components is evaluated varies between countries and lenders. However, one of the 3 C’s is more open to interpretation than the other two. That is ‘Character’. The question of ‘character’ has been tied to lending for as long as people have borrowed because at its core lending is about trust. The way lenders quantify character has continued to change with technology and community expectations, the recent rise of new data types is fuelling further advances in character assessment.
Let’s look deeper into the 3 C’s and why lenders need to continually evolve their credit assessment processes.
Capacity
Capacity refers to an individual’s or organization’s ability to repay a loan. It includes factors such as income, expenses, and debt-to-income ratio. Lenders look at a borrower’s capacity to repay a loan to ensure that they will be able to make the required payments without defaulting. A borrower with a high income and low debt-to-income ratio will be considered to have good capacity, as they will have more money available to make their loan payments.
Collateral
Collateral refers to an asset that a borrower pledges to a lender as security for a loan. The collateral can be seized by the lender if the borrower defaults on the loan. Lenders look for collateral as a way to reduce their risk when lending money. The more valuable the collateral, the more likely a lender will approve a loan. Collateral can include anything from real estate to vehicles, jewellery, or other valuable possessions.
Character
The concept of character has been a component of credit evaluations since ancient times. Centuries ago, borrowers were evaluated based on their reputation and standing in the community. Lenders would rely on personal relationships with borrowers to assess their character and trustworthiness.
With the rise of the computer age, lending became increasingly digital, and a new era of credit scores was born, based on mathematical formulas.
Today many lenders continue to base Character on credit history, credit score, references and the judgement of a lending officer. However, it is now acknowledged that all of these sources are prone to inherent bias.
The current 3-digit credit score, which was launched in 1989, was originally promoted as a way to widen credit inclusion, but the reality is far from that. It actually reinforces the biases it was intended to address. Credit histories are often shaped by a range of systemic factors, including discrimination, poverty, and other forms of social and economic disadvantage. As a result, these factors can create significant barriers to fair credit access for marginalized groups, perpetuating existing patterns of bias and inequality. Exclusion from fair credit can force these groups to use loan sharks, paying exploitative interest rates and in turn reinforcing the barriers to credit fairness.
A reliable judge of character?
The inherent bias of existing solutions means lenders need to look further to promote diversity and inclusion in the lending industry. Some leading organisations are exploring ways alternative data can be used to promote mutually beneficial financial outcomes for lender and borrower.
Psychometric assessments are a way to create first-party behavioural data with the applicant to evaluate their character. These assessments use a series of questions or interactive games to create behavioural data linked to personality traits, such as conscientiousness, openness, and risk aversion. Lenders can then use this information to assess a borrower’s likelihood to repay a loan.
An added benefit of psychometric assessments is there is no need for personally identifiable data (PII). The process is entirely opt-in for applicants and has proven a successful way to include those who do not have a traditional financial history, such as young people, migrants or those returning to the workforce.
Another alternative data source gaining traction is device data assessment. Through an embedded process, the lender can collect permissioned metadata from a smartphone at the time of application. This non-personal data can be used to generate a behavioural credit score, which can be used in place of or in conjunction with traditional credit assessments.
Not only is this data more inclusive, but it has also been shown to be highly predictive of creditworthiness and stable over time.
Democratising Data
New data sources are playing a role in democratising who has access to services. As the bureau score was borne from the dawn of the computer age, we have now moved into the mass data age, where data science and machine learning are driving a new era of understanding when it comes to character and behavioural analytics.
Ultimately, the democratisation of data can promote more equitable and inclusive societies, as more people are empowered to participate in data-driven discussions and decisions. Giving borrowers the option to share data which may help them access a better lending outcome puts the power of data back in their hands and allows them to decide how and when to use their data to access the services that are important to them.
Character at the core
While the 3 Cs of credit will remain a useful framework, the ways they are evaluated needs to be continually questioned as lenders push for progress.
The mass data age has provided the tools to better understand borrowers and build better lending outcomes for all. People are diverse. Credit assessment should consider a diversity of data.
