Credit scoring is a statistical method employed by lenders to predict the probability an existing and/or prospective borrower or counterparty “will default on loans/credit products, or incur additional charges for repayment, also known as measuring creditworthiness,” the team at Juniper Research explains.
Juniper Research also mentioned in a report that the method is “a key tool in making credit affordable for consumers and businesses.”
The Juniper Research research report added that it achieves this “by linking credit products such as loans, insurance, credit cards, and mortgages, to risk, making these products secure, thereby connecting consumers to secondary capital markets and increasing the amount of
capital that is available to economies.”
In effect, this securing process is “a matter of risk predictability dependent on a number of factors, determined by not only financial indicators but also other publicly available information reported by credit bureaus.”
The report from Juniper Research further explained that in “its most basic form, a credit score can largely determine the approval rate of borrowers for credit and/or debt products, and their repayment terms.”
As stated in the update, credit scores allow individuals and businesses “to obtain more credit choices at competitive rates from lenders; streamlining application processes.”
They also extend “beyond their financial functionalities.” For instance, credit scores can be a factor in “completing rental arrangements successfully.” As such, credit scoring is also “a social consideration, especially for customer segments which are unbanked, underbanked and/or too young to credit, which are referred to as thin-credit file customers.”
It plays a key role in bridging “access to finance for these segments, and both credit bureaus and other providers have been placing great emphasis in developing scoring systems for non-mainstream customers.”
Such efforts reportedly involve integration of alternative data “which have been historically excluded from traditional scoring, and deemphasising certain indicators (ie, medical debt) and underlining some others to ensure credit can be meaningfully built.”
The report from Juniper Research further noted that these translate “into effectiveness for lenders to extend credit, and credit bureaus are continuously building better predictive capabilities that involve widening the datasets and employing ML and AI to underpin existing models and to refine them, as well as to develop new risk models.”
This is enabled by “better availability of data and data sources, and to an extent, regulatory mandates (ie, consumer credit acts, banking regulations for credit) allowing providers to achieve better accuracy and improve customer experience.”
The Juniper Research report further explained that the FICO score is “one of the best examples of a consumer credit scoring system which was first introduced in 1989 and since then, has been widely regarded as the default credit score in the US.”
Since then, there have been numerous developments “across different markets, especially in emerging ones, such as the rise of smartphones, eCommerce, alternative credit, and Open Banking.” Hence, credit bureau business models “cannot be static, and indeed there is a lot of progress being made to keep scoring systems relevant and inclusive in line with differing needs of consumers, businesses, and lenders.” For example, additional data sources such as Open Banking is now being leveraged “to grant read-only access to financial information to provide a better credit score and risk review.”
Various other data sources are also “employed to focus on the individual’s economic circumstances and creditworthiness.” Moreover, in tandem with these developments, the role of credit scoring has evolved “from a mere credit approval or rejection mechanism to a dynamic concept that contributes to the financial literacy and education of consumers.”
As noted in the report:
“Fraud and risk management are associated credit risk scoring avenues. Changes to credit scores and credit report activities potentially serve as early warning systems for both providers and lenders for fraud detection. Therefore, many credit scoring providers have branched out to offering fraud and identity verification services utilising the same underlying data. The same is true for KYC (Know Your Customer), KYB (Know Your Business), and AML (Anti-money Laundering) offerings as part of the solution portfolio and customer onboarding services, leveraging data collected originally for credit scoring purposes. Credit scoring, therefore, has morphed into an enabler from a set of numbers, unlocking multiple social and financial benefits for individuals and businesses alike. With the advent of AI and ML, as well as alternative data, scoring systems will continue their progress and try to navigate the challenges of a more open yet complex global financial system.”
The evolving use of AI in credit scoring is “intertwined with transparency and explainability.”
According to the OECD’s definition, explainability is “enabling people affected by the outcome of an AI system to understand how it was arrived at [which entails] providing easy-to -understand information to people affected by an AIxsystem’s outcome that can enable those adversely affected to challenge the outcome, notably – to the extent practicable – the factors and logic that led to an outcome.”
In addition, explainability also needs “to be actionable , which means that once an explanation is provided, individuals or businesses should be able to take an action to change their behaviour or remedy the wrong data about their behavior that is included in scoring models.”
This also holds true “for lenders which leverage AI -based models in risk and credit decisioning.”
As noted in the update, the lack of transparency and explainability, and therefore, accountability, can “lead to detrimental outcomes for consumers, and pose financial, as well as reputational risks to lenders.”
Linked to transparency and explainability, “the use of AI has been the subject of the much -debated bias issues, specifically when its claims to improving financial inclusion are considered.”
To achieve fairer outcomes at lending to pockets of sub-prime or thin-credit file consumers, these models “are increasingly being trained to eliminate discriminatory variables from the source data.” Although this does not entirely guarantee zero bias, “it can pave the way for lenders to actively revise or remodel their scoring systems.”
Juniper Research anticipates AI’s share in credit scoring model development “to grow exponentially, in line with the developments in generative AI.”
The report concluded that data protection and privacy “will be key considerations in moving forward, and human intervention, for instance data scientists within organizations, for ethical and responsible use of AI, will be needed for the foreseeable future.”