of users from smart apps (Jenkins, 2014;Dwoskin, 2015;Lohr, 2015). In fact, Wei et al.
(2016) and Kshetri (2016) show that Big Data enables creditworthiness assessment of
potential borrowers with limited ﬁnancial history and thereby increases access to ﬁnancial
services, particularlyfor low-income borrowers and micro-enterprises.
Yet, usage of Big Data and associated algorithms raise concerns on the enforcement and
adequacy of regulations that aim to prevent discriminatory scoring to protect consumers’
rights to question their scores and consumers’privacy via regulations such as US Fair
Credit Reporting Act, Equal Credit OpportunityAct, Fair and Accurate Credit Transactions
Act (2003) and Privacy Guidelines of Organisation for Economic Co-operation and
Development (OECD) (Campbell-Verduyn et al., 2017). These algorithms are criticized for
being “black boxes”due to their opacity for producing arbitrary results and for furthering
discrimination (Citron and Pasquale, 2014). Big Data also poses challenges to privacy and
security of personal information as revealed by the recent Equifax data breach, where
approximately 143 million Americans’personal data were stolen by hackers. In a recent
statement Senator Mark Warner, the Senate Cybersecurity Caucus co-founder, called the
breach “a real threat to the economic security of Americans”and mentioned the need to
rethink data protectionpolicies”(Mathews, 2017).
The interplay between ﬁnance, technology and regulation is not new. The development of
information and communication technologies has contributed to ﬁnancial innovation and
globalization of ﬁnancial services, accompanied with deregulations and re-regulations over time
(Cerny, 1994). In fact, Perez (2009,2013) discuss that technology revolutions create major
technology bubbles during the transition to the new paradigm. However, once the bubble
collapses, a golden age could be unleashed if the ﬁnancial system is restructured accordingly and
institutional governance and regulations are adequately developed. Big Data is now
revolutionizing how ﬁnancial services, particularly credit scoring, are created and delivered. The
very actors harnessing these new credit scoring technologies that use Big Data are banks, credit
bureaus, ﬁntech companies and other non-bank ﬁnancial service providers such as telecom
companies. While Big Data may enable these actors to develop more accurate algorithms to
assess creditworthiness, predict failure and develop tailored pricing and products/services, it, at
the same time, brings challenges regarding data privacy and security as in the example of
Equifax. However, there is a research-practice gap as the academic research in this ﬁeld is scarce.
Accordingly, our study is motivated by the on-going developments regarding new data sources,
technologies and regulations in the credit scoring ﬁeld.
Our objective is to gain a better understanding of the main themes of credit scoring as
they relate to technological change and associated regulations over time. Accordingly, we
conducted a content analysis of “credit scoring”across Proquest and Emerald research
databases over the past 41 years (1976-2017). Content analysis is a systematic review of
literature to make valid inferences about texts for knowledge building (Weber, 1990;
Finfgeld-Connett, 2014). Accordingly, we reviewed 258 articles that appeared in peer-
reviewed 147 different academic journals from 1976 to 2017, ranging from law journals to
ﬁnancial services, computer engineering and operations research journals. Our main
RQ1. What are the main research themesin credit scoring literature?
RQ2. What is the directionand progression of credit scoring themes over time?
RQ3. What is the relative proportionof application, behavior and other scoring types?
RQ4. What types of statisticaltechniques and models are used in credit scoring?