In the era of digital economy, the development of technology is not only changing people’s way of life, but also promote different industries to become more digitalization. The combination of traditional industries and new technologies has created new business models, and this change are gradually changing the way people work. Therefore, in the era of the digital economy, people need to learn and understand new technologies more quickly and increase insight and thinking about business models in order to adapt to the rapid development of society (Forbes ,2016). The financial industry has always been regarded as an industry driven by technological development, and this article will discuss changes in credit approval in the digital economy era and its future development trends.
In the past, traditional credit approvals mainly relied on manual review. Credit examiners need to manually review decision-making materials such as application forms, ID cards, credit reports, and income verification provided by customers, as well as non-decision-making materials such as address information, and relatives information. After manual review, credit examiners sort and score loan applications according to their expert experience, and determine whether to pass or reject loan applications. In this business model, credit approval requires a large number of experienced business experts, and the credit risk management entirely depends on the limited expert experience of credit examiners. As the scale of the business increases, this business model often increases higher labor cost to financial institutions, extending the loan review cycle and resulting that financial institutions can not effectively manage credit risks.
In the digital economy era, with the development of technologies such as big data and machine learning, the credit approval process has become more intelligent and automated, and credit risks have become increasingly controllable. Big data technology has enabled credit risk management no longer to rely on manual review too much. In addition, due to the variety of data collection, the risk of information asymmetry is further reduced with the support of big data technology. In the automated credit approval process, financial institutions use facial recognition and electronic signature technology to quickly identify the authenticity of customer identity information. By matching the identification information that applicants provide with citizen identity information database administrated by government organization, financial institutions can do a further verification of the authenticity of loan applicants’ identity information to avoid fraud risk. After the verification of identity information, the credit decision engine system invokes external big data scoring module and embedded credit decision module to evaluate the credit status of the loan applicant and decide whether to approve or reject the loan application of the lender. Meanwhile, the system will automated calculate the credit line of the loan applicant based on the applicants’ credit status and the internal credit management strategy of the financial institution. Automated credit approval technology shortens the cycle of the whole credit application and makes credit decisions more consistent, reducing the risk of inconsistent credit approval caused by the expert experience of different credit examiners (Business Matters,2018). Meanwhile, faster credit approval process enables loan applicants to obtain better service experience, and thus enables financial institutions to attract more customers to increase the profit growth.
Big data technology enables financial institutions to better control credit risks and reduce the human cost of credit approval. This trend drives financial institutions to invest more on technology. Consequently, data scientists, IT engineers has become hot positions in the financial industry, while the amount of credit examiners has fallen sharply. And it seems that this trend will still maintain within next several years. However, it does not mean that credit risk management does not need the credit examiners‘ involvement. Their expert experience can support data scientists and IT engineers’ work. For example, they can help IT engineers to understand the business process and give practical Suggestions in terms of user experience. They can also help data scientists give their own expert advice on model design and validation, making credit score models more accurately predict the potential credit risk. Furthermore, They can choose to engage more valuable jobs, such as due diligence or compliance management. It is also possible for them to choose to become an technology expert with rich business experience by learning new techniques.
As Andrew Ng says in an interview (Stanford Business,2017),” AI and big data technology is transforming today’s society”. Although the current technology may still have shortcomings, the application of automated approval technology has become an irreversible trend. For credit examiners, they need to constantly learn new technologies and deepen their insight into the business, so that they can adapt to the variation of job requirement brought by new technology applications.
Sources:
Forbes (2016). 5 Ways Digital Technology Is Changing Your Job. Retrieved from: https://www.forbes.com/sites/joemckendrick/2016/12/29/5-ways-digital-technology-is-changing-your-job/#7d3d88e064bd
Business Matters (2018). How is technology improving the loan approval process? Retrieved from:https://www.bmmagazine.co.uk/business/how-is-technology-improving-the-loan-approval-process/
Stanford Business (2017). Andrew Ng: Why AI Is the New Electricity. Retrieved from https://www.gsb.stanford.edu/insights/andrew-ng-why-ai-new-electricity
Liu Tian Yi's Blog 
As this blog mentioned automated credit approval process, it actually has aroused my experience with a company called “Taikang Life Insurance” in China. The services I experienced were all accessible on mobile devices, such as facial recognition and electronic signature. For Big data technology, I am concerned that it may has negative impact on disclosure of private information. For instance, a serious data scandal of Facebook. In some developing countries, the legal system is not effective on data disclosure, and it may become worse. Similarly, I reckon that credit examiners will lose their jobs in the future due to big data technology has been improving and will be more functional. On the other hand, I am curious that if big data technology also can be used in other fields of businesses, as well as, is the process of credit approval completely digitalized or there is still something that big data technology cannot do, but credit examiners can do.
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Hello,Thank you for your feedback.I will answer questions of three aspects that you are concerned.
First,many countries has established policies to protect personal privacy.For examples,GDPR,Cyber Security Act in China, California Consumer Privacy Act in USA.I think the protection of data privacy needs joint effort of business organizations and government, and governments are trying to optimize laws to better protect citizens’ privacy.From techinical perspective,many companies have use encryption to protect personal privacy and avoid the leakage of customers’ personal sensitive imformation.The data shared between business organizations are usually labelled data,not original data.Therefore,methods demonstrated above can protect customers’ personal privacy to some extent.
Second,i do not think credit examiners will lose their jobs.I think that all of technologies are born to serve businesses.Therefore,there always needs professionals to assist technical experts’work,such as business validation and designing system modules.I do not think that the job of credit examiners will completely disappear in the future.But financial institutions do not need to hire a lot of credit examiners as before due to the wide application of automatic credit approval technology.Consequently,the amount of credit examiners will continously decrease during the following several years.
Third, in terms of loan amount approval,many organizations do not completely rely on the amount given by credit decision model.Sometimes,they consider how to balance the profit and risk management.That means they may give a higher or lower amount of loan than the loan amount that credit decision model give,which is the stage in the credit approval process that may need human intervention.Maybe there are other specific activities in the credit approval process that may need human intervention.It may need us to further explore.
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Hi, Tony. Many thanks to have your full answers back. Obviously, the roles of government can definitely be an effective approach in relation to solving privacy issues, such as giving punishment if any of the organisations breaking the law. Furthermore, I also can see how deep your understanding is in terms of finding a balance between credit examiners and technologies. Overall, I totally agree that there is always a place where credit examiners still can play an important role in the future. This is due to human beings are much more emotional and have most of soft skills that technologies do not have. Perhaps, the roles will partly be changed, but I think this would be a proper way of working with technologies within next decades.
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