빅테크의 금융업 진출에 따른 경쟁구도 재편에 대한 이론 분석: 대출시장의 경쟁과 정보생산을 중심으로

초록

This paper analyzes the competition in loan markets where bigtechs and financial intermediaries coexist, by combining the models of Karapetyan and Stacescu (2013) and Hauswald and Marquez (2006). Bigtechs are equipped with hard information such as big data and financial intermediaries are mainly dependent on soft information. The specific model is as follows. There are N banks and bigtechs in the economy. They are competing in a circular loan market where countless borrowing companies exist. A bank obtains information about a company by forming a relationship with it, and a bigtech obtains information about a counterparty company in the course of general commerce. The model consists of 3 stages. In Stage 1, banks enter in the credit market. It is assumed that each bank has to pay a fixed cost to enter the market and that the entrants are equally spaced. In Stage 2, when companies apply for loans, the bank produces information about these companies. In Stage 3, companies select a loan contract by comparing the loan conditions of various banks or bigtechs. The main results from the theoretical analysis are as follows. (i) In the case where bigtechs or data brokers supply big data to banks instead of directly participating in the loan market, banks' incentives to invest in soft information production will increase. Banks’ additional investment will improve signal accuracy. More accurate information about each borrowing company is produced thanks to big data analysis, and profits from each company increase accordingly, but fixed costs are spent to purchase big data instead of paying for each company. Consequently, borrowing opportunities can increase for non-financial companies, and the efficiency of resource allocation in the economy can be improved. (ii) If big data is supplied to financial companies, the number of entrants in the credit market will increase, intensifying competition in the market. The current model excludes the case where only some banks purchase big data, and assumes only two cases, whether all banks purchase big data together or not. Accordingly, if the cost of big data purchase is greater than the sum of the benefits from big data purchase, banks' collective decision-making is not to purchase big data at the same time. In the end, it can be seen that the number of banks participating in the loan market always increases when big data is purchased. The welfare of the whole economy can increase as a result of the increased competition in the loan market by the provision of big data. (iii) When bigtechs armed with big data directly compete with financial intermediaries that rely on soft information, the greater the informational value of big data, the smaller the market share of banks. If there are N banks in the loan market, bigtechs can erode the loan market if the accuracy of the signal given from big data satisfies certain conditions. In this situation, an equilibrium is possible where bigtechs and financial intermediaries, such as small banks or cooperative financial companies, coexist.

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