The Banking Executive Magazine - Issue 150, June 2021

The Challenge of Big Tech Finance These entities now do virtually every- thing related to finance. Amazon ex- tends loans to small and medium-size businesses. Kakao of- fers the full range of banking serv- ices. Alibaba’s Ant Financial and Tencent’s WeChat provide a cornu- copia of financial products, having expanded so rapidly that they re- cently became targets of a Chinese government crackdown. The challenges for regulators are ob- vious. Where a single company channels payments for the majority of a country’s population, as does M- Pesa in Kenya, for example, its fail- ure could crash the entire economy. Regulators must therefore pay close attention to operational risks. They must worry about the protection of customer data – not just financial data but also other personal data to which Big Tech companies are privy. Moreover, the Big Tech firms, be- cause of their ability to harvest and analyze data on consumer prefer- ences, have an enhanced ability to target their customers’ behavioral bi- ases. If those biases cause some bor- rowers to take on excessive risk, Big Tech will have little reason to care if it is merely providing technology and expertise to a partner bank. This moral hazard is why Chinese regula- tors now require the country’s Big Techs to use their own balance sheets to fund 30% of any loan ex- tended via co-lending partnerships. Governments also have laws and regulations to prevent providers of fi- nancial products from discriminating on the basis of race, gender, ethnic- ity, and religion. The challenge here is distinguishing between price dis- crimination based on group charac- teristics and price discrimination based on risk. Traditionally, regulators require credit providers to list the variables that form the basis for lending deci- sions so that the regulators can deter- mine whether the variables include prohibited group characteristics. And they require lenders to specify the weights attached to the variables so that they can establish whether lend- ing decisions are uncorrelated with ethnic or racial characteristics once conditioned on those other meas- ures. But as Big Tech companies’ ar- tificial intelligence-based algorithms replace loan officers, the variables and weights will be changing contin- uously with the arrival of new data points. It’s not obvious that regulators can keep up. In algorithmic processes, moreover, the source of bias can vary. The data used to train the algorithm may be biased. Alternatively, the training it- self may be biased, with the AI algo- rithm “learning” to use the data in biased ways. Given the black-box nature of algorithmic processes, the location of the problem is rarely clear. Finally, there are risks to competi- tion. Banks and fintechs rely on cloud computing services operated by the Big Tech firms, rendering them dependent on their most formidable competitors. Big Techs can also cross-subsidize their financial busi- nesses, which are only a small part of what they do. By providing a range of interlocking services, they can prevent their customers from switching providers Regulators have responded with open banking rules requiring finan- cial firms to share their customer data with third parties when cus- tomers consent. They have author- ized the use of application programming interfaces that allow third-party providers to plug directly into financial websites to obtain cus- tomer data. the BANKING EXECUTIVE 36 ISSUE 150 JUNE 2021