LA INTERPRETACIÓN QUE PROPONGO EN ESTE CASO COMO CAUSAL PARA PLANTEAR LA ANULACIÓN DEL LAUDO

There are five different main causes in which current Fintech lenders use Big Data driven credit scoring processes. There is also a certain amount of overlap in some organizations that pursue multiple goals. These are the following.

 Improving financial inclusion in emerging countries  (Re)financing student loans

 Providing payday loans

 Providing peer-to-peer lending

 Providing consumer credit scoring service

Full financial inclusion is one of the main drivers of the projects that use alternative sources of data instead of the traditional credit history and financial capacity tests. Providing formal access to financial services to the whole world population is a challenge which is not directly relevant to NeoBank in the Netherlands. The near whole of the Dutch population has access to financial services based on their credit records. Exceptions to this rule are expatriates and very young adults that make use of financial products that require extensive screening. Applications that requires similar data and fuel the investments in Big Data are (1) determining client receptivity towards marketing offers and (2) the capacity utilization rate of financial products. This results in offering personalized financial products, targeted marketing and aligned communications. It is expected that in order to achieve this, the risk department needs to enhance their collaboration with the marketing department. Front and back office need to work hand in hand to provide the backbone for client visible applications. The organization also needs to employ data specialists and invest in capable hardware for each department that can potentially generate significant value.

The refinancing of student loans in the Netherlands is out of the question as there is no market for this. The government offers student loans at superior low rates and there is no incentive to refinance as students are given a maximum of 35 years (previously 15) to pay back the loans at a constant low rate. However, financing students themselves is an interesting market in order to bind loyal customers to the firm. Various Fintech firms are now offering financial products while taking the potential creditworthiness of clients into consideration through Big Data analytics. Students and young adults are part of the Millennial consumer population. This indicates that in general they make use of new technology and generate a lot of alternative data while they lack formal credit history in comparison with full grown adults. However, the students are unlikely to apply for loans outside the governmental lending system. If they do, the chances are high that their limit has been reached and that it considers a high-risk individual.

Page 49 of 95 The annual percentage rate of payday loans is much higher than other types of consumer credits, second only to pawn loans. Consumers usually contract a payday loan to pay for unexpected emergency expenses in a convenient manner. The loan is then paid off in the short term. The Fintech firms that grant these type of loans use automatized decision making systems to approve loans or reject loan applicants. These systems are needed because the underwriting process cannot cost the firm too much money. The decision systems do not necessarily take alternative data variables into consideration but some firms do make use of Big Data to optimize their decision algorithms. Most firms require you to fill out multiple online forms on employment, monthly expenses and personal details. Each country has a different application process due to the difference in legislation and standard data which is available to the organization through partnerships or public governmental data. In general, payday loans (“flitskredieten”) are much less regulated and require less screening from companies. On these types of loans, there is different legislation than say a personal loan or other credit lines due to the nature and characteristics of the credit.

Peer-to-peer (P2P) lending is a constant feedback loop of multiple parties that make use of the exchange platform. Consumers, business owners and investors meet their offer and demand on this platform. Nearly all P2P lenders in the U.S. require borrowers to use a FICO credit score from a credit scoring agency to express their creditworthiness. Most P2P lending platforms in the U.S. work together with one of the large credit scoring agency. In other countries, P2P lenders focus mainly on bringing investors and small business owners together. They require the borrowers to upload a questionnaire, digital forms, personal credit history, ratings or credit scores to prove their creditworthiness. Additionally, these firms verify client identity by requesting digital documents such as ID card, salary slips, employer’s declaration, bank statements and so forth. Based on the eventual calculated credit score, the borrower is placed in a risk category. The investor decides if the risk taken by investing is acceptable, bigger risks yield higher interests. Regulation and legislation on peer-to-peer lending are currently nearly nonexistent in the Netherlands, but heavily monitored by the AFM. There are a few P2P lenders active in the Dutch market, some of them have a small segment in which they carry these activities out next to their main operations in crowdfunding. Examples are Sameningeld B.V. (Mortgages), Geldvoorelkaar.nl, Lendico and Lendex. Big Data based credit scoring service providers collaborate with financial institutions in their endeavor to reduce risk of default and score consumers accurately. The bank would contract these firms to improve the current credit scoring processes by enhancing them with alternative sources of data. These service providers offer cloud-based solutions to improve the underwriting process. “Common” alternative sources used by different branches are mobile data, social media data and utility payment data. However, public governmental data, data procured from statistics offices and third party data brokers are also used. Traditional credit scoring uses dozens of data variables and focuses on mapping a client’s ability to repay while Big Data credit scoring uses thousands of data variables and maps the behavior of clients too. One of the main questions the industry asks themselves is how to package and sell accurate creditworthiness to consumers. There is a need to convince the clients to allow access to their data for mutual benefit. In order to do so, consumers must see the added value in ceding this information. Bankers must oblige themselves to practice ethically in their process of determining accurate creditworthiness. If not done correctly and effectively, the control over money lending will be contested and eventually usurped by organizations drawing from other sources of authority. The government and financial authorities need to regulate “bad loans” and make sure clients are able to escape negative spirals. The objective of Fintech firms must be to guarantee customers better service through personalized loans with proper counseling. All the while business in the financial sector must be conducted with responsibility.

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