Question

1. How big data supports Fintech credit scoring? Will that raise any ethical problems? Any law enforcement on that? (1 mark)
2. Comparing the emerging of derivatives innovations decade years ago and Fintech, are there any similarities, dissimilarities? Do you think Fintech will replace derivatives’ role, or it will complete derivatives’ role? (1 mark) Please search case study “Orange County Bankruptcy” in Google. First, do some self-learning, and then write an essay to give me some directions on:
3. Who is Robert Citron? (1 mark)
4. What was the Orange County Governance Structure? (1 mark)
5. What was the political and economic background before the bankruptcy? (1 mark)
6. What was the Orange County Investment Pool and balance sheet (1994)? (1 mark)

Answer 1

Question 1:

New credit scoring models used by Fintech Companies use advanced technology which allows them to collect and use large amounts of data and analyse the same for effective decision making. These modesl alos rely upon machine learning techniques and big data to mine non linear information from variables, unlike the traditional linear models.

The AI based credit scoring models have generally outperformed the traditional models in predicting the loans' defaults and losses.

Since the machine based credit analysis is totally dependent on the system and does not take into account the subjectivity or biases of an individual credit analyst, hence in a way, these can prove to be more efficient in analysing whether a particular credit proposal is worth consideration.

However, owing to issues like data security and transparency of clients' confidential data, AI can prove to be quite risky and hence pose a threat to data security. Thus, if data security is hampered, then it can prove to be unethical.

Thus, several IT laws are in force which ensure such confidentiality is maintained and that the clients's data is in safe custody.