Fintech Research papers

Paper #1 - Credit risk prediction in an imbalanced social lending environment

Key Notes

• Credit risk prediction is an effective way of evaluating whether a potential borrower will repay a loan
• Borrowers benefit from lower interest rates; lenders receive a higher return than they would from a bank
• Class imbalance is a common problem in loan default prediction
• The under-sampling approach includes random under-sampling (RUS), and instance hardness threshold(IHT) algorithms
• For over-sampling approach, random over-sampling (ROS), synthetic minority over-sampling technique (SMOTE), and adaptive synthetic sampling (ADASYN) are studied
• publicly available datasets released by the Lending Club, a well-known P2P lending platform(lendingclub.com)

Paper #2 - Machine Learning in FinanceEmerging Trends and Challenges 

Key Notes

• inevitable trust deficit in deploying them in critical and privacy-sensitive applications, the so-called “black-box” nature of such models
• Risk modeling - operational risk management, compliance, and fraud management  
• Portfolio management: The portfolios are designed based on the recommendations of smart algorithms that optimize various parameters with return and risk being the two most important ones
• Algorithmic trading: Algorithmic trading exploits the use of algorithms to carry out stock trading in an autonomous manner with the minimal human intervention
• Fraud detection and analysis: Fraud detection and analysis is one of the most critical machine learning applications in the finance industry
• Financial chatbots

Paper #3 - Recommendation Engine for Lower Interest Borrowing on Peer to Peer Lending (P2PL) Platform

Key Notes

• a recommendation framework for borrowers to help them borrow with lower interest rates
• Bidding loan: first and foremost, borrowers themselves decide the maximum interest rate they are willing to pay.
• machine learning models to classify if a given borrower will succeed on the bidding loan platform
• machine learning models to predict the interest rate payable for bidding and traditional loans

• contains 12,006 loans (both funded and nonfunded loans) with 12 features and 2 response variables — the borrower’s interest rate and the status of the bidding loan

• Predicting the success rate of funding bidding loans

Paper #4 - Determinants of Interest Rates in the P2P Consumer Lending Market: How Rational are Investors?

Key Notes

• The (1) loan-specific view analyzes elements such as loan volume and the loan period by investigating the effects of these elements on the interest rate for P2P consumer loans
• (2) borrower-specific factors focus on aspects that affect a borrower's credit rating

Paper #4 - Deep Learning for Financial Applications : A Survey

Key Notes

Paper #5 - MACHINE LEARNING ALGORITHMS FOR FINANCIAL ASSET PRICE FORECASTING

Investment professionals often refer to this non traditional data as “alternative data" [12]. Examples of alternative data include the following:

• Satellite imagery to monitor economic activity. Example applications: Analysis of spatial car park traffic to aid the forecasting of sales and future cash flows of commercial retailers. Classifying the movement of shipment containers and oil spills for commodity price forecasting [13]. Forecasting real estate price directly from satellite imagery [14].
• Social-media data streams to forecast equity prices [15], [16] and potential company acquisitions [17].
• E-commerce and credit card transaction data [18] to forecast retail stock prices [19].
• ML algorithms for patent analysis to support the prediction of Merger and Acquisitions (M&A)

Capital Asset Pricing Model (CAPM)

The CAPM holds the following main assumptions:

• One-period investment model: All investors invest over the same one-period time horizon.
• Risk averse investors: This assumption was initially developed by Markovitz and asserts that all investors are
• rational and risk averse actors in the sense that when choosing between financial portfolios investors aim to optimize the following:
• (a) Minimize the variance of the portfolio returns.
• (b) Maximize the expected returns given the variance.
• Zero transaction costs: There are no taxes or transactional costs.
• Homogenous information: All investors have homogenous views and information regarding the probability distributions of all security returns.

• In the context of financial asset price forecasting the information processing problem we are trying to solve is the prediction of an asset price t time steps in the future - we are effectively trying to solve a non-linear multivariate
• time series problem

Paper #6

FinBrain: When Finance Meets AI 2.0

More Reads

• From banks to DeFi: the evolution of the lending market
• Buy Now, Pay Later (BNPL) ...On Your Credit Card∗
• Data science and AI in FinTech: An overview

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