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October 30, 2021

AI in Finance

Paper #1 - AI in Finance: Challenges, Techniques and Opportunities

Key Notes

  • Key Areas are capital markets, trading, banking, insurance, leading/loan, investment, asset/wealth management, risk management, marketing, compliance and regulation, payment, contracting, auditing, accounting, financial infrastructure, blockchain, financial operations, financial services, financial security, and financial ethics
  • Classic techniques including logic, planning, knowledge representation, statistical modeling, mathematical modeling, optimization, autonomous systems, multiagent systems, expert systems
  • Modern techniques such as recent advances in representation learning, machine learning, optimization, data analytics, data mining and knowledge discovery, computational intelligence, event analysis, behavior informatics, social media/network analysis
  • Specific business problems, such as market trend forecasting, stock price prediction, credit scoring, fraud detection, financial report analysis, pricing and hedging, marketing, consumer behavior analysis, algorithmic trading, social commerce, and Internet finance.
  • Portfolio planning and optimization: including designing, planning, optimizing and recommending investment portfolios and strategies in a market
  • Forecasting and prediction: including the regression, classification, estimation and prediction of trend (up or down), movement (direction and scale, etc.), value (e.g., price or volatility)
  • Business profiling: including describing, segmenting, characterizing and classifying markets, products, customers, and services.
  • Sentiment and intention modeling: including characterizing, representing, modeling, analyzing and evaluating the polarity, diversity, propensity and their dynamics of customer sentiment and intention 
  • Anomaly detection: such as characterizing, quantifying, detecting, classifying and predicting abnormal, exceptional and changing behaviors, products, patterns, performance





Paper #2 - Enhancing Financial Inclusion using Mobile Phone Data and Social Network Analytics

Key Notes

  • Datasets - call-detail records, credit and debit account information of customers is used to create scorecards for credit card applicants
  • Call-detail records are used to build call networks and advanced social network analytics techniques are applied to propagate influence from prior defaulters throughout the network to produce influence scores
  • predictive model for a target measure of interest (e.g., churn, fraud, default) 
  • sociodemographic  information, such as age, marital status and postcode; debit account activity, including timing and amount of payments; and credit card activity
  • sociodemographic features such as age, marital status and residency as reported at the time of the credit card application are extracted.





Paper - P2P LOAN ACCEPTANCE AND DEFAULT PREDICTION WITH ARTIFICIAL INTELLIGENCE

Key Notes

Features for the first phase are: 

  • debt to Income ratio (of the applicant); 
  • employment length (of the applicant); 
  • loan amount (of the loan currently requested); 
  • purpose for which the loan is taken
  • loan amount (of the loan currently requested); 
  • term (of the loan currently requested); 
  • instalment (of the loan currently requested); 
  • employment length (of the applicant);
  • home ownership (of the applicant. Rented, owned or owned with a mortgage on the property); 
  • verification status of the income or income source (of the applicant. If this was verified by the Lending Club); 
  • purpose for which the loan is taken; 
  • Debt to Income ratio (of the applicant); 
  • earliest credit line in the record (of the applicant); 
  • number of open credit lines (in applicant’s credit file); 
  • number of derogatory public records (of the applicant);
  • revolving line utilisation rate (the amount of credit the borrower is using relative to all available revolving credit);
  • total number of credit lines (in applicant’s credit file); 
  • number of mortgage credit lines (in applicant’s credit file); 
  • number of bankruptcies (in the applicant’s public record); 
  • logarithm of the applicant’s annual income (the logarithm was taken for scaling purposes); 
  • FICO score (of the applicant); 
  • logarithm of total credit revolving balance (of the applicant).

Paper #3 - Behavior Revealed in Mobile Phone Usage Predicts Credit Repayment

Key Notes

  • Mobile phone transaction history prior to the extension of credit, and whether the credit was repaid on time
  • Transition to a postpaid plan
  • Call and SMS metadata

Paper #4 - Data Science in Economics

Key Notes







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