The hedge fund manager and former Goldman Sachs partner was addressing concerns about subprime mortgage loans by a bank he formerly ran when the name came up. "The most troubling loan was actually to the Octomom and we worked very, very hard... to move her to another home," Mnuchin said while addressing the practice of offering subprime loans. And here is the moment a Trump cabinet nominee referenced Octomom at his confirmation hearing. In a matter of moments, the moniker "Octomom" became a nationally trending topic on Twitter. The bank in question was OneWest, which Mnuchin formed to purchase what was left of subprime lender Indy Mac from the Federal Deposit Insurance Corporation in 2009 following the country's massive financial crisis, CNN reported last month.
The idea of banking has drastically changed. But this change is based on a small device in your pocket, your smartphone. This piece of technology has had a profound effect on how consumers have helped to drive innovation in banking like never before. The ways in which consumers manage their finances has changed beyond all recognition. How often do you drop into a branch to pay in money, withdraw cash, re-negotiate your current or savings account, mortgage, insurance and loan agreements?
In the area of credit risk analytics, current Bankruptcy Prediction Models (BPMs) struggle with (a) the availability of comprehensive and real-world data sets and (b) the presence of extreme class imbalance in the data (i.e., very few samples for the minority class) that degrades the performance of the prediction model. Moreover, little research has compared the relative performance of well-known BPM's on public datasets addressing the class imbalance problem. In this work, we apply eight classes of well-known BPMs, as suggested by a review of decades of literature, on a new public dataset named Freddie Mac Single-Family Loan-Level Dataset with resampling (i.e., adding synthetic minority samples) of the minority class to tackle class imbalance. Additionally, we apply some recent AI techniques (e.g., tree-based ensemble techniques) that demonstrate potentially better results on models trained with resampled data. In addition, from the analysis of 19 years (1999-2017) of data, we discover that models behave differently when presented with sudden changes in the economy (e.g., a global financial crisis) resulting in abrupt fluctuations in the national default rate. In summary, this study should aid practitioners/researchers in determining the appropriate model with respect to data that contains a class imbalance and various economic stages.
Computational intelligence in finance has been a very popular topic for both academia and financial industry in the last few decades. Numerous studies have been published resulting in various models. Meanwhile, within the Machine Learning (ML) field, Deep Learning (DL) started getting a lot of attention recently, mostly due to its outperformance over the classical models. Lots of different implementations of DL exist today, and the broad interest is continuing. Finance is one particular area where DL models started getting traction, however, the playfield is wide open, a lot of research opportunities still exist. In this paper, we tried to provide a state-of-the-art snapshot of the developed DL models for financial applications, as of today. We not only categorized the works according to their intended subfield in finance but also analyzed them based on their DL models. In addition, we also aimed at identifying possible future implementations and highlighted the pathway for the ongoing research within the field.
Facebook's foray into crypto has made the firm few friends in political circles. The Libra backlash has been so bad that it has painted bitcoin and the rest of the crypto industry in a bad light. This week US senators have ramped up their rhetoric against internet monopoly and its lofty banking ambitions. A high ranking member of the Senate Banking Committee has lashed out at Libra again this week when he compared it to the subprime mortgage crisis that caused the 2008 global recession. According to Yahoo Finance, Senator Sherrod Brown pulled no punches in his opening remarks during the assembly.