Socialist at RNC protests calls for disarming police, abolition of capitalism

| July 20, 2016

article image
Holding a sign reading “abolish capitalism” and “Black Lives Matter” and wearing a T-shirt promoting the candidacy of Workers World Party presidential candidate Monica Moorehead and her VP running mate Lamont Lilly, was Oklahoma City native and Bishop McGuinness High School graduate Gloria Rubec, a longtime anti-war and anti-death penalty activist from Texas, here on the streets of Cleveland.

Spotlight

Project Heather

Building a Scottish Stock Exchange for the 21st century. Join us in bringing impact to global capital markets and create liquidity, access and visibility for all.

OTHER ARTICLES

Optimizing Risk Management With Machine Learning In Banking

Article | May 26, 2021

The enormous amounts of data accessible to banks and their high demand for forecasting make the financial industry a perfect area for machine learning (ML) to shine. In this article, we explore the current applications of machine learning in banking when it comes to risk management, define its challenges and provide a future outlook. Credit Risk Management For the past few decades, banks have mostly used logistic and probit regression models for credit risk assessments and internal risk management. However, all conventional models inherit the same flaw — they predict outputs based only on linear relationships between input variables. This limitation was exposed in the catastrophic 2008 housing market crash. Although the crisis’s negative consequences have been multiplied by uncontrolled sales of credit default swaps and other complex financial instruments, the fundamental reason for failure was in the inaccurate credit risk model. In the aftermath, with the intent to force financial institutions to provide more detailed reports, The Federal Reserve’s CCAR now requires banks to account for more than 2,000 economic attributes. Consequentially, this also led to other regulating authorities introducing new standards that improve supervisory data quality and reporting. At the same time, with the proliferation of banking apps, social media, and digital communication overall, financial institutions now collect lavish amounts of unstructured data. If gathered and processed correctly, these new datasets can help gauge critical insights for a wide range of banking operations. This is where machine learning comes into play. More advanced non-linear approaches to credit risk modeling including neural networks enable banks to make predictions with a previously unseen level of accuracy and granularity. Challenges The utter superiority of machine learning over traditional credit risk modeling approaches comes at the cost of the prevailing ‘black box’ problem. While we can decide to trust ML algorithms based on statistical evidence of their feasibility, current regulatory constraints won’t allow it to happen. However, machine learning can still be used to a great extent while being regulation-compliant. Even simple linear machine learning approaches still yield more accurate results than conventional ones. Many banks also use unsupervised machine learning methods to explore data, while using traditional classification and regression models to make predictions. Fraud Management and Surveillance Nowadays, the majority of banks’ fraud detection systems use rule-based approaches. This causes banks to deal with a significant number of false positives, forcing them to spend inordinate amounts of resources to distinguish meaningless behavioral deviations from real threats. The ability of machine learning to capture subtle trends and uncover non-linear relationships allows banks to get a complete picture of a client’s activity and significantly lower the probability of false positives. For example, by integrating ML into its fraud detection model, Danske Banks managed to reduce false positives by 60%. Challenges Similar to many other AI-based solutions in the financial space, the biggest adoption hurdles concern regulations and the unexplainability of AI systems. For example, depending on the jurisdiction, banks are often unable to provide developers with sensitive information related to past breaches. Next, the outputs of unsupervised monitoring systems sometimes can’t be explained, which makes them non-compliant. However, financial institutions have found a way to at least partly leverage the power of ML for fraud management. A fraud prevention system’s alerts will still be triggered by rule-based models, but the integration of an ML algorithm on top of them can allow adjusting surveillance methods to a person’s behavior fluctuations. Such ML models are typically less complex and explainable, which makes them applicable in a regulatory context.

Read More

APIs in Fintech

Article | February 10, 2020

The banking and finance industry have grown and changed in recent years. Two of the factors driving that growth and change have been the rise of financial technology (fintech) companies or programs and the use of application programming interfaces (APIs). Because of APIs and fintech, people can now do incredible things with their money from the comfort of their homes that once required them to visit a local bank or brokerage.

Read More

What is the best accounting software for small businesses?

Article | August 10, 2020

The world of accountancy software has changed significantly during the course of the last few years and in this blog we are going to attempt to unravel the minefield of online accounting software to give you a clear understanding. Small business accounting is a huge area in which many small business owners struggle to get right. To find the best accounting software for small businesses in the UK we must first assess what cloud accounting is.

Read More

Is This A Fintech Bubble?

Article | February 24, 2020

Before I begin…good news for people nervous about the stock market panic today…. CNBC is airing a ‘Markets in Turmoil’ tonight. It never has failed at producing an investable bottom. If you are confused or angry…you might be too heavily weighted in stocks. The warning signs have been epic. Read Ben Carlson’s excellent piece titled ‘markets have always been rigged, broken and manipulated ‘.

Read More

Spotlight

Project Heather

Building a Scottish Stock Exchange for the 21st century. Join us in bringing impact to global capital markets and create liquidity, access and visibility for all.

Events