Will fintech change everyday lives in Africa and Asia?

KONSTANTIN RABIN | March 19, 2020

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The modern world is constantly changing, offering more to societies across the globe than before. The recent technological development has proven to be unprecedented and revolutionary, changing the lives of millions in different corners of the world. The financial industry is one of the most affected sectors that is experiencing a major transformation due to a number of different factors. As a result, we now witness a diverse range of services in forms never seen before.

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JHL Capital Group LLC

JHL Capital Group is a registered investment adviser founded in 2006. Our goal is to deliver superior risk-adjusted investment returns to our partners. Our partners include high net worth individuals, endowments, foundations, pensions, sovereign wealth funds and other institutional investors. We invest alongside our partners. Our team of professionals has global financial markets experience across all asset classes. We foster a culture of operational excellence, transparency and compliance.

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Artificial Intelligence With Mobile Banking Is Reshaping the Future of Banking and Finance

Article | April 10, 2020

This is the flourishing time for financial services. The concept of interrelated technologies is developing and is brought forward due to the disruption in the industry such as that of cloud computing, data science, biometrics, and blockchain. However, most of the change has been brought about in the banking sector due to the introduction of artificial intelligence (AI). It is being expected that AI will bring a significant change in the industry in the near future and all of it will be significant.

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Optimizing Risk Management With Machine Learning In Banking

Article | April 10, 2020

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.

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How Fintech Startups Are Disrupting the Payments Industry

Article | April 10, 2020

Long in the past, transfers of value took place between royalty, merchants and commoners who all used gold, silver, cattle and other physical commodities to thrive and survive. That ended in 1971 when the U.S. dollar and other world fiat systems fully detached from the gold standard and embraced floating exchange rates. Over the past 50 years, financial institutions built payment systems that are partially obsolescing in the wake of fintech disruptions like virtual currencies, distributed ledgers and decentralized protocols.

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Is This A Fintech Bubble?

Article | April 10, 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 ‘.

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Spotlight

JHL Capital Group LLC

JHL Capital Group is a registered investment adviser founded in 2006. Our goal is to deliver superior risk-adjusted investment returns to our partners. Our partners include high net worth individuals, endowments, foundations, pensions, sovereign wealth funds and other institutional investors. We invest alongside our partners. Our team of professionals has global financial markets experience across all asset classes. We foster a culture of operational excellence, transparency and compliance.

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