Berry leads expansion of Macquarie’s European fund finance: Reuters

| August 31, 2017

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Steve Berry has joined Macquarie Specialised Investment Solutions (MSIS) as division director of its Fund Finance business, the company announced.Berry will be based in London and will be responsible for expanding Macquarie’s new and existing fund finance coverage in Europe, focusing on opportunities across private equity and private debt funds.

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Pareto Securities

Pareto Securities is an independent full service investment bank with a leading position in the Nordic capital markets and a strong international presence within the energy sectors. Pareto Securities is headquartered in Oslo, Norway, with more than 400 employees located in offices in Norway, Sweden, Denmark, Finland, United Kingdom, France, USA, Singapore and Australia. Pareto Securities was established in 1986 and is today part of the Pareto Group which also offers banking, project financing and asset management services. Visit our website for job opportunities. Securities trading, Research, Corporate Finance, Project Financing, and Asset Broking

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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.

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Which Fintech sectors will emerge fighting fit from the current Covid-19 crisis?

Article | March 31, 2020

Whilst the current pandemic induced crisis is yet to reach its peak, and is leaving a trail of personal and economic destruction, we should expect a new landscape to emerge, in many respects, when the dust settles. What does this mean for the Fintech sector in terms of opportunities and how does this impact the individual?

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Connected Finances: Top Benefits of Using IoT Technology in Banking

Article | February 19, 2020

Technology has always been the main force behind changes in the banking sector, and now the Internet of Things is going to change the nature of banking itself. If we step back from such isolated technologies like blockchain or the smartphone and try to see the global picture, we easily notice that something is changing, and that’s the way of global digitalization in the financial sector.

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4 Fintech Trends to Watch For in 2020

Article | February 26, 2020

The fintech industry is one of the most visibly disruptive sectors since it can dramatically impact everyday consumers as well as the business of all sizes. It’s also potentially a highly regulated sector, with governments and regulators well aware of the need to both protect consumers and businesses, and to provide a fair, competitive environment for industry players.

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Spotlight

Pareto Securities

Pareto Securities is an independent full service investment bank with a leading position in the Nordic capital markets and a strong international presence within the energy sectors. Pareto Securities is headquartered in Oslo, Norway, with more than 400 employees located in offices in Norway, Sweden, Denmark, Finland, United Kingdom, France, USA, Singapore and Australia. Pareto Securities was established in 1986 and is today part of the Pareto Group which also offers banking, project financing and asset management services. Visit our website for job opportunities. Securities trading, Research, Corporate Finance, Project Financing, and Asset Broking

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