Pound achieves best rate against Euro since September 2012

| July 16, 2014

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The Pound has soared to reach its best rate against the Euro since September 2012, following news that inflation has unexpectedly risen in the UK. This boost is perfect timing just as schools start to break up for summer, with many families planning to travel abroad in the coming weeks.

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OTHER ARTICLES

The Way Forward: 21 Ideas for Bank Leaders to Boost Business after the Crisis

Article | March 25, 2020

In the hardest times, there is always a way to get better. With the Way Forward, I’d like to share disruptive changes that shape industries, businesses, and societies in the context of financial services. The 21 notes synthesise the findings of openness, flexibility, and customer-centricity in a digital world driven by platform business models and ecosystems. The reading shall give you a glimpse of what you could do for your business after this unprecedented time.

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5 Ways AI can Transform Fintech Industry

Article | February 25, 2020

Fintech is one of the fastest-growing industries, owing to the rising penetration of internet users. There is a paradigm shift to mobile devices for performing financial transactions and related actions. Behind the booming fintech market, there are several technologies that are contributing to making the system fast, secure, and scalable. One such technology is Artificial Intelligence (AI). The AI in the fintech market is estimated to reach USD 35.40 billion by 2025 ( Mordor Intelligence).

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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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10 Fintechs that Make Taxes Less Taxing

Article | April 15, 2020

Taxes, especially in the U.S., can be anxiety-inducing not only for consumers but also for small businesses. And even though this year’s tax filing deadline has been extended to July 15, the filing and payment requirements remain unchanged. “The daunting task of gathering documents for a year that has passed is one that is difficult for small business owners, especially when they already feel overwhelmed at tax time,” said Lil Roberts, CEO and founder of Xendoo. “Coupling that pain point with small businesses feeling that federal tax is a “black box” and understanding how to maximize tax savings is also extremely frustrating.”

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Spotlight

eCapital LLC

A steady cash flow can change the course of a business and define its future. That's why we’ve made it our sole purpose to empower businesses to thrive. Living and breathing that brand promise, we are a team of people who are passionate about small businesses.

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