Secondary Deals and You

| June 25, 2019

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How do secondary deals in venture capital benefit the potential stakeholders? Founders, angel investors, VC funds, LPs and early employees can all gain different benefits from secondaries.

Spotlight

ZooPay Limited

ZooPay is one of the new brand of Fintech companies to spawn out of London in the last 2 years. The firms multi-currency capability has given E-merchants the ability to grow their business internationally. ZooPay is well placed to dominate E-commerce in the coming years with their unique proposition. The alignment with First Data has only confirmed this statement of intent as both companies join hands to provide a greater gateway to growth.

OTHER ARTICLES

How Lending-as-a-Service Can Impact Small Businesses in Need

Article | March 31, 2020

One of the brutal facts of the COVID-19 outbreak is that it will be difficult for small businesses to survive. The self-distancing and shelter-in-place orders, while temporary, are taxing for already cash-strapped merchants. Adding to the hardship, small businesses may find it especially difficult to get a much-needed loan from their local bank or credit union since many have closed physical branches to encourage social distancing. And while banks offer many services online, only 1% are capable of extending a loan digitally.

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Optimizing Risk Management With Machine Learning In Banking

Article | March 31, 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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Coronavirus Hits Economy. How Fintech is Affected?

Article | March 31, 2020

From stock market swings and billions wiped off of the airlines, hotels, transportation revenues to drug, soap or iPhone replacement shortages – the coronavirus outbreak is taking its toll on many business aspects of life. Euler Hermes calls this a "quarantined trade". The company's analysts estimated that Covid-19 costs $320bn of trade losses every quarter. But what about fintech? It’s not immune to the virus either. As the side effects of Covid-19 will be unfolding in the weeks to come, we’ll see some fintech or finance companies taking hits. But other companies or solutions will be gaining traction.

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How Data Aggregators Are Using Blockchain To Empower Fintech Players

Article | March 31, 2020

People know their banks and their favorite fintech applications. However, consumers are generally not aware of data aggregators like Plaid and Finicity, which collect consumer data from banks, crunch it and feed it to fintech applications. Blockchain technology can play a key role in helping data aggregators manage consumer financial data while complying with regulations and empowering consumers.

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Spotlight

ZooPay Limited

ZooPay is one of the new brand of Fintech companies to spawn out of London in the last 2 years. The firms multi-currency capability has given E-merchants the ability to grow their business internationally. ZooPay is well placed to dominate E-commerce in the coming years with their unique proposition. The alignment with First Data has only confirmed this statement of intent as both companies join hands to provide a greater gateway to growth.

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