Finance function of the future

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In the future will there even be a ‘Finance function’ as we know it today? Is it possible that Finance skillsets and principles will be driven into other areas of the organization that become responsible for budgeting, performance reporting and strategic decision making? If so, where in the organization does this leave Finance?

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Why the Future of Open Banking Belongs to Platforms

Article | February 20, 2020

The platform economy is revolutionising the $50bn global deposits business. By separating the product provider and financial point of sale, banks can now choose whether they want to collect deposits for financing or offer deposits as a product, without one depending on the other. While the deposits business has not always been perceived as one of the most innovative areas of banking, it is now leading the charge towards a better connected and more functional industry. This is bringing massive benefits to customers and institutions alike.

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Optimizing Risk Management With Machine Learning In Banking

Article | February 20, 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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BIG TECH IN FINANCE: A DEEP DIVE INTO THE FUTURE OF FINTECH

Article | February 20, 2020

The following article looks at Big Tech and its impact on the financial services sector. Whilst competition from small fintech startups will certainly take away some market share from traditional banks, the impact of “GAFA” could be huge. The fintech movement did more than unbundle banking and its core services — it spurred financial inclusion across Asia, increased overall economic growth, and made significant inroads into the finance value chain. The born-digital companies brought technology to the forefront, attacking the traditional risk-averse sector from various points — digital payments, insurance, P2P lending, and investment management, among other avenues.

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10 Fintechs that Make Taxes Less Taxing

Article | February 20, 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

National Lloyds Insurance Corporation

Headquartered in Waco, Texas, we offer you a dedicated network of more than 4,200 agencies in over 30 states to provide you access to homeowners insurance, fire and flood protection, and insurance for manufactured homes. We can make your life easier.

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