In recent times, many solution providers have claimed embedding AI in their Anti Money Laundering (AML) offerings. Upon conducting further research, it has become apparent that most enterprise level solution providers are supplementing existing AML solutions with predictive analytics, dashboard overview while very few are actually offering AI enabled real-time monitoring capability or agile solutions that respond to future anomalous behavior.
It is convenient to assume doom across all industries as the world struggles to contain debilitating virus. This, however, would be a false assumption. Due to their foresight of technology adoption, Wealth Managers, specifically Registered Investment Advisors (RIAs) are now very well positioned to navigate these (temporary) turbulent times.
Please note: We are not affiliated with Intel. We simply use Intel as an example of a familiar accredited solution provider who is offering specialized hardware.
Intel has done much to progress AI adoption across certain verticals through it’s data centric vision, whereby the company has dedicated significant resources across architecture and software. Speaking from Financial Services industry’s standpoint, as of today, only a handful of large and established banks are availing Intel’s portfolio of custom hardware. Most enterprises are either unaware of nuances of Intel’s offerings or are intimidated by cost and complexity. Most of these concerns are unfounded and it is more of a matter of perception. Objective of this research piece is to shed light on current hardware advancements, their relevance in optimizing advanced AI algorithms and their specific role within Financial Services.
From an Research and Development (R&D) standpoint, Intel’s proactive stance is reflective of how the industry will evolve in the next five years.
Wheels of most economies, even as advanced as the United States, are greased by thriving micro and small business segment. In fact, health of an economy is often gauged by the number of successful small businesses and not by conglomerates or venture capital funded unicorns.
Different countries have different definitions for small businesses, some of which aren’t realistic,
In uncertain and precarious times, most enterprises split into one of these two camps.
Those wanting to capitalize on lurking opportunities at any cost (arbitrage) and those wanting to retain customers while carefully helping them navigate through uncertainty. Which approach businesses adopt, is strictly their prerogative as the ultimate objective of any business is to maximize profit for their shareholders, ideally by keeping interest of all stakeholders (such as customers) in mind.
In dire times, it is not uncommon for enterprises wanting to fend for themselves but also wanting to capitalize (even if unfairly, unethically) on imbalances in demand and supply. To combat exploitation, unfair opportunistic advantages, consumers look up to regulators and compliance officers.
Explainability and interpretability of AI solution have been repeatedly discussed amongst most sincere and committed data scientists and researchers. Best models incorporate both. As per our findings, seasoned data scientists, machine learning experts try to factor in explainability and interpretability, from inception through finalization of their model.
As per a November 2019 CNBC article by Deutsche Bank‘s Chief Economist, following were the key factors which would shape global markets in 2020:
- U.S.- China trade wars
- Downward trend in the US stock exchange due to drop in demand for US Treasuries, credit rating downgrade of various corporations, tightening credit conditions on both, corporate and consumer side
- Possibility of impeachment of the President of the United States on U.S. and impact of that judgement on global stock markets (NYSE has equity market capitalization of over $19 trillion and is the largest in the world, so a significant movement on NYSE almost always has a ripples effect on other exchanges)
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