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Three big questions about AI in financial services Lexology

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To ride the rising wave of AI, financial services companies will have to navigate evolving standards, regulations and risk dynamics--particularly regarding data rights, algorithmic accountability and cybersecurity. The success of artificial intelligence (AI) algorithms hinges on the ability to gain easy access to the right kind of data in sufficient volume. Put more simply, AI depends on good data. Even Google--which is famous for the pioneering work in AI that underpins its standard-setting search-based advertising business--makes no bones about the critical role of data in AI. Peter Norvig, Google's director of research, has said: "We don't have better algorithms, we just have more data." Companies increasingly realize that data is critical to their success--and they are paying striking sums to acquire it. Microsoft's US$26 billion purchase of the enterprise social network LinkedIn is a prime example. But other technology companies are also seeking to acquire data-related assets, typically to acquire more than just identity-linked information from social media sources by focusing instead on vast troves of anonymized consumer data.


Regtech rising: Automating regulation for financial institutions JD Supra

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Regulatory compliance is timeconsuming and expensive for both financial institutions and regulators. The volume of information that parties must monitor and evaluate is enormous. The rules are often complex and difficult to understand and apply. And much of the process remains highly labor-intensive, when even the most automated solutions are often incompatible with other systems and, even today, most still depend heavily on manual inputs. As a result, costs have risen significantly for financial institutions in recent years.


Algorithms and bias: What lenders need to know White & Case LLP International Law Firm, Global Law Practice

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Much of the software now revolutionizing the financial services industry depends on algorithms that apply artificial intelligence (AI)--and increasingly, machine learning--to automate everything from simple, rote tasks to activities requiring sophisticated judgment. These algorithms and the analyses that undergird them have become progressively more sophisticated as the pool of potentially meaningful variables within the Big Data universe continues to proliferate. When properly implemented, algorithmic and AI systems increase processing speed, reduce mistakes due to human error and minimize labor costs, all while improving customer satisfaction rates. Creditscoring algorithms, for example, not only help financial institutions optimize default and prepayment rates, but also streamline the application process, allowing for leaner staffing and an enhanced customer experience. When effective, these algorithms enable lenders to tweak approval criteria quickly and continually, responding in real time to both market conditions and customer needs. Both lenders and borrowers stand to benefit. For decades, financial services companies have used different types of algorithms to trade securities, predict financial markets, identify prospective employees and assess potential customers.


Algorithms and bias: What lenders need to know JD Supra

#artificialintelligence

Much of the software now revolutionizing the financial services industry depends on algorithms that apply artificial intelligence (AI)--and increasingly, machine learning--to automate everything from simple, rote tasks to activities requiring sophisticated judgment. These algorithms and the analyses that undergird them have become progressively more sophisticated as the pool of potentially meaningful variables within the Big Data universe continues to proliferate. When properly implemented, algorithmic and AI systems increase processing speed, reduce mistakes due to human error and minimize labor costs, all while improving customer satisfaction rates. Creditscoring algorithms, for example, not only help financial institutions optimize default and prepayment rates, but also streamline the application process, allowing for leaner staffing and an enhanced customer experience. When effective, these algorithms enable lenders to tweak approval criteria quickly and continually, responding in real time to both market conditions and customer needs. Both lenders and borrowers stand to benefit. For decades, financial services companies have used different types of algorithms to trade securities, predict financial markets, identify prospective employees and assess potential customers.


The Future is Fintech: 4 Drivers of Change in Financial Services

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The world of financial services has reached a point of no return along the road of digital transformation. A combination of several factors is driving this shift: the rise of big data, consumers' demands for convenient and affordable financial services, and the proliferation of mobile technology are all responsible. Financial services companies must adopt automated, data-drive solutions to successfully compete against new, technology-based entrants. Although the rules surrounding developing fintech are still under construction, its potential to improve operational efficiency, safeguard investments, and fortify cybersecurity, is uncapped. At this point in the game, technology adoption is mandatory for financial institutions.