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

#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.


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 New Morality of Debt – IMF F&D

#artificialintelligence

Throughout history, society has debated the morality of debt. In ancient times, debt--borrowing from another on the promise of repayment--was viewed in many cultures as sinful, with lending at interest especially repugnant. The concern that borrowers would become overindebted and enslaved to lenders meant that debts were routinely forgiven. These concerns continue to influence perceptions of lending and the regulation of credit markets today. Consider the prohibition against charging interest in Islamic finance and interest rate caps on payday lenders--companies that offer high-cost, short-term loans.


Apple under fire for new credit card's apparent unequal treatment of women

The Japan Times

NEW YORK – Apple Inc. pitches its new card as a model of simplicity and transparency, upending everything consumers think about credit cards. But for the card's overseers at Goldman Sachs Group Inc., it is creating the same headaches that have bedeviled an industry the companies had hoped to disrupt. Social media postings in recent days by a tech entrepreneur and Apple co-founder Steve Wozniak complaining about unequal treatment of their wives ignited a firestorm that has engulfed the two giants of Silicon Valley and Wall Street, casting a pall over what the companies had claimed was the most successful launch of a credit card ever. Goldman has said it has done nothing wrong. There has been no evidence that the bank, which decides who gets an Apple Card and how much they can borrow, intentionally discriminated against women.


AI Can Make Bank Loans More Fair

#artificialintelligence

As banks increasingly deploy artificial intelligence tools to make credit decisions, they are having to revisit an unwelcome fact about the practice of lending: Historically, it has been riddled with biases against protected characteristics, such as race, gender, and sexual orientation. Such biases are evident in institutions' choices in terms of who gets credit and on what terms. In this context, relying on algorithms to make credit decisions instead of deferring to human judgment seems like an obvious fix. What machines lack in warmth, they surely make up for in objectivity, right? Sadly, what's true in theory has not been borne out in practice.