"Data is the new oil." Originally coined in 2006 by the British mathematician Clive Humby, this phrase is arguably more apt today than it was then, as smartphones rival automobiles for relevance and the technology giants know more about us than we would like to admit. Just as it does for the financial services industry, the hyper-digitization of the economy presents both opportunity and potential peril for financial regulators. On the upside, reams of information are newly within their reach, filled with signals about financial system risks that regulators spend their days trying to understand. The explosion of data sheds light on global money movement, economic trends, customer onboarding decisions, quality of loan underwriting, noncompliance with regulations, financial institutions' efforts to reach the underserved, and much more. Importantly, it also contains the answers to regulators' questions about the risks of new technology itself. Digitization of finance generates novel kinds of hazards and accelerates their development. Problems can flare up between scheduled regulatory examinations and can accumulate imperceptibly beneath the surface of information reflected in traditional reports. Thanks to digitization, regulators today have a chance to gather and analyze much more data and to see much of it in something close to real time. The potential for peril arises from the concern that the regulators' current technology framework lacks the capacity to synthesize the data. The irony is that this flood of information is too much for them to handle.
Banking and fintech firms have been using artificial intelligence (AI) for the last few years to improve fraud detection on credit and debit cards, analyze patterns of defaulters, caution users from overspending and even help them determine their spendings. Some companies have now also begun using predictive analytics to enhance how credit and debit cards are being used in real time. For instance, Philadelphia-based fintech firm cred.ai, The card was licenced by payments network Visa and issued by Wilmington Savings Fund Society, FSB. The credit optimizer tool uses an AI algorithm to improve the user's debt-to-credit ratio, which accounts for up to 30% of a FICO score that evaluates a person's creditworthiness in the US.
Artificial Intelligence and its inherent bias seems to be an ongoing contributing factor in slowing minorities home loan approvals. An investigation by The Markup found lenders were more likely to deny home loans to people of color than to white people with similar financial characteristics. Specifically, 80% of Black applicants are more likely to be rejected, along with 40% of Latino applicants, and 70% of Native American applicants are likely to be denied. How detrimental is the secret bias hidden in mortgage algorithms? It's important to note that 45% of the country's largest mortgage lenders now offer online or app-based loan origination, as FinTech looks to play a major role in reducing bias in the home lending market, CultureBanx reported.
For instance, Philadelphia-based fintech firm cred.ai, The card was licenced by payments network Visa and issued by Wilmington Savings Fund Society, FSB. The credit optimizer tool uses an AI algorithm to improve the user's debt-to-credit ratio, which accounts for up to 30% of a FICO score that evaluates a person's creditworthiness in the US. Apple, too, uses AI to determine a user's credit limit on the Apple Card. Closer home, Gurugram-based fintech firm OneBanc has developed a card to connect various banking systems.
Financial technology startup firm Lendai announced Wednesday that it has raised $35 million in equity and debt seed funding. The purpose of the company is to enable foreign, non-residential borrowers investing in US real estate properties the ability to access immediate financing and competitive rates using its AI-based Triple Digital Underwriting System platform – making the underwriting process fast, easy and efficient. According to the company's announcement on Wednesday, this early round of financing is led jointly by Meron Capital and Cardumen Capital, with underwriting help from Discount Capital, Skywell Capital Partners, Mindset Ventures, and Viola Credit. Proceeds from the seed financing will enable Lendai to expand its reach and to help level the playing field for foreign investors who want to invest in US residential real estate properties. Concurrently, Lendai will use the seed funding to expand its services to more US states and launch new financing loan programs.
Humans invented artificial intelligence, so it is an unfortunate reality that human biases can be baked into AI. Businesses that use AI, however, do not need to replicate these historical mistakes. Today, we can deploy and scale carefully designed AI across organizations to root out bias rather than reinforce it. This shift is happening now in consumer lending, an industry with a history of using biased systems and processes to write loans. For years, creditors have used models that misrepresent the creditworthiness of women and minorities with discriminatory credit-scoring systems and other practices. Until recently, for example, consistently paying rent did not help on mortgage applications, an exclusion that especially disadvantaged people of color.
We develop a structural econometric model to capture the decision dynamics of human evaluators on an online micro-lending platform, and estimate the model parameters using a real-world dataset. We find two types of biases in gender, preference-based bias and belief-based bias, are present in human evaluators' decisions. Both types of biases are in favor of female applicants. Through counterfactual simulations, we quantify the effect of gender bias on loan granting outcomes and the welfare of the company and the borrowers. Our results imply that both the existence of the preference-based bias and that of the belief-based bias reduce the company's profits. When the preference-based bias is removed, the company earns more profits. When the belief-based bias is removed, the company's profits also increase. Both increases result from raising the approval probability for borrowers, especially male borrowers, who eventually pay back loans. For borrowers, the elimination of either bias decreases the gender gap of the true positive rates in the credit risk evaluation. We also train machine learning algorithms on both the real-world data and the data from the counterfactual simulations. We compare the decisions made by those algorithms to see how evaluators' biases are inherited by the algorithms and reflected in machine-based decisions. We find that machine learning algorithms can mitigate both the preference-based bias and the belief-based bias.
"Alexa, buy a stock that has the best chance of going up between 1% and 3% today." Could the complexity of financial research ever become this simple? New developments in artificial intelligence (AI) and machine learning (ML) are disrupting the underwriting process, portfolio composition, robo-advising, research and virtually every corner of fintech. Someday, you'll have reliable AI that can analyze your specific investing style, alert you as to where opportunities lay hidden and offer you hard-hitting analyses to stay informed. This is vital because sound financial systems underpin economic growth and development, and they're the engine behind the civilized world in advancing shared prosperity and reducing class inequality.
Buying a home is an important milestone many Americans dream about. Kids grow up doodling images of their dream home. College students start building their credit early so they can apply for a mortgage in the future. People save money for years so they can afford a downpayment. But, imagine if after all that dreaming and hard work, your hopes of buying a home are dashed by a biased lending algorithm that uses your race, or where you grew up, to determine your future. According to a recent investigation conducted by The Markup, this nightmare is a reality for many prospective borrowers in the United States.