A new platform leverages AI to help potential buyers find an affordable home and earn bonus points on the purchase. Do you know the difference between 550 and 780? Enter here, no purchase necessary! If you don't check yours regularly, now's the time to start. Small mistakes are a lot more common than you think, and they can do some serious damage to your credit score.

Windows 11 has a lot of features you may not know about. CyberGuy shows you how to customize your computer. AI is sweeping across industries like a wave, opening up new frontiers and leaving regulators scrambling in its wake. It's easy to see why – with tools like ChatGPT on the rise, the line between humans and machines blurs more each day. However, just when we thought we had our hands full with job displacement debates and drafting digital policies, a new issue sneaks up – ChatGPT accounts stolen and traded on the dark web.

Joshua Browder, the CEO of New York startup DoNotPay, recently announced that his company's AI will represent a defendant fighting a traffic ticket in the courtroom on February 22nd. "[H]istory will be made," Browder wrote in his tweet. "DoNotPay A.I will whisper in someone's ear exactly what to say. We will release the results and share more after it happens," he added. We may never know how the "robot lawyer" will fare in court, though, because a few days later, Browder announced that DoNotPay is postponing its court case after he received threats of jail time from state bar prosecutors if he goes through with his plan.

Credit scores have been used for decades to assess consumer creditworthiness, but their scope is far greater now that they are powered by algorithms. Not only do they consider vastly more data, in both volume and type, but they increasingly affect whether you can buy a car, rent an apartment, or get a full-time job. In this second of a series on automation and our wallets, we explore just how much the machines that determine our credit worthiness have come to affect far more than our financial lives. This episode was produced by Jennifer Strong, Karen Hao, Emma Cillekens and Anthony Green. Miriam: It was not uncommon to be locked out of our hotel room or to have a key not work and him have to go down to the front desk and handle it. And it was not uncommon to pay a bill at a restaurant and then have the check come back. Jennifer: We're going to call this woman Miriam to protect her privacy.

TrackStar.ai, a company led by credit industry veterans that specializes in predictive credit technology, today announced the launch of a new proprietary, predictive API designed to help lending institutions determine consumer lending potential. By utilizing this first-of-its-kind API, lenders are able to make better decisions about qualifying current and prior loan applicants. The result is lower acquisition costs and churn, all while reducing lender's reliance on outside partnerships for leads. TrackStar's API is designed for enterprise level banking institutions and lenders to help them optimize the customer acquisition and retention process. TrackStar's predictive AI layer determines which negative credit items could be removed from a customer's credit history, allowing lenders to extend offers to customers who might normally get declined or not even considered as qualifying loan applicants.

Artificial intelligence (AI) presents an opportunity to transform how we allocate credit and risk, and to create fairer, more inclusive systems. AI's ability to avoid the traditional credit reporting and scoring system that helps perpetuate existing bias makes it a rare, if not unique, opportunity to alter the status quo. However, AI can easily go in the other direction to exacerbate existing bias, creating cycles that reinforce biased credit allocation while making discrimination in lending even harder to find. Will we unlock the positive, worsen the negative, or maintain the status quo by embracing new technology? This paper proposes a framework to evaluate the impact of AI in consumer lending. The goal is to incorporate new data and harness AI to expand credit to consumers who need it on better terms than are currently provided. It builds on our existing system's dual goals of pricing financial services based on the true risk the individual consumer poses while aiming to prevent discrimination (e.g., race, gender, DNA, marital status, etc.).

Credit institutions are poised to use a combination of artificial intelligence and facial recognition to instantly read the facial expressions of applicants to assess their likelihood of loan repayment. The South China Morning Post reports that Ping An Puhui, a Chinese micro lending unit of China's second-largest life insurer, has developed a digitalized loan process that can "analyse facial expressions of applicants to determine their willingness to repay the loans." The company contends that as a result of using new technologies, including facial recognition and big data, it has seen its customers "more than doubling to 5.5 million from 2 million a year ago," and its loan default rate drop, without the necessity of expanding its staffing. Facial recognition for identity verification and mobile payments just got a big boost with the latest iPhone launch. Thanks to Apple, consumers will become far more accustomed to the use of facial recognition for identity verification and digital payments.

There are lots of applications of text classification in the commercial world. However, the vast majority of text classification articles and tutorials on the internet are binary text classification such as email spam filtering (spam vs. ham), sentiment analysis (positive vs. negative). In most cases, our real world problem are much more complicated than that. Therefore, this is what we are going to do today: Classifying Consumer Finance Complaints into 12 pre-defined classes. The data can be downloaded from data.gov.

As U.S. banks wrestle with the decision of whether to use artificial intelligence to help calculate credit scores and make loan decisions, a potential role model is MyBucks, a company that's been doing this for more than a year --and has even begun offering 15-minute, AI-based loans through WhatsApp and Facebook Messenger. MyBucks is a Luxembourg-based fintech that owns several banks and provides loans and basic banking products in seven African countries, Poland and Spain; it's expanding rapidly into other countries. U.S. regulators have signaled a willingness to accept banks' use of AI in lending. And the evidence so far, at least in MyBucks' case, shows that AI can improve credit quality and reduce defaults. MyBucks' Haraka app, which is now offered in Zimbabwe, Uganda, Swaziland and Kenya, and in early 2018 is expected to be introduced in the Philippines and India, can score a customer within two minutes.

The lending and credit scoring sector have more data than ever before at their disposal. How they leverage this data to create value for their clients and social impact determines the outcomes they can achieve in the financial services space. In 1959, Arthur Samuel, a pioneer in the field of machine learning (ML) and artificial intelligence during an era when computers filled an entire building, defined machine learning as "a field of study that gives computers the ability to learn without being explicitly programmed." During a recent keynote, Microsoft CEO Satya Nadella referred to data used in this context as "the new electricity," calling our current era a "fourth industrial revolution" following steam, electricity and digital technology. Scott Guthrie, Microsoft executive vice president, also acknowledged that data is "enabling every business to be the disrupters of their industry by harnessing the power to drive insight from this data."