As head of EMEA analytics, Lunn has led the team responsible for delivering analytics projects to customers throughout Europe, the Middle East and Africa, and for bringing advances in analytics technology to customer projects. In her new role, she will oversee regional delivery teams in EMEA, Latin America, Asia-Pacific and North America, as well as the company's Global Analytic Delivery Center based in Bangalore, India, applying the latest breakthroughs in analytics to solve specific customers' challenges. "FICO has been a global leader in analytics for more than 60 years, ever since it brought the first credit scoring systems to market," said Louise Lunn, vice president of Global Analytics Delivery. "The explosion in data and the increasing use of AI and machine learning have brought the power of FICO analytics to more businesses worldwide, in every industry. I'm proud to be on the front lines of this effort, and to lead a global team of data scientists addressing the specific challenges of businesses worldwide."
This article is part of our reviews of AI research papers, a series of posts that explore the latest findings in artificial intelligence. Last November, Apple ran into trouble after customers pointed out on Twitter that its credit card service was discriminating against women. David Heinemeir Hansson, the creator of Ruby on Rails, called Apple Card a sexist program. "Apple's black box algorithm thinks I deserve 20x the credit limit [my wife] does," he tweeted. The @AppleCard is such a fucking sexist program.
Here we describe a simple methodology to produce predictive scores that are consistent over time and compatible across various clients, to allow for meaningful comparisons and consistency in actions resulting from these scores, such as offering a loan. Scores are used in various contexts, such as web page rankings in search engines, credit score, risk score attached to loans or credit card transactions, the risk that someone might become a terrorist, and more. Typically a score is a function of a probability attached to some particular future event. They are built using training sets. The reasons why scores can become meaningless over time is because data evolves.
Part of the U.S. stimulus package under the CARES Act allows consumers to defer payments without being reported as late to credit agencies. Great for consumers, but bankers are challenged to determine borrower credit risk for new applications. The Federal Reserve predicts that U.S. banks could lose $700 million if the pandemic continues. As a result, rules are tightening, making it more difficult for good customers to borrow money or apply for credit cards. Lenders are raising credit score requirements for mortgages, credit cards and loans.
As one of the so-called "big four" U.S. banks, Chase needs little in the way of introduction. And like many age-old institutions, including its direct rivals, the New York-based financial powerhouse has had to move with the times, with Chase now investing more than $11 billion each year on the technology side of its business. This includes software development, cybersecurity, and -- increasingly -- artificial intelligence (AI) and machine learning (ML). Talking at Transform 2020 today, Sandra Nudelman, chief data and analytics officer at Chase for the past two years, outlined some of the main ways the company is harnessing AI and ML across its business, including helping streamline internal processes such as managing PPP applications, improving marketing efforts, increasing credit lines, and preventing fraud. In response to the COVID-19 crisis, the U.S. government launched the Paycheck Protection Program (PPP) a couple of months back to ensure money continues to roll into the workforce -- this, in turn, led to significant paperwork for banks, which have had to deal with a mountain of applications.
Credit card frauds are a "still growing" problem in the world. Losses in frauds were estimated in more than US$27 billion in 2018 and are still projected to grow significantly for the next years as this article shows. With more and more people using credit cards in their daily routine, also increased the interest of criminals in opportunities to make money from that. The development of new technologies puts both criminals and credit card companies in a constant race to improve their systems and techniques. With that amount of money at stake, Machine Learning is surely not a new word for credit card companies, which have been investing on that long before it was a trend, to create and optimize models of risk and fraud management.
Let's start with a thought experiment that will illustrate the difference between a decision tree and a random forest model. Suppose a bank has to approve a small loan amount for a customer and the bank needs to make a decision quickly. The bank checks the person's credit history and their financial condition and finds that they haven't re-paid the older loan yet. Hence, the bank rejects the application. But here's the catch – the loan amount was very small for the bank's immense coffers and they could have easily approved it in a very low-risk move. Therefore, the bank lost the chance of making some money.
We are living in the era of digital technologies. When was the last time you walked into a shop that didn't have a PayTM or BHIM UPI? These digital transaction technologies have quickly become a key part of our daily lives. And not just at an individual level, these digital technologies are at the core of every financial institution. Executing a payment transaction or fund transfer has become very smooth with multiple possible options (like internet banking, ATM, credit or debit cards, UPI, POS Machines, etc.) having reliable systems running at the backend.
Trust is a key factor in the implementation of deep learning applications. From training to optimization, the lifecycle of a deep learning model is tied to trusted data exchanges between different parties. That dynamic is certainly effective for a lab environment but results vulnerable to several all sorts of security attacks that manipulate the trusted relationships between the different participants in a model. Let's take the example of a credit scoring model based that uses financial transaction to classify the credit risk for a specific customer. The traditional mechanisms for training or optimizing a model assume that the entities performing those actions will have full access to those financial datasets which opens the door to all sorts of privacy risks.
As we discussed previously, Machine Learning refers to algorithms that are used to identify patterns within data. But what exactly do we mean by "patterns", what all can we do with ML, and what is all this jargon about "models" and "training" them. In this article, I'll try to explain all this without getting too technical, and what you, as a business-user, should know about Machine Learning. Supervised Learning implies use-cases where we have a target we're trying to predict given the data. Supervised algorithms enable us to predict the target (for example the estimated credit limit, tractor sales, if the customer will churn, or the mail category) using the input data (customer's credit history, weather and macroeconomic conditions, customer's activity on the platform, mail specifications). There are models both for Regression and Classification problems, i.e. algorithms which can solve these types of problems.