Credit Claims For Boston Information Technology And Computer Science Professionals: Attorney Support For Financial Stability

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Have you used your credit card at a recent store or location only to have it declined? Are you banned from selling because you are more than normal?

Credit Claims For Boston Information Technology And Computer Science Professionals: Attorney Support For Financial Stability

Consumers’ credit cards are often declined on legitimate transactions. One reason is that the fraud detection techniques used by the customer’s bank may have incorrectly flagged the purchase as suspicious. Now MIT researchers have used a new machine learning technique to significantly reduce these negative effects, saving banks money and customer satisfaction.

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Using machine learning to detect financial fraud has been around since the early 1990s and has advanced over the years. Researchers train models to extract patterns from past actions, called “patterns,” that signal fraud. When you swipe your card, the card pings the model and, if the features are related to fraudulent activity, the sale is prohibited.

Behind the scenes, data scientists have to dream up those models, mainly to set blanket rules for size and location. If customers make more than $2,000 in a single purchase, or sell in bulk in one day, they may be flagged. But because consumer spending varies, even in individual accounts, these models are inaccurate: A 2015 report from Javelin Strategy and Research estimated that only one in five false predictions were accurate and errors can cost a bank $118 billion in revenue. because customers refuse to use that credit card.

MIT researchers have developed an “automated feature technology” method that interprets more than 200 specific features for each individual transaction – say, whether a user is in the process of making a purchase, and the average amount. spent a few days on several items. By doing so, it can better pinpoint when a cardholder’s spending habits deviate from the norm.

Tested on a database of 1.8 million transactions from a large bank, the model reduced false positive predictions by 54 percent compared to traditional models, which the researchers believe was able to save in the bank 190,000 euros (about $220,000) in profit.

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“The biggest challenge in this industry is negative results,” said Kalyan Veeramachaneni, a senior research scientist at MIT’s Laboratory for Information and Decision Systems (LIDS) and lead author of a paper describing the model, presented at the European Council. for machine learning. “We can say that there is a direct relationship between the modeling technique and [reducing] negative results. … It is the most important thing to improve the accuracy of these machine learning models.”

Paper co-authors: Roy Wedge ’15, a senior researcher in the Data to AI Lab at LIDS; James Max Kanter ’15, SM ’15; and Sergio Iglesias Perez of Banco Bilbao Vizcaya Argentaria.

Three years ago, Veeramachaneni and Kanter developed Deep Feature Synthesis (DFS), a sophisticated method to extract very specific features from any data, and decided to apply it to financial transactions.

Companies will sometimes host contests where they provide limited data with a predictable risk such as fraud. Data scientists develop predictive models, and the prize money goes to the most accurate model. Researchers have participated in one such competition and achieved high scores with DFS.

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However, they found that the method could be implemented in its ability to be trained on visual principles. “If you look at what the data companies release, it’s a fraction of what they actually are,” Veeramachaneni said. “Our question is, ‘How do we take this approach to real businesses?'”

Supported by the Defense Advanced Research Projects Agency’s Data-Driven Discovery of Models program, Kanter and his team at Feature Labs — a spinout promoting the technology — developed a database for extracting features, in called Featuretools, used in this study. .

The researchers obtained a three-year database provided by an international bank, which contained detailed information on transaction volumes, times, locations, customer types, and results in used. There are about 900 million transactions from about 7 million individual cards. Of those transactions, approximately 122,000 were confirmed to be fraudulent. The researchers trained and tested their model on subsets of that data.

In practice, the model looks for patterns of transactions and between cards that match cases of fraud. It automatically combines all the variables found in the “in-depth” features to give a very detailed look at each transaction. From the data, the DFS model extracted 237 features for each transaction. Those represent the most common trends for cardholders, Veeramachaneni said. “Say, on Friday, it’s normal for a customer to spend $5 or $15 cash at Starbucks,” he said. “That variable would be like, ‘How much money was spent at a coffee shop on Friday morning?'”

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It creates an if/then decision tree for that list of features that do or do not indicate fraud. When a new function is run on the decision tree, the model determines in real time whether it is a fraud or not.

Applied to a traditional model used by a bank, the DFS model generated about 133,000 false positives with 289,000 false positives, about 54 percent less occurrences. That, along with a small number of detected errors – true fraud that has not been detected – could save the bank around 190,000 euros, researchers estimate.

Iglesias found that he and his colleagues at BBVA were able to reproduce the MIT team’s results using the DFS model with business cards and data, with little increase in cost. serial number.

The backbone of the model is “primitives” that are coded with a simple function that takes two inputs and provides an output. For example, calculating the average of two numbers is primitive. It can be combined with a primitive that looks at the timestamp of two tasks to find the average time between tasks. Applying an initial formula that calculates the distance between two points from those trades gives the average time between two trades at two specific points. A primitive can determine whether a purchase was made on a weekday or weekend, etc.

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“Once we have those primitives, there’s nothing stopping us from fixing them… and you start to see these interesting things that you didn’t think about before. If you dig deeper into the algorithm , the primitives are the secret,” says Veeramachaneni.

An important feature created by the model, Veeramachaneni notes, is the calculation of the distance between those two places and whether it is human or remote. If someone buys something at, say, the Stata Center in person and, half an hour later, buys something in person 200 miles away, then there is a high level of fraud. But if a purchase is made on a mobile phone, the illusion falls.

“There are many patterns you can interpret in the patterns you see in past data about fraud or fraud cases,” Veeramachaneni said.

“Certainly, this automated scene synthesis technique, as well as the overall experience that MIT has contributed to this project, has revealed a new way of looking at the research of other problems where we have a reduction of features. For example, we have promising results in the detection of anomalous trends in the local network or market activities, just to mention two [ features],” Iglesias added.

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The findings could help doctors identify which cancers would benefit most from drugs called checkpoint blockade inhibitors.

The machine learning method works on most mobile devices and can be extended to evaluate other patients outside of the doctor’s office.

Plata’s expertise in the field of education and business will help advance the mission of the consortium and will accelerate the implementation of the systems that can be implemented in the future.

Pounds succeeded a generation of MIT Sloan faculty members and taught the wider Institute through the turbulent years of the Vietnam War.

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While computer scientists may initially treat data and error as evil, researchers argue that it has a hidden value in considering societal values.

For the first time, researchers have seen the flow of lithium ions through a battery, which can help engineers plan the design of the device. So we want you to know that, when you visit our website, we use technologies such as cookies to collect anonymous data so that we can better understand and serve our audience. For more information, see our full Privacy Policy.

This report, co-authored by The Century Foundation, the National Employment Law Project, and Philadelphia Legal Assistance, presents the findings of a comprehensive study of state efforts to reform their unemployment benefits (UI) systems. ). This is the first report to detail how UI modernization has changed the customer experience. It provides lessons learned from state efforts and recommends planning and implementation methods to help states succeed in future programs.

Although the need for better systems was recognized earlier

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