Indexes gain at least 1 percent as financial shares lead

LEWIS KRAUSKOPF |

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Wall Street rallied for a second straight day on Wednesday, led by gains in beaten-down financial shares after JPMorgan's quarterly results.The major indexes each ended up at least 1 percent. The S&P 500 finished at its highest level in more than four months, while the Nasdaq registered its highest close of the year and the Dow industrials touched a more than five-month high.

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Applied Data Science in Fintech

Article | February 28, 2020

Under the supervision of Dr. Mario Gillrich and Maria Pelli, this course explores how new entrants in banking and finance are leveraging new technologies and methodologies to help traditional banks, their corporate clients and consumers use data and algorithms to better manage their financial operations and processes.

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How to Fight Fraud and Financial Crime Using Machine Learning Applications

Article | February 28, 2020

Going far away beyond conventional attack detection, advanced machine learning operations assist organizations to stay one step ahead of financial fraudsters. We hear tons of stories about account takeovers and hacking also. How can financial institutions detect and mitigate these attacks? The world of fraud prevention in banking institutions has always been supported by rules. Bankers and their engineers were uniting rules engines on the banking data system to stop or identify common fraud patterns. For quite a while, this was sufficient. But today we are experiencing a change of society, a digital and technological revolution. Following the primary iPhone, and therefore the later mobile internet explosion, people are interconnected all the time, everywhere and for all quite useful. In this digital age, the digitization of means and behaviors forces corporations to revise their business model. As a result, banking institutions are going massively online and digital-first. Both the bank users and customers have unfolded their behaviors with the brand-new means offered by the digital era. Learn more: https://deck7.io/Women-Leadership-verrency-audrey-blackmon With the shift towards universal digitalization, perpetrators are finding new weak spots in financial digital applications. Ironically, the technology works both ways: it accommodates firms to supply more reliable customer experience and optimize operations and, at an equivalent time, aids cybercriminals in performing numerous sophisticated unlawful schemes. According to the Association of Certified Fraud Examiners (ACFE), 30pc of fraud occurrences happened in small businesses, and 60pc of small-business fraud victims did not retrieve any of their losses. According to Statista, in 2017, the global FDP (fraud detection and prevention) market was calculated to be worth $16.6 billion. According to McKinsey, worldwide losses from card fraud could be close to $44 billion by 2025. Financial crimes do not limit crimes like credit card fraud, tax dodging, and elder abuse. In fact, it includes much broader offenses – such as Identity theft, human trafficking, phishing, pharming, drug trafficking, money laundering, and terrorist financing that can have enduring impacts on society. Fighting financial fraud is difficult because fraudsters frequently change and adapt. The moment you figure out how to identify and prevent one scam, a unique one emerges to take its spot. Identifying, eliminating, and blocking these threats are sensitive points for e-commerce and banking industries. Sincerely, the best technology for combating fraud is one that can evolve and adapt as instantly as the fraudster’s tactics. That’s what makes machine learning (ML) systems ideal to fight fraud and financial crime. The big problem is that companies think they need to establish rules, policies, and procedures to prevent fraud. But today’s criminals are much more sophisticated and are able to circumvent these business rules. Businesses need to take a more dynamic approach that includes business rules as well as machine learning and AI to learn from evolving criminal behavior and deliver a more sophisticated and effective approach to dealing with financial crimes. Andrew Simpson, Chief Operating Officer of CaseWare Analytics. Why use machine learning to combat financial fraud? Machine Learning knocks down the conventional ways of detecting fraud. It’s quicker, works with extensive amounts of data, and doesn’t rely on human resources. When designed optimally, it absorbs, adapts, and uncovers emerging patterns without the over-adaptation resulting in too many false positives. It’s time for ML to conclusively take center stage in assisting firms to recognize and counter fraud as fast as it’s performed. How Machine Learning Helps in Fighting Fraud and Financial Crime? Machine learning can learn normal behavior from training data and recognize abnormal behaviors that indicates money laundering, like, when money is transferred between suspicious geographies, active movement of funds between different accounts, or invoicing number sequences have been falsified. Machine learning is continually learning, and so they can recognize when the pattern of laundering change and adjust rapidly. Analyzing Huge Amounts of Transaction Data One of the most powerful features of machine learning algorithms is that they can analyze huge numbers of transaction data and flag suspicious transactions with highly accurate risk scores in real-time. Its algorithms serve 24/7 and process an immense amount of information with the flip of a switch. This risky analytics method recognizes complex patterns that are challenging for analysts to identify; this means banks and financial organizations are far more operationally proficient while detecting more fraud. The algorithms take various factors into account, including; customer’s location, the device used, and other circumstantial data points to form a detailed picture of every transaction. This strategy improves real-time decisions and protects customers against fraud, all without affecting the user experience. Thanks to extensive technological development, organizations will frequently rely on machine learning algorithms to determine which transactions are suspicious. Learn more: https://www.sas.com/en_in/insights/articles/risk-fraud/strategies-fraud-detection.html#/ Supervised and Unsupervised Learning for Detecting Complex Patterns Machines can be programmed to self-learn in an unsupervised model with ML so that transactions that do not conform to a set pattern are recognized and hence can be actioned upon in right period. Machine Learning automatizes the extraction of aware and unaware patterns from data. Once it identifies those patterns, it can employ what it learns to new and unseen data. The machine learns and modifies as new outcomes and new patterns are introduced to it via a feedback loop. In fraud detection, supervised machine learning algorithms can self-learn from targets within the data. While training a supervised model, it's important to present to it both fraudulent and non-fraudulent records that have been labeled as such. Unsupervised Machine Learning is different. It reveals potentially unusual risks you might not watch for because it works without a target. Instead, it looks for irregularities in the data. Machine Learning in Fraud Detection The fraud detection method employing machine learning starts with gathering and segmenting the data. Alongside this, the machine learning model receives training sets that train it to predict the possibility of fraud. Conclusively, it creates a fraud detection model: Input data- The first step is data input, which differs in Machine learning and humans. Humans strive to comprehend massive amounts of data, such a task is a five-finger play for ML. The more data an ML model eats, the better it can learn and polish its fraud detection abilities. Extract Features- Extracted features defining good customer behavior and deceitful behavior are added. These features normally include the customer’s location, identity, orders, network, and preferred payment method. Based on the complexity of the fraud detection system, the list of examined features can vary. Train Algorithm- Further in this process, a training algorithm is launched. In short, this algorithm is a collection of rules that a machine learning model has to pursue when deciding whether an operation is genuine or fraudulent. The more data a business can supply for a training set, the more reliable the ML model will be. Create Model- After the training is over, an organization receives a fraud detection model acceptable for their business. This model can detect fraud in no time with great accuracy. To be efficient in credit card fraud detection, a machine learning model needs to be continually improved and updated. Eventually, fraudsters will turn up with new bamboozle to game the system unless you keep it updated. Employing advanced fraud protection and detection systems electrified by ML, multiple industries can keep their finances secured. Capgemini alleges their ML fraud detection system can lessen fraud investigation time by 70% while boosting accuracy by 90%. Another ML fraud prevention solution provider, Feedzai, alleges that a well-trained machine learning solution can recognize and prevent 95% of all fraud while reducing the amount of human labor needed during the investigation stage. Reduction of False Positives With the level of complicatedness in today’s financial infrastructures, the term ‘false positive’ has become nearly correlated with the industry’s efforts to fight fraud. One of the banking’s most significant challenges is to minimize the number of false positives being generated, thereby saving time, money, and bypassing needlessly frustrating customers. AI and machine learning play a significant role in this area. Because they are proficient in examining a much more comprehensive set of data points, connections between entities and fraud patterns – including fraud scenarios not yet known to fraud analysts – the predominance of false positives can be extremely reduced. Bringing it all Together Multinationals like Airbnb, Yelp, and Jet.com are already employing AI solutions to get insights from big data and counter issues such as fake accounts, account takeover, payment fraud, and promotion abuse. Machine learning entertains all the messy work of data analysis and predictive analytics and empowers companies to grow and develop secure from financial fraud and crime. As mentioned, machine learning can be very convenient when it comes to fighting cybercrimes. ML prevents critical attacks on users’ and companies’ finances. It’s a quick, up-to-date, and cost-effective method to shield customers and the company’s data.

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How Lending-as-a-Service Can Impact Small Businesses in Need

Article | February 28, 2020

One of the brutal facts of the COVID-19 outbreak is that it will be difficult for small businesses to survive. The self-distancing and shelter-in-place orders, while temporary, are taxing for already cash-strapped merchants. Adding to the hardship, small businesses may find it especially difficult to get a much-needed loan from their local bank or credit union since many have closed physical branches to encourage social distancing. And while banks offer many services online, only 1% are capable of extending a loan digitally.

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Five Application Scenarios of AI in Banking

Article | February 28, 2020

Over the past decades, banks have been improving their ways of interacting with customers. They have tailored modern technology to the specific character of their work. For example, in the 1960s, the first ATMs appeared, and ten years later, there were already cards for payment. At the beginning of our century, users learned about round-the-clock online banking, and in 2010, they heard about mobile banking. But the development of the financial system didn’t stop there, as the digital age is opening up new opportunities — the use of Artificial Intelligence. By 2023, banks are projected to save $447 billion by applying AI apps. We will tell you how financial institutions are making use of this technology in their operations today. AI-powered chatbots Chatbots are AI-enabled conversational interfaces. This is one of the most popular cases of applying AI in banking. Bots communicate with thousands of customers on behalf of the bank without requiring large expenses. Researchers have estimated that financial institutions save four minutes for each communication that the chatbot handles. Since customers use mobile apps to carry out monetary transactions, banks embed chatbot services in them. This makes it possible to attract users’ attention and create a brand that is recognizable in the market. For example, Bank of America launched a chatbot that sends users notifications, informs them about their balances, makes recommendations for saving money, provides updates to credit reports, and so on. This is the way the bank helps its clients to make informed decisions. Another example is the launch of the Ceba chatbot, which brought great success to the Australian Commonwealth Bank. With its help, about half a million customers were able to solve more than two hundred banking issues: activate their cards, check account balances, withdraw cash, etc. Mobile banking AI functionality in mobile apps is becoming more proactive, personalized, and advanced. For example, Royal Bank of Canada has included Siri in its iOS app. Now, to send money to another card, it’s enough to say something like: "Hey, Siri, send $30 to Lisa!" - and confirm the transaction using Touch ID. Thanks to AI, banks generate 66% more revenue from mobile banking users than when customers visit branches. Banking organizations are paying close attention to this technology to improve their quality of services and remain competitive in the market. Data collection and analysis Banking institutions record millions of business transactions every day. The volume of information generated by banks is enormous, so its collection and registration turn into an overwhelming task for employees. Structuring and recording this data is impossible until there is a plan for its use. Therefore, determining the relationship between the collected data is challenging, especially when a bank has thousands of clients. There used to be the following approach: a client came to a meeting with a bank employee who knew their name and financial history and understood what options were better to offer. But that's history now. With the wealth of data coming from countless transactions, banks are trying to implement innovative business ideas and risk management solutions. AI-based apps collect and analyze data. This improves the user experience. The information can be used for granting loans or detecting fraud. Companies that estimated their profit from Big Data analysis have reported an average increase in revenue by 8% and a reduction in costs by 10%. Risk management Extension of credit is quite a challenging task for bankers. If a bank gives money to insolvent customers, it can get into difficulties. If a borrower loses a stable income, this leads to default. According to statistics, in 2020, credit card delinquencies in the U.S. rose by 1.4% within six months. AI-powered systems can appraise customer credit histories more accurately to avoid this level of default. Mobile banking apps track financial transactions and analyze user data. This helps banks anticipate the risks associated with issuing loans, such as customer insolvency or the threat of fraud. Data security According to the Federal Trade Commission report for 2020, credit card fraud is the most common type of personal data theft. AI-based systems are effective against malefactors. The programs analyze customer behavior, location, and financial habits and trigger a security mechanism if they detect any unusual activity. ABI Research estimates that spending on AI and cybersecurity analytics will amount to $96 billion by the end of 2021. Amazon has already acquired harvest.AI - an AI cyber security startup - and launched Macie - a service that applies Machine Learning to detect, sort, and structure data in S3 cloud storage.

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Hiscox

Hiscox is a leader in specialist insurance. We seek to provide the best protection and peace of mind for our clients through high quality insurance products, backed with excellent service. We are experts in covering a wide range of personal and commercial risks.

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