See the use case. Machine Learning in Finance – What’s Next? Challenges. Machine Learning powered solutions allow finance companies to completely replace manual work by, automating repetitive tasks through intelligent process automation. We focused on the top 7 data science use cases in the finance sector in our opinion, but there are many others that also deserve to be mentioned. There are various budget management apps powered by machine learning, which can offer customers the benefit of highly specialized and targeted financial advice and guidance. See the use case. premise that past events have a significant impact on both the present and the We previously covered the top machine learning applications in finance, and in this report, we dive deeper and focus on finance companies using and offering AI-based solutions in the United Kingdom. Save my name, email, and website in this browser for the next time I comment. Machine Learning Use Cases in the Financial Domain. allows the fund managers to identify specific market changes much earlier as compared to the traditional investment models. One of the core machine learning use cases in banking/finance domain is to combat fraud. Chatbots, paperwork automation, and employee training gamification are some of the examples of process automation in finance using machine learning. It involves the use of machine learning applications to make split-second Required fields are marked *. Challenges Faced by Finance Companies While Implementing Machine Learning Solutions, Lack of understanding about business KPIs, Future Prospects of Machine Learning In Finance. I am a BA Political Science degree holder who fell in love with content writing right after college. access to the internet, vast amounts of computing power and valuable data investments so as to align the portfolio based on a set target. by SharePointReviews.com, "4.3 out of 5" Fund managers are better able to identify market changes much earlier improve performance. AI and ML tools such as data analytics, data mining, and natural language processing, help to get valuable insights from data for better business profitability. The finance industry is one of the industries with the best machine learning applications. In the 2018 WEF report, 73% of financial services and investment companies surveyed were to adopt machine learning by 2022 7. These models are generally built on the client’s behavior on the internet and transaction history. While some of the applications of machine learning in banking & finance are clearly known and visible such as chatbots and mobile banking apps, the ML algorithms and technology are now being gradually used for innovative future applications as well, by drawing out historical data of customers accurately and predicting their future. avoid required reporting. Sophisticated ML algorithms can be used to analyze user behavior and develop customized offers. Machine learning applications in finance can help businesses outsmart thieves and hackers. Fraud Detection: When it comes to online fraud, banking, and financial organizations are always at a higher risk of getting cheated. This also frees up the security personnel to focus on other more complex problems. . Traditional models often use a rule-based system with a focus on the Machine learning algorithms need just a few seconds (or even split seconds) to assess a transaction. That said, the emergence of new use cases of machine learning in finance, clearly illustrating the value the technology brings, is prompting many companies to reconsider. Shift to an agile & collaborative way of execution. Credit card companies can use ML technology to predict at-risk customers and specifically retain selected ones out of these. learning to prevent money laundering is based on the ability of such systems to An excellent example of this is the, For most of the financial companies, the need is to start with identifying the right set of use cases with an, experienced machine learning services partner. Financial monitoring is another security use case for machine learning in The UK government released a report showing that 6.5% of the UK's total economic output in 2017 was from the financial services sector. They also require constant re-tuning to keep up with fraudsters or risk The value of AI & Machine learning in finance is becoming more apparent by the day./> streamlined. Turn your imagerial data into informed decisions. Bank of America and Weatherfont represent just a couple of the financial companies using ML to grow their bottom line. They use this to train machine learning models and assess Most financial management applications can match incoming payments to outstanding accounts receivable (AR) invoices, provided the payment … Twitter. Oftentimes, it involves a complex ETL process with a decision science setup that combines a rules engine with an ML platform. Machine learning applications for In today’s era of digitization, staying updated on technological advancements is a necessity for businesses to both outsmart the competition and achieve desired business growth. The chatbot helps customers get all the information they need regarding their accounts and passwords. In recent years, the ability of data science and machine learning to cope with a number of principal financial tasks has become an especially important point at issue. One of the most common applications of machine learning in the finance sector is fraud detection. In view of the high volume of Due to the illogical, Here are automation use cases of machine learning in finance: 1. Analyse data. investing heavily in ML technologies to develop automated investment advisors, the disruption in the investment banking industry is quite evident. can effectively monitor manually. Cryptocurrency, Tech, Business, Technical writer | Digital marketer, Click to share on Twitter (Opens in new window), Click to share on Facebook (Opens in new window), Click to share on LinkedIn (Opens in new window). Until recently, only the hedge funds were the primary users of AI and ML in Finance, but the last few years have seen the applications of ML spreading to various other areas, including banks, fintech, regulators, and insurance firms, to name a few. Some of the other benefits of Algorithm Trading include –. AI technologies can help make an informed decision about investments and predict possible risks using data analytics, deep learning, and machine learning algorithms. a loan or defaulting? Let’s get practical! In all three approaches, machine simMachines supports financial services clients across a variety of use cases. As machine learning becomes increasingly popular, we’re keeping track of the way it is used across industries. A robo-advisor automatically , who can develop and implement the right models by focusing on specific data and business domain after thorough understanding of the expected output that is going to be extracted from different sources, transform it, and get the desired results. Automated Trading. accounts. Google+. 7. by Customers (67 reviews), "An Extremely useful tool! Further, an interesting trend to watch in the future would be Robo-advisors suggesting changes in portfolios and a rapid rise of ML-based personalized apps and personal assistants offering more objective and reliable advisory services to the customers. These ML-based Robo-advisors can apply traditional data processing techniques to create financial portfolios and solutions such as trading, investments, retirement plans, etc. Top use: Creating business insights with machine learning Case study: One American multinational finance and insurance corporation faced competition from smaller companies that … Bear in mind that some of these applications leverage multiple AI approaches – not exclusively machine learning. However, we can still talk about some real-world use cases and ways your business can benefit. Machine learning and AI have enabled financial marketers to connect activity and behavioral inputs such as transaction history, website inquires, social media interactions with consumer-centric outputs. A lot of banking institutions till recently used to lean on logistic regression (a simple machine learning algorithm) to crunch these numbers. Fast forward to the present day, machine To enhance accuracy, some use a There are many machine learning applications in finance, including for banking and credit offerings, payments and remittances, asset management, personal finance, and regulatory and compliance services. Machine Learning / AI use-cases for Financial Services In-depth assessment of risk in portfolios (classification) Comprehensive credit-risk assessment (classification) Robo-advisers – investment management robots providing automated advice to investor (recommender system) Analysing companies’ currency exposure to gain more in … Share on Facebook Share on Twitter Share on LinkedIn. To use this approach, we must have quality data. There are many origin… through the Facebook Messenger to communicate with its users effectively. The amount of sensitive data that applicants. in. Banks are generally equipped with monitoring systems that are trained on historical payments data. information manually is not so easy. Apart from the established use cases of machine learning in finance, as discussed in the above section, there are several other promising applications that ML technology can offer in the future. Credit card companies can use ML technology to predict. Call Centers are a thing of the past, as the generations of computer-savvy people enter the banking world. More Bank of America has rolled out its virtual assistant, Erica. Further, consumer sentiment analysis can also complement current information on different types of commercial and economic developments. portfolio management The algorithm based on data and Machine Learning helps quickly find the necessary documents and the important information … AI and ML in financial services. Data security in banking & finance is a critically important area. Machine Learning Use Cases in Banking. Digital Wealth Management. High Frequency Trading (HFT) Machine learning algorithms can also analyze hundreds of data sources simultaneously, giving the traders a distinct advantage over the market average. In the past, mathematicians would use historical data The anti-money laundering machine learning system than they would with traditional approaches. In the present day, machine Machine learning models can be of great help to finance companies when it comes to analyzing current market trends, predicting the changes, and social media usage for every customer. Apart from helping them improve retention rates, it also helps them understand user behavior and their changing concerns and needs. How it's using AI in finance: Kavout uses machine learning and quantitative analysis to process huge sets of unstructured data and identify real-time patterns in financial markets. While few of these have relatively active applications today, others are still at a nascent stage. Visualize & bring your product ideas to life. But the cumulative effect of Further, Machine Learning technology can easily access the data, interpret behaviors, follow and recognize the patterns. There are tons of use cases of machine learning in finance. In financial markets trading, This technology is already live and used in automatic email reply predictions, virtual assistants, facial recognition systems, and self-driving cars. Let’s take a look. Source: Maruti Techlabs – How Machine Learning Facilitates Fraud Detection Fraud in the FinTech sector is a knotty problem for all service providers, regardless of their size and number of customers. The future holds a high possibility of machine learning technologies powering the most advanced cybersecurity networks. class of online software that can help users manage their investments. from available data and recalibrating to handle novel situations. Click here to access machine learning use cases for financial services. Notify me of follow-up comments by email. Fraud Detection. An example of this could be machine learning programs tapping into different data sources for customers applying for loans and assigning risk scores to them. Machine Learning works by extracting meaningful insights from raw sets of data and provides accurate results. In the financial industry, we have found success integrating machine learning in many use cases, but the following are great places to start: Fraud Detection. importantly, after investing funds, the software will constantly adjust the Furthermore, large financial institutions could already have lots of useful Building a fraud prevention framework often goes beyond just creating a highly-accurate machine learning (ML) model due to an ever-changing landscape and customer expectations. Financial services companies want to exploit this great opportunity, but owing to unrealistic expectations and lack of clarity on how AI and Machine Learning works (and why they need it), they often fail in this aspect. We think disruptively to deliver technology to address our clients' toughest challenges, all while seeking to At Maruti Techlabs, we work with banking and financial institutions on a myriad of custom AI and ML based models for unique use cases that help in improving revenue, reduce costs and mitigate risks in different departments. Chatbots 2. Various insights gathered by machine learning technology also provide banking and financial services organizations with actionable intelligence to help them make subsequent decisions. Banks are generally equipped with monitoring systems that are trained on historical payments data. The chatbot helps customers get all the information they need regarding their accounts and passwords. Machine learning in finance is rapidly developing – there are already dozens of options for its use in the financial sector. Learn about our. Numerous processes Pinterest. personnel to assess. Here are a few use cases where machine learning algorithms can be/are being used in the finance sector – Financial Monitoring; Machine learning algorithms can be used to enhance network security significantly. Before collecting the data, you need to have a clear view … The Machine Learning use cases are many — from sorting the email using Natural Language Processing (NLP) and automatically updating the records in the Customer Relations Management (CRM) solution, to providing efficient assistance through customer self-service portals and up to predicting the stock market trends in order to ensure successful trading. Call-center automation. They The requirements for such a platform include scalability and isolation of multiple … For instance, when a particular on learning, emergence of robo-advisors for transaction level, an outdated approach that results in many false positives. According to a research, for almost every $1 lost to fraud, the, recovery costs borne by financial institutions are close to $2.92, One of the most successful applications of ML is credit card fraud detection. analyzing available data. by Tim Sloane. failure. Unlike rules-based systems, which are fairly easy for fraudsters to test and circumvent, machine learning adapts to changing behaviors in a population through automated model building. Apart from spotting fraudulent behavior with high accuracy, ML-powered technology is also equipped to identify suspicious account behavior and prevent fraud in real-time instead of detecting them after the crime has already been committed. instruments’ pricing. AI and Machine Learning models to make accurate predictions based on past behavior makes them a great marketing tool. Even when The combination of all such challenges results in unrealistic estimates, and eats up the entire budget of the project. The recent years have seen a rapid acceleration in the pace of disruptive technologies such as AI and Machine Learning in Finance due to improved software and hardware. The future will see ML and AI technologies being actively used by insurance recommendation sites to suggest customers a particular home or vehicle insurance policy. AI. November 6, 2018 . Machine learning in finance might work magic, although there’s no secret powering it (well, perhaps just a bit of bit). The finance industry, including the banks, trading, and fintech firms, are rapidly deploying machine algorithms to automate time-consuming, mundane processes, and offering a far more streamlined and personalized customer experience. An international bank client provides loans to small businesses. So why does the industry use AI for finance? SHARES. Just 30 years ago, you would have to wait days for a bank to approve your credit. Here are four common applications of machine learning in the financial sector that have been implemented with open source technologies: 1. Classification, on the other hand, is exposing a model to known behavior, good Machine Learning Use Cases in Financial Crimes Ten practical and achievable ways to put machine learning to work. From analyzing the mobile app usage, web activity, and responses to previous ad campaigns, machine learning algorithms can help to create a robust marketing strategy for finance companies. investors, the emergence of robo-advisors for activities until the user confirms them. To establish the appropriate credit amount for a particular customer, companies use machine learning algorithms that can analyze past spending behavior and patterns. plays a key role in many facets of the sector’s ecosystem. Have you ever been a victim of credit card fraud? This is the reason why finance companies need to set realistic expectations for every. unpredictable and chaotic nature of financial markets, traditional investment data science machine learning trends. such a model after a day’s work is remarkable. Embedding AI technologies — such as machine learning, deep learning and algorithm-based machine reasoning — directly into financial management applications will be transformational. portfolio management, Download SharePoint Essentials Toolkit Now, Machine Learning for Content Filtering – Winning the Battle against Harassment and Trolling, 7 Steps to Correct Data Preparation for Machine Learning, Machine Learning Chatbots: What You Should Know about Neural Conversation Agents, Microsoft Cognitive Services – Democratization of AI, How Machine Learning With the Help of SharePoint Can Revolutionize the Manufacturing Industry. Consequently, trends. Problem. It is an especially sensitive area of Depending on a particular use case and business conditions, financial companies can follow different paths to adopt machine learning. In the past, fraud detection Using our machine learning software, the financial services industry can better detect fraud, assess credit worthiness, and more. ML-powered classification algorithms can easily label events as fraud versus non-fraud to stop fraudulent transactions in real-time. Machine Learning Use Cases in American Banks. With all the information available online, organizations find it increasingly challenging to keep all the usernames, passwords, and security questions safe. An increasing number of financial institutions are now prioritizing customer engagement for obvious reasons. ML-based solutions and models allow trading companies to make better trading decisions by closely monitoring the trade results and news in real-time to detect patterns that can enable stock prices to go up or down. The financial industry is subject to various risks, especially when investing. The approaches to handling risk management have changed significantly over the past years, transforming the nature of finance sector.As never before, machine learning models today define the vectors of business development. for their users. These models are designed on the Here are four common applications of machine learning in the financial sector that have been implemented with open source technologies: 1. To use this approach, we must have quality data. As we’ve already mentioned, AI efficiently deals with great amounts of raw data and the finance industry can provide the needed training materials for machine learning. The speed helps to prevent frauds in real time, not just spot them after the crime has already been committed. For millennials and other tech-savvy In other cases, the amount of assets does not justify hiring an advisor. Learn how your comment data is processed. available online sets the stage for massive technological progress. security risks. AI and machine learning in finance: use cases in banking, insurance, investment, and CX Just 30 years ago, you would have to wait days for a bank to approve your credit. better performance. Because human factors primarily drive the stock market, businesses need to learn from the financial activity of users continuously. that were in the past cumbersome and time-consuming have become a lot more About this paper. , customers can get all their queries resolved in terms of finding out their monthly expenses, loan eligibility, affordable insurance plan, and much more. Fraud Detection. sustainable patterns was rather difficult and much of it seemed like guesswork. systems take advantage of the smallest windows of opportunity to make profits. The ability of AI and Machine Learning models to make accurate predictions based on past behavior makes them a great marketing tool. ones should get top priority. consumer data. 0. One of Kavout's solutions is the Kai Score, an AI-powered stock ranker. With renowned firms such as Bank of America, JPMorgan, and Morgan Stanley investing heavily in ML technologies to develop automated investment advisors, the disruption in the investment banking industry is quite evident. WhatsApp Chatbot in Healthcare Space – The Need of the Hour, 9 Ways Machine Learning Can Transform Supply Chain Management, We use cookies to improve your browsing experience. Here are few Present Use Cases and Future Scope of AI and Machine Learning In Finance. Migrate from high-load systems to dynamic cloud. Or spend weeks bogged down by your insurance company’s bureaucracy just to get a refund after a minor car accident. ​ Machine Learning Use Cases in Finance. While developing machine learning solutions, financial services companies generally encounter some of the common problems as discussed below –. There are definitely number of factors and use of multiple models that we need to consider in a real world problem but in the interest of article’s length I have restricted it to KNN only. for enhanced business productivity. This site uses Akismet to reduce spam. In fraud detection it can be name of vendors, details of transaction like date, time, location, bank name or source name so on and so forth. Volumes of data — including accurate accounting records and other numbers — that have been saved by financial companies for years can now be turned into effective business drivers. pre-set checklist. For example, how much does one’s In other cases, getting useful As such, they use historical data to predict future investment VIEWS. information, it can now identify anything that seems unusual or suspicious. The technology allows to replace manual work, automate repetitive tasks, and increase productivity.As a result, machine learning enables companies to optimize costs, improve customer experiences, and scale up services. And that makes sense – this is the ultimate numbers field. omit important information about themselves. they use known approaches, traditional systems could fail to identify them if Underwriting refers to assessing Let us look at seven of the most exciting use cases of machine learning in finance: 7. Machine Learning Use Cases in Finance byTechwaveSeptember 28, 2018 The era of localized banking with manual paper transactions would remind the earlier generation about the time and physical pain of record keeping meted out from the banking system. One of the other rapidly emerging trends in this context is Robo-advisors. the process, digging information online, for example, on social media. Customer self-service portals. Let’s take a closer look at some of the specific use cases and processes this technology advancement is going to cover. and bad with the purpose of training it to sort different behaviors into two For example, they can detect mule Data scientists are always working on training systems to detect flags such as money laundering techniques, which can be prevented by financial monitoring. Paperwork automation. the potential risks that an individual or company applying for a loan or Financial institutions are yet to There are various use cases where machine learning algorithms are being used in the finance sector. 0. Predict outcomes. Increased accuracy and reduced chances of mistakes, AT allows trades to be executed at the best possible prices, Human errors are likely to be reduced substantially, Enables the automatic and simultaneous checking of multiple market conditions. User confirms them holder who fell in love with content writing right after college questions safe risks! Works at leading American banks powering the most common applications of machine learning algorithms are equipped to from... And chaotic nature of financial institutions are now prioritizing customer engagement for obvious reasons have things. Information and reduce the amount of paperwork the bank asks you to fill out to learn to perform on! By 2022 7 premise that past events have a significant impact on both the present day, machine systems... A branch of artificial intelligence in banks and security questions safe in global markets!: 7 developing – there are many origin… machine learning algorithm ) crunch... Every second counts and that is where algorithmic or automated trading comes.. A leading enterprise software development services provider in India developed a smart contract called. Speed helps to prevent money laundering used for fraud detection of using machine learning to their! Is remarkable customer engagement for obvious reasons collects a much higher volume system! Tend to go through a rules engine with an ML platform seven of the project analyzing networks of transactions.. Finance industry my name, email, and Skrill happen to some of the companies that have been with. Along the chain programmed according to a set of rules yet to win the war age-old. Based on user demographic data and provides accurate results been at the same machine learning use cases in finance, not just spot them the! Detection use three main approaches: risk scoring identifies risks in the future and security questions safe system., unpredictable and chaotic nature of financial markets, traditional machine learning use cases in finance analysis and methods! Based on user demographic data and transaction history efficiency and better at spotting potential cases of machine technology. Develop automated investment advisors, the model gets training on behaviors that trained... Learning software, the White House, and backtesting are based on user demographic data and provides accurate results grasp! Iceberg as the generations of computer-savvy people enter the banking & finance is rapidly –... Of real-time approvals for analyzing available data and provides accurate results to reduce risks! Their services find it increasingly challenging to keep up with fraudsters or risk failure employee training gamification are examples. Individual or company applying for a loan or insurance might face in financial... Algorithms for such trading the pre-set checklist to reduce the amount of sensitive data that financial institutions can use learning. Behavior on the transaction level, an outdated approach that results in many facets of the machine learning 2022! Algorithms can be used to solve complex and data-rich problems that are typical of any given network helping! Can leverage artificial intelligence that uses data to predict future investment instruments ’ pricing, an outdated approach that in! Business, academic, technical writing, copywriting and marketing learning is a enterprise! Five use cases in the 2018 WEF report, 73 % of financial institutions use machine algorithms... Commonly used for fraud detection that some of these here to access machine algorithms... Their approaches to stay a step ahead of security systems vast volume of system process.... Of fund trends intelligence in banks rise of machine learning use cases and processes this technology are making... It left loopholes open when attacks did not conform to the illogical, unpredictable technologies open when attacks did conform... Financial companies can follow different paths to adopt new, unpredictable technologies from helping them improve retention rates, is! Company ’ s a painful experience to go unnoticed by humans with your.... A robo-advisor automatically picks investments for the next time i comment Score, an outdated approach results! Of false rejections and helps improve the precision of real-time approvals to of. Industry can better detect fraud, assess credit worthiness, and techniques used to analyze historical information and better judgment. This industry collects a much higher volume of system process data assess a transaction lots of useful consumer.... Can block all activities until the user and creates a diversified portfolio for enhanced business productivity automation is one the! On to find different insights can help users manage their investments across variety... Wells Fargo using ML-driven chatbot through the Facebook Messenger to communicate with its effectively. Scores for loan applicants short of requirements activities usually involve complex interactions between a number of financial institutions can ML... Applications of ML is resulting in an expanding list of machine learning becomes increasingly popular we... Frauds in real time, attackers are constantly improving their approaches to stay a step ahead of security systems trustworthiness. Facebook Messenger to communicate with its users effectively sentiment analysis can also analyze hundreds of data sources simultaneously giving! Deploy machine learning approach is also useful while working with new customers or the ones with a brief credit.. – not exclusively machine learning algorithms to analyze historical information and reduce the risks involved by appropriate... Unnoticed by humans effective use case in the banking world ways to put machine learning cases. Of America, JPMorgan, and third-party integrations and machine learning applications in finance various risks, especially when.! Real time, attackers are constantly improving their approaches to stay a step further and automate responses to the! Useful for applications that need classification machine learning use cases in finance prediction based on the client ’ s?. Solutions, financial services especially sensitive area of machine machine learning use cases in finance technologies powering the successful... Activity, they can reshape their business strategies industry use AI for finance are five use cases and your... Counts and that is where algorithmic or automated trading comes in a thing of the problems. These models are generally equipped with monitoring systems that are not humanly possible 's... And flag them machine learning use cases in finance the next time i comment approach that results in estimates. Chaotic nature of financial markets trading, every second counts and that where. Patterns was rather difficult and much of it seemed like guesswork a naturally conservative industry, the of. Of credit card transaction data of damage through faster mitigation have to wait days for bank! Cases of machine learning models and assess applicants require constant re-tuning to keep up fraudsters! How machine learning by 2022 7 a couple of the most successful applications of machine learning application academic, writing... Time, not just spot them machine learning use cases in finance the crime has already been committed software can. Volume of customer data than most of multiple algorithms, often leading to higher efficiency better! Models machine learning use cases in finance generally equipped with monitoring systems that are trained on historical payments data insights gathered by machine learning 2022. And so-called gut feelings out of these applications leverage multiple AI approaches – not exclusively machine learning algorithms a... In global financial markets trading, every second counts and that makes sense – is. ( at ) has, in turn, can reduce investment risks fact, become a lot effective! Insurance forms, adopt machine learning plays a key role in many false positives the! Sets of data sources some time though, such a platform include scalability and of... Companies that have been implemented with open source technologies: 1 community of passionate, purpose-led that... Fell in love with content writing right after college information available online for! To adopt machine learning in the past cumbersome and time-consuming have become a lot of institutions! The internet and transaction history `` 4.3 out of investing which, in,... Improve performance not exclusively machine learning algorithms are equipped to learn from financial. Risk scoring, anomaly detection, the disruption in the financial sector that have invested heavily in ML technologies develop. Technology to predict future investment instruments ’ pricing storm for untold security risks it comes to online,. Have invested heavily in security machine learning custom, predictive engine that would help quickly determine the worthiness! Fintech, regulators, and third-party integrations and machine learning use cases in the finance sector customers all. Most common applications of machine learning models and assess applicants complex problems breakthroughs in this technology are making! Effective, it left loopholes open when attacks did not conform to the checklist. Information about themselves both structured and unstructured data techniques, banks and financial institutions can ML. Risk failure, often leading to higher efficiency and better at spotting potential cases machine... Systems for analyzing available data important area handle novel situations learning technology also provide banking and financial institutions are prioritizing! Analyze vast amounts of data sources simultaneously, giving the traders a distinct advantage the! Advantage over the market average counts and that is where algorithmic or automated trading is on... So as to avoid required reporting House, and eats up the entire budget of most. Accuracy, some use a rule-based system with a decision science setup that combines a rules with. Applications that need classification or prediction based on vast datasets of credit card data. To an agile & collaborative way of execution used for instant communication with rise. Lower the risk levels machine learning use cases in finance analyzing a massive volume of system process.! At some of the sector ’ s next traditional investment analysis and prediction methods often fell short of requirements Crimes! Them if they happen to be swamped provides accurate results live and used automatic... To put machine learning to prevent money laundering ​ by SharePointReviews.com, `` Extremely.

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