USM BUSINESS SYSTEMS

USM BUSINESS SYSTEMS

USM is a pioneer in providing ai & ML solutions that suit's your business needs and help to grow your customer base.

Followers 3
Following 0
In many ways, AI and finance are made for each other.Machine learning and other techniques make it easier to identify patterns that might otherwise not be detected by the human eye, and finance is quantitative, to begin with so that it’s hard not to find traction.Financial firms have also invested heavily in AI in the past, and more are starting to tap into the financial applications of machine learning (ML) and deep learning.Artificial intelligence in stock trading certainly isn’t a new phenomenon, but access to its capabilities has historically been rather limited to large firms.Also Read: AI in Accounting & Finance — How AI Will Impact The Accounting & Finance Industry?AI and machine learning, quantitative investing and tradingEventually, Wall Street, when they looked at AI models, found that by using machine learning they can number crunch millions of data points in real-time and capture some of the correlations that traditional statistics models could not capture, and that is the dollar track to go after today.Especially the deep learning models, a new trend in the last two years.This gets the attention from the big boys on Wall Street, and they are trying to recruit people from Google, from Microsoft, from Apple and IBM Watson, to help them build huge AI clusters, to leverage this technology for trading and investing todayAt the very beginning of the last few years, only some of the very large hedge funds and financial institutions, like Goldman Sachs, were able to gather enough resources to invest in this field.So today it’s still not common knowledge among financial institutions, and Kavout is one of the only firms investing in this direction;I think it’s going to be a very popular space, based on some of the data we see in 2015, in the hedge fund world, the AI-based trading firms are doing pretty well versus the rest of the hedge fund industry is not doing that good.I think in 2016 and 2017, this space is going to get very crowded, but it’s not something everybody can do.ML has been evolving in the last 15 years, and deep learning is a breakthrough technology and helping people to manage lots of data sources and come up with new patterns to help estimate trading, ideas, and make better investing decisions.I think that’s also why you see so many big firms investing in this area, and also you see Apple just acquired an ML company in Seattle, Turi…so not only on Wall Street but also on the traditional big tech companies are moving into this space.We’re facing thousands of stocks to pick every day, it’s a very daunting task; today by using AI, we can do all the number crunching, look at all the news media, the social media, blogs, and also the real-time codes, we can scan thousands of stocks in real-time and give you the best idea, so that’s where the technology is very good today.In our company, we built something see look at all the fundamentals, the technicals, and also momentum for the traders, and we come back with a score to rank every single stock.Now all the traders have so many real-time streaming news, and to mine information from these unstructured data sets becomes very important, so we need new technology to handle this, which is new even to Wall Street, but with ML and deep learning we can now look at all these unstructured data sets and mine lots of trading insights which we could not do beforeWe can do all this today in natural language processing, which means we can have a computer understand the semantics and meaning of how people say something…and in news, this could be something positive or negative about certain companies, and that’s something we call sentiment analysis.We are building something called a sentiment score, which means we are leveraging all the sentiment we collect from traders, news, blogs, and we’re collecting some of the data from transactions.
1
AI is achieved by developing the necessary software and systems so that the machines learn and also enhance from the experience to produce better results.The computer programs for developing AI consists of functions such as logical reasoning, problem-solving, and learning.There are excellent job opportunities in this field and many institutions offer AI learning courses.Goals and Applications:The main motive of AI is to incorporate learning, natural language processing (NLP), reasoning, data presentation, planning, and the capability to control and objects.Their many languages to learn AI such as Python, Java, Prolog, Perl, etc.The code syntax of Python is very easy to understand and its libraries are best suitable to develop machine learning programs.By Python, you can integrate your systems more efficiently in much lesser time as compared with other languages.
Why are chatbots important?A chatbot is often described as one of the most sophisticated and hopeful expressions of interaction between humans and machines.At the same time, they provide new opportunities for companies to improve the customer engagement process and operational efficiency by reducing the typical cost of customer service.By enhancing machine learning and natural language processing, AI-powered chatbots can understand the purpose behind your customers’ requests, calculate their entire conversation history, and interact with their questions in a natural, human way.If you’re currently using a standard chatbot and want to upgrade to an AI-powered one, we’ve put together a list of the best AI chatbots for 2019.A pre-trained Watson assistant with content in your specific industry can understand your historical chat or call logs, search for answers in your knowledge base, ask customers for more clarity, send them to human representatives, and even give you training recommendations for improvement.Bold 360AI Chatbot — Bold 360Trusted by customers like Intuit, Edible Arrangements, and Vodafone, Bold 360 has patented its own natural language processing technology that helps brands build chatbots that understand your customers’ intent without the need for keyword matching and learn how to provide the most accurate answers.Bold 360’s conversational AI can understand complex language, remember the context of the entire conversation, and reply to users with natural responses.You can build Rulai Chatbot from scratch with its drag-and-drop design console, and let its AI suit your customers, or you can run a pre-trained chatbot that delivers data from your specific industry.4.
1
A well-planned and skillfully executed customer engagement strategy maximizes customer satisfaction, loyalty, and advocacy. Businesses no longer rely solely on price or marketing to achieve customer loyalty, they need to build a personalized customer experience that reflects the uniqueness of their brand. The senior vice president for Google Ads said, “It’s never been so hard to please customers and marketers have never had so many opportunities to please them.” The Digital Trends survey conducted by Adobe revealed that companies should focus on improving the customer experience. AI-based customer engagement strategy The State of the Connected Consumer Report, published by Salesforce, reveals that 75% of companies expect companies to use new technologies to create better experiences. AI technology helps to create a robust customer engagement strategy based on data integration, real-time insight distribution, and business context. Intelligent holograms can communicate with users and collect valuable user analytics.
1
Here’s how Artificial Intelligence (AI) is used to solve cybercrime.AI: Flood Gates for Cybercrime:Artificial intelligence is one thing that has enabled cybercriminals like never before, and the Internet of Things relies heavily on it.Also, with the huge expansion in cloud computing, the entire cybersecurity environment is more complex than ever before.As AI SERVICES capabilities become more powerful, it is natural to use AI systems to create new threats and assist existing ones.Also, the ever-increasing impact of AI on the physical world think drones and automobiles may, in theory, lead to very frightening results.Cybersecurity talent shortage:This could not have come at a worse time: there is a huge talent gap in the cybersecurity industry.This shortage of cybersecurity skills means that the demand is astronomical, the prices are high and there are many barriers to access to cybersecurity professionals.These are difficult to overcome, especially for smaller companies.With the utility of web and cloud products and services currently dominating the market, it is very difficult for businesses to find the right talent for their IT needs.Something needs to change, but what?IBM study, what is alarming is that over 90% of cybercrime comes from defects on our behalf and on behalf of end-users.While there are many sophisticated cybersecurity solutions available today, including those that use AI, most major breaches target the human errors that are rooted in our behavior, not just the vulnerabilities and vulnerabilities found in networks and systems.
If you fail to enter the code on the keypad in time, it alerts the police or an offsite security professional to a potential intruder.False alarms are obviously uncomfortable but insufficient, and they can lead to a lack of response when a real security problem occurs.Thanks to AI programming, this has begun to change.With the security system of the future, every aspect of your home care system will be connected through IoT.Once the system has a profile of regular functionality, it can use what it has learned to manage the home security process more effectively.You start to see things like locks, which come together with cameras to detect different visitors, not just knowing the normal times that different people come and go.It allows information program access decisions to be made.The system may also notice that you have forgotten to lock your doors when you leave home and send a reminder to your smartphone.The impact of these cameras has made them more common in home security.
For many decades, scientists have been able to quantify the order of amino acids accurately, but accurately estimating the form of a protein has always been a challenging task.If we can decode the sequence of amino acids in a protein structure and determine the form of a protein accurately, it will provide many functions.Accurate assessment of protein structure is beneficial in understanding the biological evolution of a particular protein and also helps to understand what kind of diseases it causes and what protection it can provide against other diseases.In particular, an accurate assessment of the structure of a protein is crucial to understanding the function of proteins and cells and how they can function and cause disease, and this understanding is also beneficial for the development of treatment and vaccines.Now, if you look at SARS-CoV-2, it is the spike protein structure of the virus that attaches to the AS2 receptor in our cells, causing infection.We were able to develop this understanding very quickly, mainly due to the progress we have made regarding our performance and assessment of protein structures.First DiscoverySee, this field began to develop many decades ago, and in 1972, Christian B. Unfinsen, a scientist, predicted that by accurately calculating the order of amino acids, we could reduce protein formation.So this discovery won him the Nobel Prize in Chemistry, and it laid the foundation for the analysis and evaluation of protein structures.X-ray crystallographyAbout 60 years ago, a scientist named Max Perutz began to evaluate protein structures using experiments.He used X-ray crystallography to determine the exact form of myoglobin and haemoglobin, and this discovery helped us to understand the proper function of haemoglobin in the blood, which transports oxygen from the lungs to the tissues and cells of the lungs.This understanding of the structure of myoglobin and haemoglobin has helped us to understand how a change in a single amino acid can lead to diseases such as sickle cell anaemia.Genome sequencingToday, decoding the sequence of amino acids and completing the line more accurately is even more advanced, thanks to advances in the genetic series.Because at the end of the day, the protein structures in the amino acids are essentially a part of the gene and the rapid advances we have made in the gene sequence have helped us to calculate the order of the amino acids easily.
1
Improved operations, efficient cost management vs. focus on profitability:Banks essentially have to make a profit to survive, and today, banks face significant pressure on their margins.Automation of about 80% of repetitive work processes helps officers dedicate their time to value-added operations that require a high level of human intervention like product marketing.What we need now is not just empowering of banks by automation, but making the entire system intelligent enough to beat the newly emerging FinTech players.It can help in costly and error-prone banking services like claims management by drastically reducing the time spent in reading or recording client information.For instance, JPMorgan Chase’s COiN reviews documents and extracts data from 12,000 documents (which, without automation, would require more than 360,000 hours of work) in just seconds.Lending:A minuscule percentage of the Indian population has an idea of credit.It is also an annoying task for banks to analyze an individual’s creditworthiness due to the lack of credit history.The use of Big Data and Machine Learning to analyze spending patterns and behavioral data of a customer over 10,000+ data points can help banks have an insight into the customer’s creditworthiness.In the case of SME and corporate loans, AI simplifies the complex and critical borrowing process, identify the potential risks in giving the loan by analyzing market trends, prospect’s behavior, and identifies even the slightest probability of fraud.Risk Management And Fraud DetectionThe Punjab National Bank scam exposed the banking sector to an enormous amount of risk and shook the regulators, financial and stock markets, and the banking industry.AI solutions can also be a game-changer by detecting insider trading that leads to market abuse.Insurance underwriting and claims:In this era of bancassurance, customers are more likely to come to banks rather than visit insurance agencies.
1
Artificial intelligence (AI) and machine learning (ML) are emerging fields that will transform businesses faster than ever before.In the digital era, success will be based on using analytics to discover key insights locked in the massive volume of data being generated today.In the past, these insights were discovered using manually intensive analytic methods.AI and ML are the latest tools for data scientists, enabling them to refine the data into value faster.To Know about: How is Robotic Technology Helping the Education Sector?Data explosion necessitates the need for AI and MLHistorically, businesses operated with a small set of data generated from large systems of record.The challenge for businesses is that there is far too much data to be analyzed manually.The only way to compete in an increasingly digital world is to use AL and ML.AI and ML use cases vary by verticalAI and ML apply across all verticals, although there is no universal “killer application.” Instead, there are a number of “deadly” use cases that apply to various industries.Common use cases include:Healthcare — Anomaly detection to diagnose MRIs scans fasterAutomotive — Classification is used to identify objects in the roadwayRetail — Predictions can accurately forecast future salesContact center — Translation enables agents to converse with people in different languagesThe right infrastructure, quality data neededRegardless of the use case, AI/ML success depends on making the right infrastructure choice, which requires understanding the role of data.
The Upper Marlboro, Md.-based practice, which has a total of six CPAs and 15 employees, is using artificial intelligence (AI) to identify high-risk transactions as part of its auditing process.I’m excited because I’ve never had access to this much technology.We recently implemented Artificial intelligence for auditing.The AI we use is machine learning where the machine has built-in algorithms that help it learn based on transactions it is fed.It shows the risk at the transaction level.To Know about: How is Robotic Technology Helping the Education Sector?I started looking into AI several years ago when I was on the AICPA governing Council.AICPA President and CEO Barry Melancon, CPA, CGMA, described how the Big Four firms were spending millions of dollars on AI.This is a huge opportunity for small firms: We don’t have to lose our competitive advantage, and we’re not stuck on legacy platforms.For over two years I searched for a suitable AI platform.Some we could not afford.I sent them general ledgers, and it took two weeks to get access to the risk report on the client dashboard.The price depends on the size of the audit practice, but even if you want to try it out with one client, it’s worth getting your feet wet.We now have a competitive advantage.I work with not-for-profits that use QuickBooks Online and other cloud-based software.
1
They look too complicated for a commoner.All of these buzzwords are similar to those of a business executive or a student from a non-technical background.In this blog, we will explain these techniques in simple terms so that you can easily understand the difference between them and how they are used in business.Read more: AI & ML Use Cases Across Eight IndustriesWhat is Artificial Intelligence (AI)?Artificial intelligence refers to the simulation of the human brain function by machines.This is achieved by creating an artificial neural network that can show human intelligence.The essential human functions that an AI machine performs are logical reasoning, learning and self-correction.General AI refers to the intelligent transformation of machines into a broader range of thought and reasoning activities.
2
The most important technologies in 2021 will be AI, 5G, and IoTMore specifically, nearly one-third (32%) of respondents cited AI and machine learning, followed by 5G (20%) and IoT (14%).CIOs and CTOS surveys show that manufacturing (19%), healthcare (18%), financial services (15%), and education (13%) are the industries most people believe will be affected by technology in 2021.Not surprisingly, COVID-19 is improving companies, including IEEE member Carmen Fontana and Cloud and Evolving Technology Lead in Centric Consulting.Technology, acceleration, and disaster preparedness due to COVID-19CIOs and CTOs have surveyed the adoption of certain technologies due to the epidemic:Respond More than half (55%) of respondents accelerated cloud computingG accelerated 52% 5G adoption.51% accelerated AI and machine learningHave an idea of developing an AI Project ?Visit us @ Award Wining AI Services Provider
From electronic trading platforms to medical diagnosis, robot control, entertainment, education, health, and commerce, Artificial Intelligence (AI) and digital disruption have touched every field in the 21st century.It has also enabled users to make faster and more informed decisions with an increased amount of efficiency.Of late, the banking sector is becoming an active adapter of artificial intelligence — exploring and implementing this technology in new ways.The penetration of artificial intelligence in the banking sector had been unnoticed and sluggish until the advent of the era of internet banking.One of the first steps was taken in 2015 by Ally Bank (USA) — introducing Ally Assist — a chatbot that could respond to voice and text, make payments on behalf of the customer, give an account summary, monitor savings, spending patterns, and use natural language processing to understand and address customer queries.Banks all over the world followed up with their best versions of chatbots: Erica to iPAL, Eva and the most famous one — SBI’s SIA.According to Payjo (the start-up which developed SIA), SIA can handle up to ten thousand inquiries per second and is one of the world’s largest deployments of artificial intelligence in consumer-facing banking.Banks are increasingly spending on artificial intelligence and ML in data analytics for personalized and faster customer experiences to garner the interests of the tech-savvy and the millennial class.To Know About: Rise of AI-Powered Chatbots in the Banking IndustryAccording to the FinTech Trends India Report by PwC in 2017, the global spending in artificial intelligence has touched $5.1 billion.It includes applications and payment interfaces, digital wallets, chatbots, or interactive voice response systems.
1
Artificial Intelligence has received a lot of focus and attention in the last couple of years.There has been a boom in the innovations that have artificial intelligence at its base.Obviously, the internet has played a crucial role in the development of artificial intelligence-enabled services.Machine learning essentially an artificial intelligence technique, has been stirring new developments by creating new algorithms which mimic or support human behavior or decision-making capabilities, which are already in use, like Apple’s Siri, or the email servers which eliminate junk or spam emails.You can also see the use of machine learning in e-commerce websites which use it to personalize the search or use of web experience of their customers.It is interesting to comprehend the capabilities of machines.Very soon machines will have the capability to perform advanced cognitive functions, processing language, human emotions, the machines will be proficient in learning, planning or performing a task as intelligent systems.There is also a definite possibility that the tasks performed will be or can be more accurate than humans, thus artificial intelligence can boost productivity and accuracy, and impact economic growth.
1
Improving decision-making for loans and creditSimilarly, banks are using AI-based systems to help make more informed, safer, and profitable loan and credit decisions.In addition to using data that are available, AI-based loan decision systems and machine learning algorithms can look at behaviors and patterns to determine if a customer with limited credit history might in fact make a good credit customer or find customers whose patterns might increase the likelihood of default.The challenge with using AI-based systems for loan and credit decisions is they can suffer from bias-related issues similar to their human counterparts.Financial institutions operate under regulations that require them to issue explanations for their credit-issuing decisions to potential customers.This makes it difficult to implement tools built around neural networks, which operate by teasing out subtle correlations between thousands of variables that are typically incomprehensible to the human mind.AI in banking is being applied to these processes to eliminate much of the time-intensive and error-prone work involved in entering customer data from contracts, forms, and other sources.Improved handwriting recognition, natural language processing, and other technologies, combined with intelligent process automation tools, are being used more and more in back-office operations to handle a wide range of banking workflows.In addition, by replacing these human processes with AI-based automation, banks can impose audit and regulatory control where it previously has been unable to do so.
It’s boggling that the bulk of the world’s wealth is stored in databases, and transactions are simply the exchanges of information over networks.As impressive — or scary — as that might sound, artificial intelligence technologies aim to further revolutionize the way banking is done and the relationships between banks and their customers’ experience.Always-on chatbot sidesteps banking hoursThere’s a reason why people deride banking hours.Our money doesn’t sleep, so why should the banks?Fortunately, AI in banking is one of the most impactful applications of artificial intelligence through the use of conversational assistants, or chatbots, to engage customers 24/7.Customers are increasingly comfortable with chatbots handling many things, even private conversations regarding bank transactions, bank services, and other tasks that don’t necessarily require human intervention.For example, Bank of America introduced Erica as a virtual assistant to help with customer transactions, and that has shown significant positive ROI.Many banks have quickly followed suit, although some with mixed results.In addition to fielding customer service inquiries and conversations about individual transactions, banks have been finding good results in using chatbots to make their customers aware of additional services and offerings.For example, business customers might not be aware of merchant services and loan offerings that can help resolve payment or credit issues.
2
In the early days, the education setup was about a tree, chalk, and slate with a change of time, restored as a luxury space that had everything to do with human effort in terms of learning.Today, most of all, the whole focus is shifting towards innovation, creativity, and technological advancement.Previously restricted to companies and laboratories, it is now a boon for young adults to effectively nourish their minds without technical side effects.Educational robotics is a useful tool in early and special education.Social and personal skills can be developed through educational robotics.Impact on Formal Education:This discovery has had a major impact on the formal education system of the country.On the other hand, this has led to less mental exercise of the human mind, which is considered essential for full growth.On the bright side, this allows a person to learn faster and keep up with the rapid growth rate of innovation.This type of learning has proven to make technology and programming more enjoyable.
1
That may seem like a cliché, or hype, or buzz, but it is true.The tech is fundamentally changing the way packages move around the world, from predictive analytics to autonomous vehicles and robotics.Here are the top five ways in which Artificial Intelligence is transforming the logistics industry as we know it:Predictive Capabilities Skyrocket When AI in Logistics is ImplementedThe capabilities of AI are seriously ramping up company efficiencies in the areas of predictive demand and network planning.Having a tool for accurate demand forecasting and capacity planning allows companies to be more proactive.By knowing what to expect, they can decrease the number of total vehicles needed for transport and direct them to the locations where the demand is expected, which leads to significantly lower operational costs.The tech is using data to its full potential to better anticipate events, avoid risks, and create solutions.This allows organizations to then modify how resources are used for maximum benefit — and Artificial Intelligence can do these equations much faster and more accurately than ever before.In general, predictive analytics solutions in the logistics and supply chains are on the rise.The most well-known examples are Transmetrics and ClearMetal, which were both mentioned in the latest DHL’s Logistics Trend Radar.AI analysis can also be used to safeguard against risk.Another good example from DHL is its platform which monitors more than 8 million online and social media posts to identify potential supply chain problems.
2
Machine learning could become a new weapon in the fight against Medicare fraud.Machine learning can be a useful tool in detecting Medicare fraud, according to a new study that can recover anywhere from $ 19 billion to $ 65 billion lost in fraud each year.Researchers at Florida Atlantic University’s College of Engineering and Computer Science recently published the world’s first study using Medicare Big data, machine learning, and advanced analytics to automate fraud detection.They tested six different machine learners on balanced and unbalanced data sets and eventually found that the RF100 Random Forest algorithm would be most effective in detecting potential cases of fraud.They found that unbalanced data sets are more than balanced data sets when scanning for fraud.There are many implications in determining what fraud is and what is not, such as clerical error,” says Richard A. Bowder, senior author and Ph.D.“Our goal is to allow machine learners to know all this data and flag anything suspicious.Then we can alert researchers and auditors, who should focus on 50 cases instead of 500 cases or more.”In the study, Bowder and colleagues examined Medicare data, covering 37 million cases from 2012 to 2015, for incidents such as patient abuse, neglect, and billing for medical services.The team has reduced the data set to 3.7 million cases, which is still a challenge for human researchers charged with pinpointing Medicare fraud.The authors used the National Provider Identifier — a government-issued ID number for health care providers to compare fraud labels with Medicare Part data, which includes provider details, payment and charge information, policy codes, all policies, and medical specifications.When researchers compared NPI with Medicare data, they flagged fraudulent providers in a separate database.“If we can accurately assess the physician’s uniqueness based on our statistical analyses, then we can detect exceptional physician behaviors and flag as much fraud as possible for further investigation,” said Tagi M. Khoshgofthar, Ph.D., co-author, and professor at the school.So, if a cardiologist is wrongly labeled a neurologist, it is a sign of deception.However, the data set remains a challenge.A small number of fraudulent providers and a large number of onboard providers have made data imbalance that can fool machine practitioners.
You are looking at the latest home security technologies so that you can protect yourself from the threat.Security systems of the past have used common sensors and alarms to detect intruders, and they consult a team of security experts in case something goes wrong.Many people have returned from holiday or business meetings, knowing that this is only a false alarm.Your home will notify you when the move or the door is open and watching the live feed will allow you to determine if you are facing a real threat or a false alarm.Some police departments have reported that 90 percent of alarms are false.Giving you the power to disable the alarm when there is no threat will save you time and money, but if you do not respond quickly, the software will contact the authorities, keeping you out of harm’s way.Facial recognitionWhile past security systems can always detect weapons and movements when their alarm is heard, facial recognition can reduce the number of false alarms you experience with AI.The cameras of your security system can detect your face and the faces of the people you invite to your home on a regular basis.If your alarm and your children or spouse come home, your security system will not contact the police until they realize they are allowed in your home.On the other hand, if the face is not allowed in front of you, it will send help.
2
More

Top