How to Balance Your Media Diet

 

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We Hunger for Ideas
A defining aspect of humanity is that we are driven, fed and led by ideas as well as food.

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Top 7 Data Science Use Cases in Finance

AnalyticsAnywhere

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. Companies want to know more what improvements the technologies bring and how they can reshape their business strategies.
To help you answer these questions, we have prepared a list of data science use cases that have the highest impact on the finance sector. They cover very diverse business aspects from data management to trading strategies, but the common thing for them is the huge prospects to enhance financial solutions.
Automating risk management
Risk management is an enormously important area for financial institutions, responsible for company’s security, trustworthiness, and strategic decisions. 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.
There are many origins from which risks can come, such as competitors, investors, regulators, or company’s customers. Also, risks can differ in importance and potential losses. Therefore, the main steps are identifying, prioritizing, and monitoring risks, which are the perfect tasks for machine learning. With training on the huge amount of customer data, financial lending, and insurance results, algorithms can not only increase the risk scoring models but also enhance cost efficiency and sustainability.

AnalyticsAnywhere2

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. Companies want to know more what improvements the technologies bring and how they can reshape their business strategies.
To help you answer these questions, we have prepared a list of data science use cases that have the highest impact on the finance sector. They cover very diverse business aspects from data management to trading strategies, but the common thing for them is the huge prospects to enhance financial solutions.
Automating risk management
Risk management is an enormously important area for financial institutions, responsible for company’s security, trustworthiness, and strategic decisions. 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.
There are many origins from which risks can come, such as competitors, investors, regulators, or company’s customers. Also, risks can differ in importance and potential losses. Therefore, the main steps are identifying, prioritizing, and monitoring risks, which are the perfect tasks for machine learning. With training on the huge amount of customer data, financial lending, and insurance results, algorithms can not only increase the risk scoring models but also enhance cost efficiency and sustainability.

Among the most important applications of data science and artificial intelligence (AI) in risk management is identifying the creditworthiness of potential customers. To establish the appropriate credit amount for a particular customer, companies use machine learning algorithms that can analyze past spending behavior and patterns. This approach is also useful while working with new customers or the ones with a brief credit history.

Although digitalization and automatization of risk management processes in finance are in the early stages, the potential is extremely huge. Financial institutions still need to prepare for this change by automating core financial processes, improving analytical skills of the finance team, and making strategic technology investments. But as soon as the company starts to move in this direction, the profit will not make itself wait.

Managing customer data

For financial firms, data is the most important resource. Therefore, efficient data management is a key to business success. Today, there is a massive volume of financial data diversity in structure and volume: from social media activity and mobile interactions to market data and transaction details. Financial specialists often have to work with semi-structured or unstructured data and there is a big challenge to process it manually.

However, it’s obvious for most companies that integrating machine learning techniques to managing process is simply a necessity to extract real intelligence from data. AI tools, in particular, natural language processing, data mining, and text analytics, help to transform data into information contributing in smarter data governance and better business solutions, and as a result – increased profitability. For instance, machine learning algorithms can analyze the influence of some specific financial trends and market developments by learning from customers financial historical data. Finally, these techniques can be used to generate automated reports.

Predictive analytics

Analytics is now at the core of financial services. Special attention deserves predictive analytics that reveals patterns in the data that foresee the future event that can be acted upon now. Through understanding social media, news trends, and other data sources these sophisticated analytics conquered the main applications such as predicting prices and customers lifetime value, future life events, anticipated churn, and the stock market moves. Most importantly such techniques can help answer the complicated question – how best to intervene.

Real-time analytics

Real-time analytics fundamentally transform financial processes by analyzing large amounts of data from different sources and quickly identifying any changes and finding the best reaction to them. There are 3 main directions for real-time analytics application in finance:

Fraud detection

It’s an obligation for financial firms to guarantee the highest level of security to its users. The main challenge for companies is to find a good fraud detecting system with criminals always hacking new ways and setting up new traps. Only qualified data scientists can create perfect algorithms for detection and prevention of any anomalies in user behavior or ongoing working processes in this diversity of frauds. For instance, alerts for unusual financial purchases for a particular user, or large cash withdrawals will lead to blocking those actions, until the customer confirms them. In the stock market, machine learning tools can identify patterns in trading data that might indicate manipulations and alert staff to investigate. However, the greatest thing of such algorithms is the ability of self-teaching, becoming more and more effective and intelligent over time.

Consumer analytics

Real-time analytics also help with better understanding of customers and effective personalization. Sophisticated machine learning algorithms and customer sentiment analysis techniques can generate insights from clients behavior, social media interaction, their feedbacks and opinions and improve personalization and enhance the profit. Since the amount of data is enormously huge, only experienced data scientists can make precise breakdown.

Algorithmic trading

This area probably has the biggest impact from real-time analytics since every second is at stake here. Based on the most recent information from analyzing both traditional and non-traditional data, financial institutions can make real-time beneficial decisions. And because this data is often only valuable for a short time, being competitive in this sector means having the fastest methods of analyzing it.

Another prospective opens when combining real-time and predictive analytics in this area. It used to be a popular practice for financial companies have to hire mathematicians who can develop statistical models and use historical data to create trading algorithms that forecast market opportunities. However, today artificial intelligence offers techniques to make this process faster and what is especially important – constantly improving.

Therefore, data science and AI made a revolution in the trading sector, starting up the algorithmic trading strategies. Most world exchanges use computers that make decisions based on algorithms and correct strategies taking into account new data. Artificial intelligence infinitely processes tons of information, including tweets, financial indicators, data from news and books, and even TV programs. Consequently, it understands today’s worldwide trends and continuously enhances the predictions about financial markets.

All in all, real-time and predictive analytics significantly change the situation in different financial areas. With technologies such as Hadoop, NoSQL and Storm, traditional and non-traditional datasets, and the most precise algorithms, data engineers are changing the way finance used to work.

Deep personalization and customization

Firms realize that one of the key steps to being competitive in today’s market is to raise engagement through high-quality, personalized relationships with their customers. The idea is to analyze digital client experience and modify it taking into account client’s interests and preferences. AI is making significant improvements in understanding human language and emotion, which brings customer personalization to a whole new level. Data engineers can also build models that study the consumers’ behavior and discover situations where customers needed financial advice. The combination of predictive analytic tools and advanced digital delivery options can help with this complicated task, guiding the customer to the best financial solution at the most opportune time and suggesting personalize offerings based on spending habits, social-demographic trends, location, and other preferences.

Conclusion

For financial institutions, the usage of data science techniques provides a huge opportunity to stand out from the competition and reinvent their businesses. There are vast amounts of continuously changing financial data which creates a necessity for engaging machine learning and AI tools into different aspects of the business.

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. If you have any further ideas, please share your vision in the comment section.

Source:activewizards.com

Machine Learning Provides Competitive Edge in Retail

OnTheGo

A simple concept behind machine learning is proving that computation software can access a dataset and create results from that access. That concept also serves as the most crucial element in providing meaningful personalized service for customers.

In marshaling its resources, Amazon has begun to school retailers and search engines on how crucial an element machine learning is to a competitive environment.

Several Amazon advertising services are starting to rival Google in a significant business model for Google, online advertising. Amazon has been long offered Product Display Ads that feature product images and text that relate to people’s searches. It just launched a few advanced advertising services such as a cloud-based header bidding service, according to MarTech.

More to the point of machine learning, Amazon is now beefing up services related to this technology. The company has announced a new program that will allow developers to build and host most Alexa skills using Amazon Web Services for free. It also introduced three new AI services — Amazon Rekognition, which can perform image recognition, categorization, and facial analysis; Amazon Polly, a deep learning-driven text-to-speech (TTS) service; and Amazon Lex, a natural language and speech recognition program. The initiatives will bolster Amazon Web Services (AWS) against Microsoft and Google.

These product milestones for Amazon occur as the retail industry — the most frequent users of personalization ads — confront a complex puzzle of tech and trends. Retailers face a massive distribution transformation. Retailers are shifting away from their traditional locations. For instance, mid-level malls are losing stores such as JCPenney’s, Sears, and Macy’s. Other retailers are experimenting with smaller stores and kiosks in an effort to adjust their floor space. Even once online-only retailers such as Warby Parker and — you guessed it, Amazon — have added small brick-and-mortar stores to establish a cohesive consumer experience.

Changing Consumer Behaviors
Changing consumer digital behaviors are adding to the challenge for retailers. Behaviors such as “webrooming” and “showrooming” have become more popular over the last five holiday shopping seasons, and now have become standard activities. Webrooming and showrooming are when shoppers visit physical stores but use their smart phones to comparison shop and check competitive prices, and even place orders with a store’s competitior. The adoption of these behaviors meant retailers had to improve their mobile sites, launch apps, examine beacons, and consider virtual reality to create a customer experience that supports the brand and retains sales.

All of this has raised the bar for correlating data variety for trends — new sources, new contexts, and new intentions, all at different times. Managers who had just converted to the church of analytics now must listen to a new measurement sermon: where does machine learning fit within their business? And because of Amazon, retail managers are experiencing an urgency to learn machine learning protocols and also plan how to execute strategy in a world becoming dominated by a giant competitor.

Through its operational prowess, scale of services, and inroads into IoT devices and cloud solutions, Amazon has positioned itself to make a myriad of correlations between business metrics and technical metrics. I mentioned in recent posts that nascent search activity emerging from Amazon site visitors is rivaling search engines as a consumer starting point for researching products and services. Amazon can now take significant advantage with machine learning. Much of machine learning relies on data preparation, addressing data quality such as treating missing variables. Amazon has an opportunity to provide better context with the search conducted, and play a central role with partners who want to better understand how their products are received.

Amazon can then leverage its discoveries into meaningful customer and business value. A potential example is implementing tactics influenced by BizDevOps, a blend of front-end software development with business development and operations tactics. Its purpose is to align app development to customer and business value in upfront planning. That alignment has become critical as analytics has shifted from singular inferences from website activity into a central measurement of various activity across digital media and IoT devices. If you do a Google search, you’ll find more than a few posts on the topic of BizDevOps mentioning Amazon as a model example.

Retail’s Machine Learning Future
Amazon’s potential with machine learning is a long way from the early years when Wall Street analysts criticized the once-only-a-book retailer about its quarterly losses. Amazon’s machine learning potential also has far reaching implications.

Amazon’s interest in personalization ads and growing machine learning prowess is tantalizing to supporters of programmatic advertising, which aims messages and gains access to highly targeted, highly valued audiences. Marketers can better predict how ad creative, products, and services can be combined to better appeal to customers in different cycles of the customer experience or a sale. Amazon can ultimately play a central role with platform partners who want to better understand how their products are received.

If this Amazon news makes your strategic team feel that they are behind the curve, take heart. The good news is that machine learning is in its early stages with retailers seeking ways to integrate data and devices that produce the data. Retailers turn to Google for search and paid ads because it covers a large number of industries, so Amazon will remain a retail niche for now.

But if business managers want to find potential success like Amazon has found, they must look internally with technology teams to see how machine learning techniques can be the operational glue between business resources and personalized experience for customers.

Source: allanalytics.com

600 million Samsung galaxy users left vulnerable.

zyrobose

Samsung exploit leaving 600 million users vulnerable

Yep…
Yet again another serious problem for mobile devices.
However this time its not Apple and instead its Samsung who is to blame.
Six hundred million (600m) users of Samsung Galaxy Devices are vulnerable to a hack capable of reading incoming and outgoing text messages,spying on the user via the camera,listening in from the microphone and also install applications (malicious ones which can cause even more harm.)
A simple problem in the Samsung repackaged version of Swift Key keyboard (which is used on Samsung devices) is at fault for this serious exploit.
Hackers can get in the way of the server that the keyboard requests updates to and cause all of these problems for users by sending malicious code back to the device.
Users cannot do much about this as of yet until Samsung releases either a software update or an app update…

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