Article | April 3, 2020
Primarily,the IoT stack is going beyond merely ingesting data to data analytics and management, with a focus on real-time analysis and autonomous AI capacities. Enterprises are finding more advanced ways to apply IoT for better and more profitable outcomes. IoT platforms have evolved to use standard open-source protocols and components. Now enterprises are primarily focusing on resolving business problems such as predictive maintenance or usage of smart devices to streamline business operations.Platforms focus on similar things, but early attempts at the creation of highly discrete solutions around specific use cases in place of broad platforms, have been successful. That means more vendors offer more choices for customers, to broaden the chances for success. Clearly, IoT platforms actually sit at the heart of value creation in the IoT.
Article | April 3, 2020
The financial industry has been going through digital transformation for years. Digital technologies have helped to automate manual and tedious tasks like processing and reporting of historical data to forecasting and financial predictive analytics.
The financial services industry owes its success to data. Data is constantly evolving in the form of market trends, client investment, customer service, campaigns. Data gives a boost to banking strategies. As reported by Accenture in a recent survey, 78 percent of banks have made the shift to using data for operations; however, only seven percent of them have extended to using predictive analytics in finance.
Predictive analytics in finance has had a slow but steady start. It is an area of growing interest for banks and other institutions as new newer technologies launch in the market. To complete your company’s digital transformation, data analytics in finance will make a difference in that process.
To be successful, organizations must have the ability to adapt to changes.
Having predictive analytics on your side, your organization can deal with ever-changing circumstances with less to no difficulty.
Understanding Predictive Analytics: What is it?
Predictive analytics is a process of interpreting data to measure any possible future outcomes. It is carried out with the help of statistical modeling, historical data sets, and machine learning. The collected historical data is fed into an algorithm that recognizes patterns and forecast trends and possible future behavior from days to years in advance.
Analyzing historical data and predicting the future has been an old practice in the finance sector. Banks and financial institutions have been evaluating past events or historical data for a long time now.
Making precise forecasts in trends and analyzing data becomes easier due to predictive analytics.
There is a wider scope to predictive efforts with more speed and accuracy and apply them throughout strategic and tactical business practice areas.
Predictive Analytics in the Financial Sector: What are the Benefits?
Many organizations are ready to accept the positive applications of predictive analytics but remain skeptical about the return on investment.
It is worth understanding the potential of predictive analytics to any business big or small. It doesn’t matter if you are not in the banking sector to benefit from taking a peek into the future of financial performance.
Any finance and accounting department can take advantage of advanced predictive analytics for the following reasons:
The technology keeps a regular track of the consistency between expectations and reality to warn you about possible gaps.
Analytics accurately helps you identify any possible threats to your business and warns you.
Enhanced User Experience
Predictive analytics guides you to recognize the strengths of your business and lets you know how to maximize customer satisfaction.
Analyzed Decision Making
You can understand your customers better with predictive analytics. With this information, you can correctly match your customers with the product in a better way.
Importance of Predictive Analytics
Most successful banking and financial institutions depend on predictive analytics because it simplifies and integrates data to increase profits for companies. Predictive analytics can improve different finance processes.
But the importance of analytics goes beyond just banking services and actually goes into a better quality of customer service. Better customer service is only possible because of the advanced technology that shares customer feedback and preferences throughout the organization, in turn giving relevant information to every employee to make necessary product enhancements.
To understand the importance of predictive analytics, below are some of its use cases:
Predictive analytics in financial institutions and banking give you a complete profile of your customer base. It is impossible to contact every customer and interview them about their likes, needs and wants. This is where big data analytics in finance comes into play. It gives you the whole information about your customers regardless of the services they subscribe.
Customers usually don’t have the same needs throughout their lives. As they grow older and have families, their financial needs change accordingly. For instance, a young person considering getting married will always try and save monetarily to buy a house, life insurance, college funds, whereas an older couple will save that money for their retirement.
Apart from enabling different financial services, predictive analytics empowers you to serve individual customers with ease. Let’s take an example. When a customer applies for a loan, predictive financial services can help you analyze if the customer can repay the loan.
Predictive analytics also helps offer alternative services like secured loans to customers who may not qualify for the originally applied services.
Online Banking Made Better
Consumer interest fluctuates in spikes. Predictive analytics informs managers enough in advance so they can set up online infrastructures in those areas. Predictive analytics has made it easier to identify a possible customer base. For example, it can provide metrics to the marketing teams. In turn, the marketing teams can target the customers with ads for probable mortgage loans or business loans in hopes of converting them into their customers.
Data analytics in finance also helps in preventing and detecting fraud and abuse. Although detecting fraud doesn’t necessarily fall under predictive analytics, it can inform the IT department about potential scammers and which online services must be protected.
Foreseeing Market Variations
Predictive analytics can predict market variations and changes. By combining internal and external data, your organization can predict revenue growth in particular market sectors.
For nascent or growing companies, predicting market changes is an important ability. Profitable companies should also be reviewed through predictive analytics to generate demand projections owing to the uncertainties caused by the Covid-19 pandemic. Your return on investment can grow or reduce even with the minutest changes to the growth plans that would seriously impact investor confidence in the future.
Predictive analytics also help to establish which marketing campaigns are working and which strategies need to change.
Predictive Analytics and the Future: What Next?
Technological improvements have allowed predictive analytics in finance to improve and change constantly. Any organization can use customized data solutions to meet your customers’ needs and reach new ones efficiently. Your organization can use predictive analytics to move your business and products ahead and understand how the market will thrive, giving you the much needed heads up you would need to change your strategies and tactics.
Frequently Asked Questions
Is predictive analytics is the future of finance?
Predictive analytics is called the ‘future of financial software,’ which means it can provide accurate planning and cost-effectiveness.
How can analytics be used in finance?
Analytics helps in predicting revenue, improve supply chains, identify trouble spots, understand where the company is bleeding money, and fraud detection.
How do predictive analytics benefit financial institutions?
Predictive analytics can help financial institutions and customers detect fraud, financial management, predicting markets, improving products, better user experience, etc.
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"name": "How do predictive analytics benefit financial institutions?",
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Article | April 3, 2020
DevOps will provide over-the-air (OTA), seamless software updates which would allow important and immediate updates without affecting the car’s capabilities through Liquid Software liquid software. OTA updates will enable automakers to fix engine and automotive malfunctions, as well as implement safety standards directly into the program. Tesla is one of the pioneers of over-the-air updates but while its’ cars are off. In total, Tesla’s updates are usually about 30 minutes. Since 2012, hundreds of OTA updates have been sent out by the company to adjust things like speed limit settings, acceleration, battery issues, and even braking distance. Most car manufacturers are behind when it comes to over-the-air software updates.
Article | April 3, 2020
Powerful technologies and expertise can help provide better data and help people better understand their situation. As the world contends with the ongoing coronavirus outbreak, officials battling the pandemic need tools and valid information at scale to help foster a greater sense of security for the public. As technologists, we have been heartened by the prevalence of projects such as Call for Code, hackathons and other attempts by our colleagues to rapidly create tools that might be able to help stem the crisis. But for these tools to work, they need data from sources they can validate. For example, reopening the world’s economy will likely require not only testing millions of people, but also being able to map who tested positive, where people can and can’t go and who is at exceptionally high risk of exposure and must be quarantined again.