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In this article, we will discuss some of the trends indicating the future of big data in the financial sector.
Big data analytics has been around for over ten years now. The early adopters have learned from mistakes and shown how companies can actually profit from using big data analytics.
Future of Big Data in Finance Sector
Moving Beyond the Hype
The hype is settling down now for big data. CIOs are not as willing to spend millions just on experimentation, they want to know how the returns are going to roll in.
Taking a Reality Check
One common mistake is overestimating what data insights can reveal. Overreliance on a single data source such as sentiment analysis can result in disaster. Executives and data scientists are now realizing that big data analytics isn’t a solution to everything, and a little bit of humility is called for.
Artificial intelligence and machine learning are closely linked with data science. To effectively understand structured data and unstructured data, algorithms need to be smart and learn from the past.
Many of the tech giants like Google, Microsoft, IBM, and Amazon are now offering machine learning as a service. That means everyone now has access to the type of machine learning that powers Google searches.
This is important, as now you don’t have to be a huge enterprise to have access to these technologies.
Keeping Data Secure
Regulators are getting better at understanding big data and enforcing that it is used responsibly. There will be big consequences for firms that don’t do their best to keep data safe. These will come from both governments in the form of fines and penalties, and customers in the form of trust.
This is becoming even more difficult with the emergence of the internet of things. The financial institutions that take this seriously will thrive.
Moving to the Cloud
The IT world is moving to the cloud, and financial companies are the ones leading the way. Cloud computing is a large factor that has driven big data applications. It allows for computing on a scale not possible before.
The enormous data sets can be distributed among thousands of servers in a data center and worked on by thousands of machines at a time. Financial institutions are now beginning to trust the public cloud more than before.
Developers and Data Scientists
It’s becoming a competitive necessity for most companies to start using data science to function.
But, as a relatively new discipline, there simply aren’t enough skilled workers in the field. Financial institutions need to be able to deal with it. To fill this void, many companies are turning to machine learning to analyze their data in bulk.
Financial Startups and Challenger Banks
The blistering pace of technological advancement is disrupting almost every industry in the world, and financial services are no different. Startups and smaller companies have less historical weight to carry, and they can adopt technology faster and with greater precision.
Many financial startups are using big data analytics to offer unique services to customers that the big boys just can’t right now.
Zopa is a peer-to-peer lending platform. Instead of borrowing money from a bank or credit union, you can borrow from thousands of individuals who are ready to lend. This is in direct competition to the bank’s way of doing things.
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Smaller, challenger banks like Atom and Metro Bank are also taking advantage of technology to put themselves in positions to compete. They are leveraging this new technology to win over customers.
Future of Big Data in Finance Sector? It is Here to Stay
A tidal wave of data is being generated at a faster pace every year owing to the digital transformation of every industry- in every form imaginable. Within this new and valuable data lies valuable knowledge about the way the world currently works, what people think, and what is going to happen next.
Data analytics is the secret to finding it.
The future of financial services will be dominated by big data analytics. It’s no longer an experiment, the benefits of actionable data are huge and they are being enjoyed by companies all over the world.
Now, the tools are available for smaller companies to get in on the action. If you need assistance from data professionals in using big data technology for your fintech business, DevTeam.Space can help you with an exceptional team of data engineering developers.
You will need data engineers to better understand your raw data through data exploration techniques like frequency count, heatmaps, correlation, segmentation, etc., and improve data quality for better results. Your chief data officer should also be able to suggest you suitable data platforms for data storage, etc.
Your team of software developers should be experts in using various cloud solutions and analytics tools that help in processing data efficiently. Different AI techniques, including deep learning models, are used today to find data patterns and important data points faster.
Write to us your initial big data and fintech development requirement through this link, and one of our technical managers will contact you for discussing further project details.
Top FAQs on Future of Big Data Trends in the Financial Services Industry
Some trends in the finance industry that show the future of big data include companies understanding the worth of big data analytics for numerous purposes from customer satisfaction to business development, the popularity of artificial intelligence and machine learning algorithms, and advanced computing resources for big data processing through cloud computing services.
Digital identity management, fraud detection, intrusive data traffic, customer’s sensitive data security and privacy, heavy regulations, and penalties in case of non-compliance, a requirement of expert data engineering skills are some of the major challenges in the way of using big data in the financial services industry.
Big data analytics is helping fintech companies to grow their business in multiple ways. Finance companies analyze data and better understand their customers and plan business development strategies accordingly. This is not possible without making use of the market data.