Applications Of Big Data

Applications Of Big Data Has Revolutionized Finance Industry

A new and very popular catchphrase in the realm of information and technology is Big Data. This actually refers to the quantitative methods that involve collection and analysis of huge amounts of data and information that cannot be managed manually. You will see a lot of use of this special technology in hospitals, educational institutions, and several government agencies and most significantly in the finance industry, all of which has to deal with large amounts of data on a regular basis.

Two of the most significant factors that are making big data project an increasingly popular, feasible and economic are:

  • The advancements in computing power and
  • The falling prices thereof.

In fact, the advent and use of cloud computing is further lowering the cost of big data analysis. This has made it accessible and affordable to even the smaller firms as they now do not have to make any substantial capital investments in computing infrastructure.

With the growth of big data and in response to it, data science, a new career category has also sprung up. According to the reports of International Data Corporation, IDC published in Worldwide Semiannual Big Data and Analytics Spending Guide it is found that:

  • The global investment in BDA or Big Data and Business Analytics is about $130.1 billion till last year
  • It is expected to grow up to $203 billion by the end of 2020.

The report suggested that a major chunk of these investments comes from the finance sector that is now inclined more on machine learning, artificial intelligence and now in big data solutions and data management systems.

Big data applications in finance

Using this platform the financial institutions, banks and site like National debt relief.com  and others claim to have been benefited in either one or a couple of the following:

  • Acquiring business intelligence discernments from enterprise data
  • Forecasting the performance of different portfolios as well as financial markets
  • Amplifying the capabilities of cybersecurity teams to prevent anti-money laundering and to detect fraud
  • Predicting and ensuring compliance of the organization with all obligatory regulations and others.

Within the finance service industry, in particular, utilizing big data has a lot of applications as it helps in diverse fields such as:

  • Monitoring and surveillance of the employees
  • Designing different predictive models
  • Helping the loan officers to make proper and accurate lending decisions
  • Helping the insurance underwriters to set premiums and
  • Pricing all illiquid assets such as real estate.

Using big data, the banks and financial institutions can now develop useful algorithms for forecasting the trends and direction of the financial markets. This, in turn, helps them to make proper plans well ahead of time to ensure better and uninterrupted service and business growth.

Different areas benefitted

Big data analytics has helped all different machinery that makes a complete finance sector. If you look deeply into it you will be able to realize it.

  • Auto Insurance: Back in the 1980s, hard data on the driving habits of the individual policyholders was hard to collect and analyze. This made accurate risk measurement and assessment impossible to set an accurate premium on a policy. By 2010 when data collection technology was available, black boxes are installed in the cars to track every data even how fast one typically drives or how typically the brake suddenly.
  • Consumer Credit: Lending decisions are now more foolproof as lenders do not need to rely on the traditional FICO credit ratings Now they can combine it with various social network analysis acquired from various other sources before they make any lending decisions. Information like how frequently a potential borrower changed mobile phone numbers or how they interact with friends offer strong clues to assess how bad is the risk. Typically, people who are most active and show the strongest social connections and community ties are considered as best risks.
  • Small Business Lending: Small businesses are considered to be a risky lending but with big data analytics the predictive models helps the finance institutions to make a proper decision. It helps them to know and asses the quality of relationships they can have with the potential borrowers as well as their own customers.
  • Crop Insurance: With the ability of big data to analyze long term weather patterns it is now easy for the crop insurance providers to deal with the farmers. They can now run huge and better simulations to set up an accurate premium.
  • Mortgage Lending: This is one important part of money lending and the use of big data analysis has helped the banks and financial institutions in a great way. It is now easy to determine more reasonable sales prices for residential and commercial properties that are repossessed due to default mortgages. The tool helps them to analyze different influencing factors such as the local economic conditions, trends in the property markets, suggested sales prices, chances of any disruption in the market or default, minimizing the risks involved, time to hold a property repossessed, credit evaluation and marketing.

Since the financial service providers of this day and age almost entirely operate online, big data has been exceptionally helpful as there are hundreds of data points generated with every single transaction. This huge amount of data turns to be extremely valuable for the finance industries if these are processed and analyzed properly.

Performance based on data than intuition

Most importantly, use of big data has helped the financial services providers to perform on data rather than intuition. With not even an iota of guesswork involved, they can now make smarter and faster decisions.

Therefore, it can be concluded that big data knows which data is useful and valuable and applied the best methods to measure it. Ideally, the foundation of a smart financial dashboard is the clear knowledge and analysis of the different metrics involved.

This helps them to have clear and better insights to discern the past, present and future of a potential borrower as well as the prospects of lending from the business point of view of the money lenders.