HUMANS ARE CREATING DATA AT AN AMAZING RATE, AND FINANCIAL SERVICE EXECUTIVES NEED TO BE ABLE TO ADAPT TO THE COMPLEXITY OF BIG DATA IN FINANCE.
Data is growing at an alarming rate. According to the World Economic Forum, we will be able to produce 463 gigabytes per day in 2025.
Big Data simply refers to the ability to interact with large quantities of data in many different ways. Big Data in finance is the latest financial buzzword. What does this actually mean? Big Data is a collection of technologies and methods used to collect, sort, process, and analyze large and complex data sets.
Big Data in finance is a tool for financial professionals that allows you to gain insight from your data and help propel your business forward. These data sets can be analyzed using predictive analytics, customer analytics, and real-time analysis to find patterns that can help you optimize your business growth. Analytics is just one aspect of Big Data in finance. If you want to see the full potential of Big Data, your company will have to dig deeper.
This article will discuss three key Big Data in Finance to help you keep your business on the cutting-edge of the digital age.
Big data in finance: cyber security.
Equifax reported that Equifax suffered a data breach in September 2017. Equifax exposed the personal information of more than half the Americans. Equifax reported in September 2017 that Equifax had suffered the biggest Big Data breach of the 21st century, exposing the identities and sensitive information of 147 million Americans. Since then, Cyber Security has been a priority for the financial sector all over the globe.
Companies and the financial service sector claim that software can be used by banks and financial institutions to analyze large amounts of transaction data and to use machine learning to analyze cybersecurity information.
This software uses machine learning algorithms to detect patterns and anomalies within data networks that could indicate cyber threats.
Big data in finance: robot advisory enhances customer engagement.
Robo-advisors are now available to provide personalized, low-cost advice for customers on their financial portfolios. Big Data in finance analytics is now available to passively manage portfolios purely on the basis of algorithms.
ROBOTS ARE NOT JUST REPLACING TRUCK DRIVERS. ROBOTS ARE ENTERING FINANCIAL SERVICES AND WILL SOON BE THE NEXT VICTIMS.
Robot advisors are digital platforms that offer automated, algorithm-led financial advice services. Common robot advisors gather client data via surveys about their financial picture and their goals. The data is used to automatically invest assets, provide financial advice, and then delete.
Chatbots can be described as a simplified version of robot advisors. Chatbots can answer customer queries, guide customers through the sales process and offer tips and advice. They also gather customer data that will improve customer experience.
Big data in finance: consumer credit score.
Many friends on Facebook can indicate popularity within certain circles. Credit companies are increasingly using social media data such as LinkedIn, Twitter, and Facebook to assess the credit risk of consumers. When granting credit, these businesses consider the person’s digital history, professional connections, social lives, and digital history.
SOCIAL MEDIA CAN HAVE AN IMPACT ON YOUR CREDIT SCORE.
These start-ups aim to capitalize on the perceived disadvantage of traditional loan criteria which are strictly based on credit scores. Many people with bad credit or past defaults would not be considered by traditional criteria. These start-ups, however, aim to reconsider loan extensions through a review of social status.
Big data in finance: mortgage lending.
Big Data in finance is becoming more prevalent in the financial services industry in the latest years. This will bring about many changes in the mortgage lending industry in the near future.
Social media data will be used to help mortgage applications, similar to consumer credit scores. Big Data will be used to extract as much information from public databases, bank records, and other websites as possible during the mortgage application process.
Another approach for mortgage applications is to allow homeowners to submit their applications as usual, and then use the pre-populated data from the mortgage company to find discrepancies in the applications. This will enable applicants to be more precise and cut down on the time it takes for their applications to be processed. This will allow you to see if identity theft may be possible. If there are not enough discrepancies applicants may be flagged and sent to manual review.
COMPUTER ALGORITHMS ALSO WILL BEGIN SCORING ALL APPLICATIONS THAT USE MACHINE LEARNING ALGORITHMS.
It is possible to determine the algorithms that are used to decide whether applicants have been approved or denied. Approved applications can be processed immediately. However, rejected applications may be discarded or subjected to manual review. Mortgage lenders will be able to save time and money by not having to review applications until they have been processed. This will enable mortgage lenders to grow faster and reach more customers, while also reducing delays.
We can use algorithms and data from local property markets to determine reasonable sales prices before any mortgage loans default. More precise pricing will help reduce property market turmoil that can be caused by repossessions or defaults. It is also worth considering reducing the time that a bank must keep a property before selling it.
Big data in finance: optimizing protection & risk detection.
As Big Data technology advances, artificial intelligence, which can maximize protection while also minimizing risk, becomes more sophisticated.
Potential risks can be identified earlier by performing a liability analysis. Financial institutions can work with customers to minimize exposures and reduce liabilities to provide greater protection. Advanced customer data, transaction data, and geospatial data will make it easy to detect risk. Also, advanced data analysis can be used to examine transaction anomalies.
The software can be integrated into enterprise data networks by banks and financial institutions. The software’s algorithm scans customer data and sales transactions to determine risk models and compare them. This includes loss-given default as well as the probability of default. The dashboard allows you to view data insights and quickly assess current risks as well as forecast future ones.
Big data in finance: unified data analytics platform.
In the past, large financial institutions were able to reach across departments. retail banking, commercial banking, asset management, etc. Every platform must be configured for Big Data analytics. Data mining and data transfer between business sectors became very difficult and time-consuming.
However, unified data analytics platforms are quickly gaining popularity. These platforms will allow large financial institutions to easily create a unified system.
THESE PLATFORMS WILL ENABLE BETTER DATA QUALITY AND MORE EFFICIENT DATA MANAGEMENT.
Because different analytics platforms are used by different departments within financial companies, it is difficult to share data among them. Many data sources can be used to extract huge amounts of data in investment banking, retail banking, and direct banking.
The unified platform for analytics allows the creation of custom data science environments upon request. Data scientists can create their own work environments and easily deploy machine learning across an organization using the unified analytics platform.
Big data in finance: financial services big data.
Big Data is quickly making its way into financial service as one of the most important roles in business optimization. Financial services are still behind.
Big Data is a disruptive force in the financial sector. Big Data is changing financial services. These changes include personalized customer experiences via robot advisory, improved cybersecurity to prevent data breaches, and a shift from credit scores to social scores.
IT IS ESSENTIAL TO LEAD YOUR COMPANY FROM THE FRONT IF YOU WANT TO SUCCEED IN THE NEW AGE OF BIG DATA.
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