The Role of Big Data in Financial Analysis

Big data analysis is becoming an important issue that must be considered by finance specialists. Big data is revolutionizing how businesses operate within the finance industry.

American Express uses big data analytics to quickly analyze customer spending patterns and assess fraud cases, helping save huge sums of money while providing their customers with superior service.

Detecting Fraud

One of the main applications of big data in finance is fraud detection. By using real-time processing of consumer information and detecting suspicious behavior in real time, companies can identify potential fraudulent activity before it takes place and save both money and time in doing so.

Financial organizations utilize big data to enhance customer service by analyzing interactions and tracking customer histories with companies. This can help pinpoint any areas where changes must be made to increase efficiency.

There are various challenges associated with big data, including its velocity, variety and veracity. Velocity refers to how fast most modern data changes; stock prices often fluctuating several times each second for example. Variety refers to how different kinds of information – structured databases to unstructured forms like images, videos and log files can all make up big data, while veracity must remain reliable and accurate if big data is to remain useful.

Developing New Products

Big data analytics have become one of the key elements in financial services product development (NPD). By examining large amounts of customer data, companies can create products more relevant to customers while increasing revenue streams.

Big data encompasses more than traditional transactional information; it also encompasses unstructured behavioral and social media data that provides useful insights for new product development (NPD). Companies using big data to improve products and services while strengthening competitive advantage.

Big data can be defined by its volume, velocity and variety. With smartphones, web-based communities and sensors exponentially creating new forms of information that must be stored somewhere; data accumulation accelerated by open-source frameworks like Hadoop and NoSQL; its variety spanning text to images to video as well as audio/sensor information; finally it’s generated and updated at an incredibly rapid pace compared to conventional data sets.

Tracking Performance

Big data can assist companies with monitoring performance and taking appropriate actions when a pattern emerges, which is especially valuable to investors as they make more informed decisions regarding where and when to invest their money.

Financial organizations can use big data to enhance customer experiences. For instance, they could track what customers do on their websites to provide improved services that meet customers’ needs more fully.

Financial organizations can utilize big data analytics to detect fraud before it occurs and reduce losses for their company, saving both time and resources in the process. Furthermore, compliance with privacy regulations is ensured as personal identifiable information (PII) may be used illegally. Without adequate safeguards in place PII may lead to litigation as well. For this reason, an appropriate security framework must be put in place in order to avoid legal problems down the road.

Algorithmic Trading

Financial firms generate billions of data points every day. Big data technology provides businesses with a way to use this information for analysis to improve business performance or detect any risks that might threaten the firm.

Banks use big data analytics to predict which loans will go into default and use this information to avoid investments that might cost money in the form of bad loans or investments that could fail.

Big data analytics are revolutionizing the financial industry, but not without their own unique set of challenges. Finding people capable of understanding both technical and financial aspects of big data analysis requires unique skills sets which may be hard to come by. Another issue involves protecting all this sensitive information from hackers.