We are generating data at an astonishing rate, with approximately 90% of all global data being created in just the last few years. Many studies expect big data to continue growing at lightning speed. As Big Data becomes more dominant, senior executives and leaders in the financial industry need to maintain close attention to this market.
Big Data in Finance
The core goal of Big Data in finance is to generate insights from certain data sets to ensure your business improves. Automated, real-time analytics, predictive tools, and customer insights are example methods of measuring these sets of data to identify trends and raise business performance.
In order to take complete advantage of big data and its ability to support business growth, financial businesses need to consider a number of factors:
The frequency of cyberattacks has risen, with certain events resulting in exposing extremely confidential information. With the rising threat of possible cyberattacks, cybersecurity has inevitably become a top priority within the Big Data in the financial services industry.
Businesses such as Versive provide software that measures transaction data and cybersecurity data using Machine Learning. Through a series of algorithms, the software monitors data to identify patterns and possible errors, identifying any that could suggest a possible cyber attack. Implementing services like this into financial businesses is critical in ensuring data is protected and business performance is maintained.
Robotic technology is emerging into the financial industry, particularly within the advisory market. Robotic-advisors are being introduced to provide an affordable, personalised financial service to customers. By using a strict set of algorithms, Big Data analytical systems can be used alongside to manage financial portfolios without requiring human support.
At present robo-advisors provide automated algorithm-managed financial and planning services. These advisors gather customer data concerning their background and goal through surveys and use the data to automate investment into assets and provide further financial support. A simplified version of a robotic advisor is a chatbot, managing customer inquiries and supporting individuals by providing tips and advice. Industry experts, this market to grow rapidly with investors actively looking for low-cost robotic advisor services.
As Big Data continues to progress, so do other innovative systems such as artificial intelligence that are capable of protecting and managing risk. Advanced data, transaction data and deeper analytics enable financial businesses to monitor patterns and manage risks.
AI software provider Ayasdi uses big data analytics services to support financial companies predicting potential regulatory risks with machine learning. The business believes its software supports banks with regulatory compliance, monitoring customer transaction data to identify anomalies.
Financial businesses and banks and apply the software simply within their own data systems, providing a clear dashboard to identify and predict potential risks to a business.
One System for your entire business
In previous years, large financial businesses spanning multiple functions would develop individual Big Data analytical systems. This resulted in difficult and time-consuming management of data between various business departments.
This year we are experiencing a continued rise of unified data analytics platforms, enabling the use of a simplified and more efficient unified system for larger financial businesses. Like most financial businesses with multiple departments, it can be challenging to communicate different data sets if each area is using a different analytics platform. A unified service enables customisation of data sets, allowing data scientists to create their own, dedicated working system.
Big data has become an essential tool in many industries but studies suggest the financial services industry is behind in terms of uptake of big data. Based on the Morgan Stanley Digitalisation Index, financial services stands at 18th out of 34 sectors, behind Pharma, Utilities and Oil and Gas. The financial market has a continued increase in competition in the emergence of Big Data. Focusing on cybersecurity, implementing robotic support where possible and creating a singular system are all vital techniques in maintaining a strong position with Big Data changes in the financial market.