Future of Big Data in Financial Services
Significant developments in technology have made big data indispensable in the world of business and finance. Financial services companies are increasingly using big data to revolutionise their organisations, operations, and the entire financial sector.
In a study by Hasan and Popp, the financial industry uses an estimated trillion data pieces daily. These large data sets are constantly analysed to create decisions related to investments, tax reform, and risk analysis. Therefore, big data is getting more attention in the financial services industry, where information significantly impacts critical production and success factors.
Central to the success of big data in finance is the ability of businesses to adapt and innovate their systems. At Finworks, we help businesses take advantage of big data to drive innovation and make products of their Big Data assets.
What is Big Data in Finance?
Big data are diverse and complex data sets. Financial services firms can analyse these data sets computationally to reveal trends, patterns, and associations that can influence their decision-making.
How Big Data Is Revolutionising Finance
Big data in finance has sparked considerable technology advancements in recent years, enabling practical, customised, and secure solutions for the sector. Big data analytics has succeeded in completely changing the financial services industry, not just a firm’s specific business procedures.
Real-time Stock Market Insights
Big data has altered how investors make decisions and how stock markets operate. With the help of machine learning, which involves employing algorithms to detect patterns in vast volumes of data, computers can now make precise predictions and judgements similar to those made by humans. Through this, investors can execute transactions at higher speeds and volumes.
Big Data Analytics in Financial Models
Applying big data in finance offers fascinating opportunities to enhance predictive modelling to forecast accurate return rates and investment outcomes. Financial services companies can improve their algorithms by accessing big data, leading to more accurate market predictions and risk mitigation for financial trading.
Today, a business’s data insights, operations, technology, and systems revolve around the customer. As such, banks and financial market organisations focus their big data activities on customer analytics to improve their product offerings and customer service.
Risk Management and Fraud Detection
When used in finance, big data decreases the potential harm caused by fraudulent activity. By comparing internal and external data, big data technologies provide excellent risk management tools to identify risks, including market, instrument or systemic risks. Banks and financial institutions, for example, can quickly spot fraudulent activity through their access to real-time customer data.
Big Data Challenges Facing the Banking and Finance Industry
The amount, velocity, and variety of information that big data relies on are becoming increasingly difficult for legacy data systems to handle as an expanding number of unstructured and structured sources rapidly generates it. The ability to draw insights from the information and to enable sophisticated technologies to become requirements for management.
Although the technology is currently there to address these issues, businesses still need to learn how to manage extensive data, integrate new technological projects into their operations, and overcome general organisational reluctance. Big data’s particular issues concerning finance are a little more complicated than those in other sectors for various reasons.
Meeting Regulatory Compliance
Regulatory compliance in financial services data within the United Kingdom involves adhering to a complex framework of laws and regulations designed to ensure the secure, ethical, and responsible handling of financial data. These regulations include the Data Protection Act 2018 (aligned with the EU's General Data Protection Regulation), the Financial Services and Markets Act 2000, and sector-specific guidelines from regulatory bodies like the Financial Conduct Authority (FCA) and the Prudential Regulation Authority (PRA). The challenge lies in navigating evolving regulations, adapting to technological advancements, and consistently demonstrating compliance to ensure the trust of customers and regulatory authorities.
The issue of privacy in financial services data revolves around safeguarding individuals' sensitive financial information from unauthorised access, use, or disclosure. It entails respecting and protecting customers' personal and financial data, such as account details, transactions, and credit history. Privacy concerns in financial services encompass data breaches, identity theft, and potential misuse of personal information for fraudulent activities. Financial organisations are concerned about storing confidential information on the cloud, and while some transitioned successfully to the cloud, these endeavours can be expensive.
The issue of governance pertains to the proper management, control, and ethical use of data within the financial industry. It involves establishing frameworks, policies, and practices to ensure data accuracy, security, privacy, and compliance with regulations. Effective data governance addresses challenges such as data quality, data integrity, unauthorised access, and data sharing while enabling transparent, responsible, and trustworthy handling of sensitive financial information.
Now seen as a significant business intelligence barrier, the inability to link data across organisational and departmental silos results in complex analytics and impedes big data initiatives. Data silos can lead to duplication of efforts, inconsistent information, and incomplete insights, impeding a comprehensive view of customers, transactions, and market trends. In the financial sector, breaking down data silos is crucial to enable holistic risk management, accurate customer profiling, personalised services, and compliance with regulations. Integrated data platforms, modern data architectures, and collaborative strategies are necessary to unify data across the organisation, fostering better coordination, informed decision-making, and improved customer experiences.
Future of Big Data in Financial Services
Big data analytics have existed for more than a decade. Early adopters have learned from their failures and have demonstrated how businesses can genuinely benefit from embracing the tools offered by big data. However, big data has so much to offer in the later stages of its evolution.
Risk Management and Fraud Prevention
Big data analytics can provide real-time risk assessments and fraud detection by analysing large volumes of transaction data. This will lead to more effective risk mitigation strategies and quicker response to potential threats. Big data analytics can aid financial institutions in meeting complex regulatory requirements by automating compliance monitoring and reporting. This will reduce errors, enhance transparency, and streamline regulatory processes.
Machine learning revolutionises financial services through predictive analytics, risk assessment, fraud detection, algorithmic trading, and personalised customer interactions. It refines credit scoring, portfolio management, and compliance monitoring, harnessing alternative data for insights. Robo-advisors optimise investments, while high-frequency trading exploits market inefficiencies. Quantitative analysis benefits from advanced models, and predictive algorithms enhance market predictions. This innovation streamlines operations, improves decision-making, and enhances security, reshaping the industry for greater efficiency and customer satisfaction.
Enhanced Customer Insights and Service
Big data analytics will enable financial institutions to gain deeper insights into customer behaviours, preferences, and needs. This will facilitate personalised financial products and services, leading to improved customer satisfaction and loyalty. Big data will enable more accurate wealth management advice by analysing clients' financial data, goals, and risk tolerance to tailor investment recommendations. Big data-powered chatbots and virtual assistants will provide customers with immediate and personalised assistance, enhancing the overall customer service experience.
Keeping Data Secure
Authorities are becoming more adept at comprehending big data and enforcing its responsible use. There will be severe consequences if businesses don’t take the necessary precautions to protect their data. Through fines and penalties, the government and the private sector and the consumer, through trust, will provide these.
Big data analytics will play a crucial role in digital identity verification, reducing fraud and ensuring secure online transactions.
Moving to the Cloud
Financial firms are setting the pace for cloud migration in the IT industry. Cloud storage allows organisations to scale their storage resources up or down as needed without the need for extensive hardware investments. This is particularly valuable for big data, which often involves massive and growing datasets. Cloud migrations enable better data accessibility giving redundancy and disaster recovery options. Cloud storage providers have data centres located in different regions, facilitating data storage and processing closer to where it's needed, which can improve performance and compliance with data regulations. Importantly, cloud storage can be integrated with real-time data processing frameworks, allowing organisations to analyse and respond to data streams in real- ime. Now, more than ever, financial institutions are starting to trust the public cloud.
Financial Startups and Challenger Banks
Nearly every industry in the world experienced massive disruption by the lightning-fast rate of technology development, and financial services are no exception. Startups and smaller businesses can implement technology more quickly and precisely because their historical baggage burdens them less.
Many financial startups use big data analytics to provide customers with distinctive offerings that the established players currently cannot.
Future of Big Data in the Finance Sector? Unrivalled Capabilities of Finworks Data Platform for Big Data Applications
In the dynamic landscape of today's financial services industry, harnessing the power of big data has become a necessity rather than a luxury. In this pursuit, the Finworks Data Platform is at the forefront of innovation and efficiency. Its comprehensive suite of tools and features, tailored specifically for the financial sector, makes the platform the ideal solution for supporting robust big data operations. As organisations continue to grapple with ever-expanding datasets, Finworks Data Platform stands as a reliable partner, offering the scalability, reliability, and agility required to thrive in an era where data reigns supreme.
With the Finworks Data Platform, you’ll get big data self-service preparation, including the following features:
- The ability to onboard multiple business silos, structured, unstructured and ingest from pre-existing Data Lakes or other repositories
- Workflow templates to facilitate data validation, cleansing and duplicate identification, as well as support for bi-temporal capabilities. Thus, ensuring high-quality data is made available for use by the business.
- Have built-in modelling capabilities, facilitating data linkage, lineage and semantic understanding
- Supports complex/unpredictable queries, as users are not constrained by the design of any underlying database schema with enhanced query/response times on large datasets
- Cloud agnostic: Comes with production-ready installation images for Oracle, Google, Microsoft Azure and AWS.
Contact us today to learn how Finworks can help integrate big data technologies into your business, and we'll talk about the solution that will fit your needs and current resources.