The Impact Big Data has on the Finance Industry

At the vanguard of big data collation and usage are financial services and financiers, who are using this tidal wave of data to revolutionise how we save, spend, and earn money.

Big data has become synonymous with the digital age - immense petabytes of information generated daily from every touchpoint across the world that can, with the right tools and cultures, drive innovation and business success. The finance industry has been a big data digital leader in more ways than one - from the rise of challenger banks and FinTech (based on critical understandings of customer needs, app-generated data and quality financial relevance), to creating more equitable forms of access to capital, and helping businesses return to growth post-pandemic.

But how exactly does big data help financial companies?

The 4 V’s of Big Data and how they relate to Finance

The 4 V’s of Big Data - volume, variety, veracity, and velocity - intersect with financial institutions and operations in unique ways. It’s worth pulling apart each element to get a fuller understanding of how finance incorporates data into operations.

• Volume: the amount of data generated by financial interactions, operations, trades, and sales, and the volume is truly staggering - “the New York Stock Exchange captures 1 terabyte of information each day”
• Variety: “every action of customers, competitors, suppliers, etc, will generate prescriptive information that will range from structured and easily managed data to unstructured information”
• Veracity: “High veracity data has many records that are valuable to analyse and that contribute in a meaningful way to the overall results”
• Velocity: “refers to the enormous speed with which data is generated and processed”.

In light of the above, here is how big data is changing finance in five ways: creating transparency, analysing risk, algorithmic trading, leveraging consumer data and transforming culture.

Creating transparency

One of the most important governance and ethical factors to consider is the position of vast amounts of unstructured data, how it’s stored, utilised and protected. In the financial sector, transparency refers also to the clarity of data, and how it can be used to improve financial services and outputs. This clarity is vital in creating customer advocacy of financial services - “??Fragmented, inaccessible data can create logistical and operational nightmares for a company, and trust deficits in clients when serviced ineffectively”. With the right tools, big data can provide immense business value, “True data transparency comes when all of that data can be normalised and presented in a clear and understandable dashboard, which can be customised or filtered to the business’s requirements”.

Big Data and Risk Management in Finance

In Reply Avantage’s study Applying Big Data To Risk Management, Big Data sits at the heart of risk mitigation and aversion. “Within the financial services industry, (Big Data technologies) can allow asset managers, banks and insurance companies to proactively detect potential risks, react faster and more effectively, and make robust decisions informed by thousands of risk variables”. It’s clear that big data provides the sort of real-time information to make more targeted, sustainable strategies of financial risk mitigation.

What is algorithmic trading?

While big data and data analysis has incrementally revolutionized finance and financial services during the age of cloud computing, algorithmic trading takes pole position as the business and cultural milestone for how effective data can be in finance.

• “Algorithmic trading is when you use computer codes and software to open and close trades according to set rules such as points of price movement in an underlying market. Once the current market conditions match any predetermined criteria, trading algorithms (algos) can execute a buy or sell order on your behalf”.
• “It is widely used by investment banks, pension funds, mutual funds, and hedge funds that may need to spread out the execution of a larger order or perform trades too fast for human traders to react to”.

In short, algorithmic trading leverages huge amounts of data in real-time to improve returns on investments. It does what humans can’t, at scale, immediately. This strategy of computing-based trading is also referred to as automated trading or high-frequency trading.

Consumer Data

The European Consumer Organization BEUC in their landmark report Innovative uses of consumer data by financial institutions notes, “financial and non-financial companies strive to collect more and more consumer data in an attempt to predict, with a higher and higher level of accuracy, their preferences, future behaviour and risk profile”. Available data has always been used by financial institutions to better understand customer metrics and behaviours, such as credit ratings, segmentation, personalization of offers and data broker purchasing. The era of big data will transform these data sets further into business-critical mines of customer information.

The culture of Big Data

The culture of big data - and how it drives business innovation - is in large part driven by leaders within an organisation, the cultural wind vanes in any enterprise. Whoever is leading on Big Data analysis, be they financial or not, becomes the bellwether for behaviours around data and data usage. Technological change, after all, has an immense impact on the way we do business, the way we interact with businesses, and the way we build cultures of money management. And, as technology L&D service provider O’Reilly points out, “It’s not just about numbers…big data…is also a cultural phenomenon, and has a social dimension”.