Big Data is more than a buzzword. Data-driven optimization is the major objective of the financial industry, so many companies are looking for opportunities to utilize Big Data and make a great step forward to digital transformation. Big Data helps to discover competitive advantages and reveal new market opportunities.
In this text, we are going to provide the true definition of Big Data, consider its possible usage cases in finance, and predict the challenges and disadvantages of its implementation.
The financial services industry is totally invaded by technology terms, like machine learning, artificial intelligence, the cloud, the Internet of Things (IoT), and Big Data, of course.
In some cases, the term is misused, and even when used properly – it’s really hard to define. We see that Big Data is used in the three most common meanings:
However, the encompassing definition of “Big Data” is as follows: cutting-edge technologies and frameworks used to gather, structure, process and analyze massive and diverse sets of data.
At the same time, all the above-mentioned types of analytics are about more specific activities: they are used to find patterns or to bring business value.
For sure, the practical use of Big Data is receiving business-related insights instead of just obtaining data. It demonstrates why analytics is the most widely known, visible and valuable aspect for businesses.
In the last 5-7 years, most of the banks started actively investing resources in data collection and processing technologies, but to many of them, data warehouses or Business Intelligence still doesn’t seem to be a must-have.
In the situation of increasing and shifting customer demands, banks must compete with each other and also with rapidly growing fintech companies, which are struggling to anticipate customer needs and improve their experience.
To understand the value of Big Data in the finance industry, we suggest starting from the 3 V’s:
Companies implement certain aspects of Big Data depending on the industry, the company’s own priorities and goals.
Volume is the ability of Big Data technologies to work with multiple Tbytes (1000 Gbytes) or even Pbytes (1000 Tbytes) of data. The expanding volume of financial market data makes this V-volume a priority. Investment banks, asset management companies and capital markets need to deal with transaction history data, a high volume of quotes, market, and customer data. For example, the New York Stock Exchange alone creates and stores over a Tbyte daily. The same applies to insurance and retirement firms – the require a huge amount of data for proper risk management.
Velocity defines the speed of data analysis or the speed of sending it to the storage. As we have already mentioned above, The New York Stock Exchange deals with 1 Tb of information on a daily basis: 3 years ago there were already almost 19 billion network connections, which means 2.5 connections per each person in the world. No wonder, velocity is a key thing about Big Data in corporate finance: effective and quick processing of trades and other transactions is a major advantage. Experts talk about 105 transactions per second and even more.
Variety characteristic means that Big Data applications and algorithms can deal with structured and unstructured data in a variety of formats and from different sources. For example, Big Data in corporate finance means that institutions proceed with reference data, market data, customer requests and more.
Veracity helps to figure out how accurate and applicable is the data, what is important and what is not – and to set these two types of data clearly apart. Veracity helps to build a deeper understanding of data contextualization and make this data applicable.
Big Data analytics can help financial industry players increase the effectiveness of all their marketing initiatives: customer acquisition, re-engaging and activation and relationship management.
How Big Data promotes sales and marketing in financial organizations:
1) Effective and super-targeted marketing campaigns – for example, by prospects segmentation based on public info and insights about the сustomers, by personalization of advertising messages, monitoring social media for direct feedback or by work of opinion leaders and influential customers.
2) Successful customer activation, cross- and upselling opportunities – boosting sales to existing customers or maximizing the first sales opportunity. It is possible due to predictive analysis of demand, proactive offers (like next-best-offers after the first purchase), creating product bundles and applying dynamic pricing. Banks and fintechs can increase sales by notifications, call-backs or personalized pop-ups based on customer insights and current customer behavior.
For example, banks can show investment opportunities when customers receive higher income or offer credit lines (overdraft) when customers have less money left than planned in their personal finance management tool.
Also, Big Data enables banks to contact customers right on time: when the deposit term is expiring or when the customer visits a foreign country and wants to increase the credit limit. There is certain customer information that can help a lot in sales: for example, when customers change the address after the move or relocation or change the marital status.
3) Customer relationship management. Data analytics can transform the financial business by analyzing the history of transactions and inquiries from multiple channels, family-related, and career insights. Also, it helps identify high-value clients to offer them premium services, individual offers and reveal the best communications channels to reach them. Increasing the loyalty of existing customers is possible due to tailoring loyalty programs according to the payment сard use and financial habits.
For example, banks and fintechs can partner with retailers and service providers to offer their propositions by geo preferences.
Big Data also can help to reveal possible problems with customers and prevent their leaving. For example, complaints in social media, official complaints to customer service reps or cancellation of certain services can be signs of it.
Big Data analytics helps gather information on legal cases, analyze the current legislation states of being and track amendments to avoid penalties.
Big Data is not going to slow down the pace when transforming the financial services industry. Structured and unstructured data provides customer insights, complex algorithms execute trades, automation of credit score calculation minimizes human error. These are just a couple of ideas to mark the development of the industry, which was changed due to the Big Data. If you know more about them, let us know in the comments!