Big Data In Finance

The Rise of Big Data In Finance

Mike Woods

Rise of big data in finance is impacting everything from customer acquisition, cybersecurity, marketing and productivity.

Banks and financial institutions have always known a great deal about their customers. Previously, these reams of information sat in big files, or on spreadsheets and databases. It was used to assess customers’ worthiness for mortgages and loans, but not much else. Today, banks have the means to collect even more information on their customers, and big data technology has emerged enabling to use this data in more creative ways. Machine learning fintech allows lightning-fast analysis and identification of patterns.

The rise of big data in finance hasn’t only affected banks’ relationships with their customers. It stretches into numerous other areas. In this article, we’ll examine how big data has changed the way banks and financial institutions operate.


‘Of the top ten acquirers, financial data and information providers accounted for a handful of buyers. FactSet, Markit Group – which merged with IHS last year – and Thomson Reuters have each announced at least four deals in the last 30 months.’

Mike Woods, Fintech Sector Principal

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Knowledge of Customer = Power

Big data has revolutionised the way banks can acquire new customers, as well as market new products to current customers.

Using big data analytical fintech, banks can look at customers’ payment data and spending patterns. This makes it easier for banks to spot when their customers might need new financial services, such as savings accounts, investment products or loans. Banks can also see who their most valuable customers are, then build relationships with them to ensure they don’t take their business elsewhere.

When it comes to acquiring new customers, banks can use the data they already possess for customer segmentation. They can build strong personas, then advertise to those personas with relevant, more personalised messaging.

McKinsey analysis shows that using data when coming to marketing decisions can increase effectiveness by 15-20%. On an average global marketing spend of $1 trillion per year, that increase could be worth $200 billion.

Maximising Employee Productivity

Banks are also leveraging big data technology to collect data about the work of their employees.

Banks use data collection technology, such as state-of-the-art CRMs, to measure everything their employees do. They can pinpoint exactly what each employee is adding to the business. It’s easy to identify who are the top performers, as well as who is lagging behind. Top producers can be rewarded, and staff who need help can be coached, in real-time. Managers don’t have to wait for annual review time anymore. Banks can also use data to spot patterns in how processes can be improved, and what types of personalities perform well.

Organisations can also use data analysis products to engage with their staff through surveys and rewards programmes.

Traders Become Maths Buffs

On the investment banking side of finance, big data is being put to extremely productive use.

Big data analytics is leveraged to build complex predictive models to estimate the return on trades. Trading models have always looked at prices and whether they’re rising and falling, but with data technology, banks can incorporate other factors, such as political trends and social influences. These models help investment teams mitigate risk in real-time. In theory, it should lead to better trades and minimise losses.

Banks have also harnessed big data in finance to make trades. Algorithmic trading uses big data and machine learning to enable computers to make decisions a human trader would usually make. The difference is that tech can make these decisions in fractions of a second. When beating your competitors to a price can make millions, this enhanced speed is essential.

With machine learning, the more data gathered, the more effective these programmes become. These models and algorithmic trading programmes are only getting better.

Gearing Up Security

Finally, banks are using big data to protect their customers from fraud and cybercrime.

We have seen how big data technology looks for patterns in payment and spending for the purposes of marketing. It can also detect fraudulent activities in these patterns. If the technology spots a payment leaving a customer’s account that is unusual, the bank will take measures to analyse whether it is legitimate or not.



‘The driving force behind these deals is a bid to create a powerhouse in the financial corporate information industry to rival companies such as Bloomberg and Thomson Reuters, amid a wave of takeover activity among market intelligence companies over the past two years.’

Mike Woods, Fintech Sector Principal

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The future of big data in finance

Big data technology is constantly advancing. Banks and financial institutions are investing in new ideas constantly to gain an edge against their competitors. Whether it’s more effective marketing, employee evaluation, quicker trading or fighting cybercrime, technology is redefining what’s possible.

Binny Matthews, Co-founder and  CEO at DeZyre, an online skills platform, believes the next trend will be in building the bank of the future, where customers will actually want to go. No more queuing, just instant service.

Here are some big data fintech companies in this space we’re currently watching:

TrueLayer – Secure, reliable and easy access to banking infrastructure. A complete toolkit for you to build modern financial applications through a delightful experience.

Duedil –  Helps companies find opportunities and mitigate risks, by providing the richest source of private company information on over 40 million companies.


Credit Benchmark – Credit Benchmark is a financial data analytics company offering an entirely new source of information: the credit risk views of the world’s leading financial institutions.

Mike Woods, Fintech Sector Principal

With  30 years’ international experience and has operated at board level across global businesses, spanning startups to multi-billion public and private companies in the Banking, Retail, Fintech and Payments industries. He was responsible for e-commerce and payments at Natwest Bank, and later worked at Royal Bank of Scotland, with over 1,000 people working for him innovating digital and payment solutions. He has founded such companies as Aconite Technology, a VC-backed software solutions business, and as CEO grew the company internationally into 22 countries, across the USA, Europe and Africa, selling to a PLC in December 2014.

If you would like to talk to me about developments in the fintech industry which can boost your business, please send me an email at