(First published on Financial IT)
Thomas Egner, the Secretary General of the Euro Banking Association recently referred to data as the ‘new superpower of the financial services world.’
Despite the huge appetite for data across the industry, its disruption has been muted. Research from PwC highlights how little data most financial services firms use when understanding and engaging with customers – with estimates that businesses are only using 0.5% of available data. At the same time, according to Deloitte (2017) 44% of companies say that there are no clear accountabilities for data management or defined data processes and procedures.
This poor use of data is partly cultural. Quarterly earnings reports and whitepapers, for example, have been the staple of banking executives when making decisions and analysing the marketplace. However, these papers are often slow and infrequent and as these sources are readily available they tend to offer limited intelligence or insight.
For those institutions that are using data, there has been a growing understanding that simply having mountains of data is not enough. The conversation has moved from having big data to having fast and relevant data, with a growing demand for alternative data to provide intelligent, accurate and actionable information. Companies are beginning to see the value in purchasing alternative data as a way of gaining a competitive information advantage that is so vital in the banking and finance marketplace.
Alternative data consists of data obtained from hard to access or non-traditional sources such as satellites, point of sale transactions and the Internet of Things, and can be used to better predict market movements and trends. By harnessing alternative data, corporations, economists and investors are able to access the very latest data to inform the best decisions.
Alternative Data: an information advantage
Alternative data presents an information advantage, especially prominent in the field of investment management, with hedge funds among the pioneers in the alternative data field. A 2017 report from JP Morgan suggested that asset managers were spending up to $3 billion on alternative data per year, with the number of alternative data analysts quadrupling in the last five years.
Recent research from the University of Toronto highlighted the potential, with researchers analysing nearly 1 million tweets that mentioned nearly 3,600 companies to perform textual analysis on them. This analysis was then fed into a machine learning algorithm that was able to accurately predict whether each company would meet their quarterly earnings target or not. Indeed, the approach was even able to accurately predict how the share price of each firm would respond to that event.
In addition, work undertaken by the School of Business and Economics at Friedrich-Alexander-Universitat Erlangen-Nurnberg, which saw a deep learning algorithm trained on 180 million data points about members of the S&P 500 over a 22 year period from 1992 in order to generate better quality stock picks. When compared to existing methods, the algorithm was capable of achieving double digit returns, with an especially strong performance during turbulent financial periods.
Such outcomes are not confined to the stock market, with researchers at the University of Plymouth showcasing how commodity prices can be predicted with similar accuracy. Their work saw algorithms trained on vast quantities of data to accurately predict movements in the price of oil.
Capitalizing on the opportunity of alternative data
Despite the tremendous promise presented by data, its collection is not devoid of risk. Traditional data gathering is a big threat to an organisation’s security and reputation. Methods involving mining, scraping and analysing vast amounts of personal data from day-to-day business operations – to inform marketing and advertising campaigns -are putting companies at risk.
Data breaches such as those experienced by Equifax and Capital One highlight the disadvantages of depending on personal data and traditional data collection. At the same time, there needs to more robust cyber security measures for existing data sets on customers and stakeholders.
By using alternative data there can be significant reductions made to the security risks associated with relying on highly personal data – because any data that is being collected will be anonymised and aggregated.
Once companies have successfully adopted alternative data as part of their business strategy, we’ll see an evolving risk landscape. The real threat to banks and financial institutions won’t be data breaches but will relate to businesses that aren’t capitalising on the opportunity to stay better informed.
Firms that fail to update their investment processes to incorporate a wide range of alternative data sources run a considerable strategic risk, and are likely to be outflanked by rival firms that are able to incorporate such data sources into their valuation and trading processes.
Alternative data is set to transform banking and finance in the coming years, so missing out is a risk you really can’t afford to take.
