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Big data analytics will be the key to the future in the world of finance, but in a world in which data science is evolving, what are the key pillars of a good data strategy?
 

While the first industrial revolution was powered by machines, the next one will be powered by data. We are in the middle of a big data revolution which is transforming the world of business. However, while data provides enormous opportunities it also opens firms up to risk from cyber criminals and from the regulators. So how can the financial sector, particularly in Asia, make sure that data becomes a bonus rather than a menace?

The big data revolution 

It’s easy to underestimate the speed and impact of the big data revolution. 90% all the data ever created came into being within the past few years, but that’s just a hint at what’s to come. According to some estimates, the amount of data coming into businesses could increase 10-fold by 2025. By that time the world could be producing 175 zettabytes of data. 

Data is likely to be crucial to the future. An estimated 89% of businesses believe that if they do not adopt big data strategies within the next few years they will lose their competitive edge. If they do not, their competitors will and will gain advantages across the spectrum. It will, then, truly redefine the competitive landscape of all businesses, few more so than the financial sector. Because of this the number of companies harnessing big data has soared over the past few years from 17% in 2015 to 59% in 2018. 

Big data and finance 

Financial institutions are among the most likely to benefit. Back in 2016, investments in big data within the financial sector topped $20bn making it one of the biggest customers for big data solutions. 

Asia is leading the way, especially in Singapore where estimates suggest big data contributes $1bn to the economy every year. It has hosted a number of high impact projects such as Alibaba’s first joint research institute outside of China. 

The city leads the way thanks to its high connectivity and support from the Government. Internet speeds in Singapore top world rankings. Authorities have worked hard to nurture this environment with initiatives such as the Smart Nation project which has helped to turn Singapore into a global technology hub perfect for tech entrepreneurism. With this infrastructure in place, companies of all sizes are embracing big data.  

Hong Kong is not far behind. Internet speed are only slightly behind Singapore, while they also benefit from supportive government policy in the form of the Smart Cities initiative which sees the construction of a revamped government cloud infrastructure and smart lampposts which will be equipped with devices to collect data on traffic flows, weather forecasts and environmental data. They will also be suitable for mobile companies to install 5G connectivity. 

Why embrace big data analytics?

Both in Hong Kong and Singapore, the environment is perfect for big data. Authorities are supportive and technical infrastructure is in place, giving companies everything they need to get the most out of it. For the financial sector in particular this represents a fantastic opportunity with data science and analytics driving all sorts of improvements including: 

•    Personalisation: Companies can collect a huge amount of data about their customers and use it to shed light on buying habits and trends. This allows them to offer a more tailored, personalised service. It can improve customer satisfaction and increase the amount they spend. 
•    Insights: Real time data can provide more granular detail about business operations. It provides real time financial information, shows where the company is making money and where it is losing money. It helps executives focus their strategies and make better business decisions. 
•    Trading: Big data has led to a rise in algorithmic trading which harnesses vast quantities of data and identifies trends human traders might miss. Already it is being used for over 70% of total orders as technological developments and computer power reaches the scale at which algo trading becomes viable. 
•    Security: Data such as location intelligence can help banks to identify customer spending habits and highlight any abnormal behaviour. For example, if a customer were to suddenly start making large purchases from the other side of the world, the system would flag this as suspicious behaviour. Big data, machine learning and automation are also being used by cyber security professionals to identify security threats faster. 

What’s holding it back? 

For all these benefits the financial sector has been more hesitant than others to make the move. For all the opportunities, they also face a number of risks which are unique to financial institutions. As data becomes more mobile it becomes vulnerable to attack from cyber criminals and other malicious actors. The financial sector is the biggest target of cyber criminals and all that data makes an extremely tempting target.

We’ve already seen high profile cases such as the breach of Equifax and the hack of Tesco Bank in which criminals stole £2 million from 34 bank accounts. 

Aside from the financial and reputational impact of these breaches, the financial sector will also be worried about the attitude of the regulators. Finance is the most heavily regulated sector in the world and the last few years have seen a host of new rules coming into force which govern the use of data.

The most serious of these is the EU’s General Data Protection Regulations (GDPR). These set down rules about how companies handle data, what measures they take to protect the data and what steps they have in place if a breach does occur. 

When a company is deemed to have fallen short of what is required, the regulators will act. Tesco Bank was fined over £16 million for what the regulator deemed was an avoidable data breach. The only saving grace was that this happened in 2016 before GDPR came into force. Had it happened in the last year, the fine would have been much higher. Serious breaches could attract fines of €20 million or 4% of annual global turnover which means penalties are now reaching unprecedented levels. 

This year, British Airways was fined £183million, Marriot was fined just short of £100million while Facebook broke a record with a $5bn fine for its handling of data from Cambridge Analytica. Each of these fines could have been even higher if the full powers of the regulator had been used. 

The penalties for getting this wrong are severe and implementation can be far from straightforward. Handling big data requires significant investment in technological innovation but that can run into difficulties with legacy systems. 

Many companies have turned to partnerships and employed cloud services to increase their data handling capacity, but these third parties can add to vulnerabilities. Under data protection regulations, a company is still liable for that data, even if a breach is the third party’s fault. What’s more, most companies do not hold their partners to the same security standards as they hold themselves, which means even the most security aware company could be opening up a hole in its systems. 

How to create a big data strategy 

The risks are considerable, but the benefits are worthwhile. Widespread adoption of big data in the financial sector is inevitable. Firms face a choice or moving or being left behind. The question, then, is not whether they should embrace big data but what their big data strategy should look like. 

This can seem complicated, but any strategy rests upon three fundamental principles known as the three Vs:

•    Volume: The amount of data collected. Technological developments such as the internet of things make it possible to collect vastly more data about customers and operations than ever before. This volume is an opportunity in that it can help companies to make investment decisions or gain insights into the needs of their customers, but it can also be difficult to process. 
•    Variety: Data comes in two forms: structured and unstructured. Structured is clearly defined and recognisable which falls into simple categories. Unstructured data is hard to recognise and does not fall into a standard model. This includes social media posts or video content which provides a lot of insights but can be difficult to sort and manage. 
•    Velocity: The speed with which data is stored and analysed. A business can differentiate itself from the competition by the speed of data collation and analytics. 

Whatever form the data takes it is important that it can be identified and verified. The data can only be manipulated if it has a name and a defined format. Companies will need to establish a consistent data naming process. 

All that data will have to be stored. Firms may vary between an on premises approach in which data can be stored easily but is not always accessible and a cloud approach which delivers much greater data capacity and means data can be accessed and manipulated in real time. A hybrid approach can use the cloud for short term ‘agile’ data while storing more ‘passive’ data on premises.

Big data strategy and implementation

Ultimately, because data has become so ubiquitous, data science must be seen as an ongoing process rather than a single project. Data governance should be a key part of operations, including architecture reviews, audits, risk reviews and system development methodology. 

It is a fast-moving world with new opportunities arising in the form of technological innovation and fresh threats in the form of cybercrime. A good strategy, therefore, will rest upon collating, analysing and storing. It will need the capacity to handle the increasing amounts of data coming into the business, to sort it into a usable form, derive insights and keep it safe. The rewards, for businesses which manage this, can be enormous. 
 
Related Topic: 
Using Open Source in Finance, a presentation delivered by Benjamin Tang, Quantitative Analyst, BNP Paribas at the Big Data & AI Leaders Summit.

 

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