The Future of Financial Technology – Part 2: Big Data

 In Fintech

big dataThe power of big data.

In the first article in this series we looked at how financial technology is already changing the financial services industry, and how incumbents will need to streamline their infrastructure if they hope to remain competitive in a shifting marketplace.

This time we look at the importance of automation, and how it is vital to the new Big Data-led business model.

Data scientists can only do so much

The true value of Big Data lies in the ability to draw actionable insights from disparate data sets. But, as the name implies, Big Data relies on data collection routines that harvest and store anything that may prove to be of analytical value.

Data scientists are key to Big Data strategy, and are more than capable of extracting the necessary insights that inform business decision making. The problem is that the market continues to grow, as does the speed at which transactions and decisions are made. The only way to cope, will be to empower the new streamlined platforms to take autonomous action based on pre-configured observations and rules.

Increasing automation

The continued reliance on human decision making in the era of Big Data presents some problems:

  • Humans are unable to process many thousands of variables simultaneously to make decisions.
  • Humans introduce latency into the process, slowing operations down and reducing potential profit margins.
  • Humans are more costly than computing resources and properly written algorithms.
  • Humans will simply be unable to ‘handle’ the Big Data requirements of the future.

Instead businesses looking to the future will need to utilise artificial intelligence, machine learning and autonomy to remain relevant.

Already in place?

Some aspects of the financial services are already operated by autonomous machines; foreign currency exchange and stocks trading are being traded by software with minimal human intervention. The difference is that these existing systems are limited in scope, they are not tuned to process unstructured data from across the entire enterprise.

The automated platforms of the near future will need to be robust and trustworthy, as they will be expected to manage high value activities successfully. Among the applications we can expect to see emerging are:

New market platforms

In order to assist with the new data-driven economy, FinTech businesses are already building market information platforms, complemented by automated data collection and analysis tools. This platforms, like in-house Big Data stores themselves, contain actionable insights that can be unlocked for profit.

However, if incumbent financial services providers are to stay in the game, they will need to build systems that are not only able to take advantage of the services and data, but to do so automatically, without human intervention.

Externalised processes

Where appropriate, external resources can be used to ‘share the load’, providing capacity and performance boosts on demand. Using Cloudbursting technology for instance, businesses can build fully flexible Big Data systems that offload processing to the Cloud once onsite capacity peaks.

But to avoid latency and delays as data is shifted around, autonomous systems will be able to provision capacity as and when required, again without manual intervention. This kind of intelligent provisioning minimises the risk of downtime or delays as information is processed offsite.

Investor empowerment programs

The new generation of investors and customers have been brought up in the era of self-service services, and therefore expect to be involved in managing many more aspects of their portfolio or financial dealings. Aside from giving them the tools to manage their own affairs, businesses will also need to create new ways of delivering timely, credible advice.

Using Big Data and autonomous analysis, this information does not necessarily have to be delivered by a human however. Rather than providing general market trends via an accounts manager, businesses can harness autonomous systems to comment in real time, helping customers to begin maximising their profits immediately. In return, these platforms can also gather valuable social tracking and retail arithmetic data to be fed back into their analysis, helping to further clarify and improve services, and to identify new revenue channels.

Big is going to get bigger

Even if Big Data techniques were outlawed, or fell into disrepute, the use of autonomous systems and artificial intelligence still makes sense. Humans are not only slow and costly, they are also prone to making mistakes that affect customers and businesses alike.

Instead automated algorithms are capable of both avoiding these problems and leveraging streamlined systems to ensure that clients and companies make the very best of every identified opportunity. Without a way to proactively respond to market trends and forces, businesses risk getting left behind in the new data-driven economy.

Come back next time and we’ll look at the strategic role of data and how it is driving change in the financial sector.

Submitted by Paul Sullivan via ModedaWeb.


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Sergey Sanko
Sergey had started an IncomeClub after years of being an investment advisor for high affluent investors and managing fixed income securities. He is the lead investment advisor representative and holds a Series 65 license. Sergey earned his Executive MBA degree from Antwerp Management School.
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