How Big Data and Blockchain are enhancing FinTech

Medium | Maria Wachal  | Oct 29, 2019

Data driven ideas - How Big Data and Blockchain are enhancing FinTechThe volume, velocity, and variety of data that financial companies are relying on is overwhelming, but there are game-changing technologies that can tame the data and provide indisputable value.

FinTech companies want to provide personalised and innovative services and products to accelerate customer experience. Customer centricity enabled with data analytics is the number one priority for this sector. Injecting financial sector with advanced tech like Big Data and Blockchain can enable banking and finance go far beyond cashless payments and mobile services toward personalised customer experience that will transform FinTech further.

The revolution number 4

Humankind witnessed so far 3 industrial revolutions that impacted production. During the 1st revolution people started using steam power and machines replaced muscle power. The 2nd revolution changed our lives with the introduction of electricity. Finally, when the computer chip was invented, we witnessed the 3rd revolution which not only automated production, but changed our personal lives for good.

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The 4th industrial revolution — the digital transformation — is happening right in front of our eyes. It is fuelled by Big Data powered by Artificial Intelligence and Machine Learning, and technologies like Blockchain, that can significantly impact the way the world is progressing.

Big Data in FinTech

We live in a world of hyper-personalization and financial firms want to deliver the promise of personalized customer experience. Being data-driven and leveraging indirect information about customers to transform them into valuable insights is crucial. The notion that can speed up the evolution in this area is the implementation of the real-time self-learning data models that can provide analytics for every single outreach and interaction.

When we talk about collecting data about customers, we are typically referring to 5V characteristics of Big Data:

  • Data Volume — a huge volume of quotes, market data, and historical trade data.
  • Data Velocity — the speed at which the data is being generated. The faster trade data is processed, the faster it can be applied for trading.
  • Data Variety — the existence of various formats and sources of data that can be be structured or unstructured.
  • Data Veracity and Data Value —the quality and usefulness of the data.

There are various opportunities for applications of Big Data in the finance world that could significantly boost predictiveness and relevancy of data, because data needs to be rigorously analysed and assessed for its veracity and functionality in order to avoid constraints of quantity vs quality.

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Fintechs need personalisation

Traditional banks will slowly go out of business. Innovative FinTech companies want to deliver personalised and cost-effective finance products. In order to do so, they need to utilize enormous numbers of data from various touch-points.

The old approach to customer segmentation, meaning dividing customers into groups based on data related to demographics, geography, economic status as well as behavioural patterns, can be enhanced with Big Data algorithms that introduce relevance-drivers. They play a crucial role in determining the minimal overlap across segments and minimal separation within the segments.

Big Data and ML applications enable businesses to get to know customers on a more granular level by providing better analytics and forecasting. Personalisation is already gaining grounds in insurance services. The more of data insurers have on their customers, the more they can adjust their premiums accordingly. Data-driven insights can also further confirm data analysts’ intuition and reduce human errors when developing a risk profile of customers requesting financing or underwriting and credit-scoring in loan management. The massive growth and availability of real-time data also improves the accuracy of marketing and devise customer retention/loyalty programmes.

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FinTechs need automation

Financial companies need to harness big data to aid in processes like trading, risk or fraud. Computer algorithms are able to learn and adapt to real-time changes and conduct trades autonomously. With machine learning systems can automatically learn and improve to identify risks better and to anticipate and avoid them. Every FinTech company wants the trading algorithms that outperform those of their competitors. Supervised machine learning gives higher accuracy than any other mathematical models and enables trading decision based on more than stock information, way better than those with the human involved.

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