How Big Data and Blockchain are enhancing FinTech

The 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.

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.

— 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.

— 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.

— 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.

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.

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.

Harvesting data and being able to react upon it in real-time is a game changer in many industries, including FinTech. In one of our projects we had to feed a recommendation engine with data from Google Analytics, among others. It had to happen in real-time to serve tailored content while the client is exploring the shop. Check out this blog post about the technical challenges we faced while integrating different toolsets:

Blockchain in FinTech

Financial institutions process large amount of sensitive and confidential information. It makes them highly prone to cybersecurity incidents. Some of it can be avoided with regulations like GDPR, but in order to truly gain customers’ trust, FinTechs have to go further and strengthen cybersecurity.

FinTechs need RegTech and higher trust

Blockchain technology automates processes that need to meet certain regulations before being executed. The ever increasing demand of compliance within the financial industry prompts the shift towards secure technology to better store, track and retrieve data. Transactions on the blockchain are irreversible and encrypted. The same copies of the entire chain are stored on different servers deployed throughout the network. The history of records can be reviewed and verified, which means that audits are applicable and the trust is built. By removing middle parties Blockchain has no central point of vulnerability to be exploited, making the technology transparent and safe, and a fit application for RegTech.

“Regtech is the management of regulatory processes within the financial industry through technology. The main functions of regtech include regulatory monitoring, reporting, and compliance.”

FinTechs need data integrity and accessibility

Another thing that has become one of the biggest regulatory challenges for FinTechs is Durable Medium. It is a solution in the context of circulation of documents in B2B and B2C relations. Durable Medium can be any instrument, there is no specific technology suggested, but it is a perfect case for the second generation Blockchain.

Durable Medium — “(…) material or tool enabling the consumer or entrepreneur to store information directed personally to them in a manner allowing access to information in the future for the time period appropriate to the purposes for which the information is used and which allows to reproduce the stored information in unaltered form.”

With its selective transparency blockchain is becoming a new part of the system. It can secure much faster and definitely cheaper method of signing documents electronically where users can reproduce the information at any time without relying on the action of another party and stored data (documents) cannot be altered in any way from the time of submittal of the declaration of will.

Wrap up

Data is a lifeblood of FinTech. In order to really take off and allow cost-effective and widespread implementation of innovative solutions that tame data, regulators, tech leaders and businesses should build the necessary capabilities in operations to maintain technological convergence. The digital revolution gives a tremendous opportunities in finance world for big data and blockchain applications.

Digitalization is not only the change in applied technologies and business models, but also a change of culture and mindset. This journey calls for an experienced tech partner that can elevate the initial idea by translating FinTech’s needs into business-led functionalities of a future software.

We’re always trying to grow here at SoftwareMill, so we’re thrilled to be recently recognized for our efforts by Clutch’s research as a Top Software Development Firm in the Financial Industry .

Feedback like this is what we strive for, since our goal is to accomplish projects the way our client’s want it done. We’re always happy to be recognized, but we couldn’t do it without our clients. We understand how difficult some projects with software providers can be, so our goal is to make it as easy and productive a partnership as possible, something our previous client’s make note of in their reviews:

“They have an excellent ability to work remotely without it being a barrier. They fit seamlessly into our team.” — CTO, Computer Software Company

We help companies scale their businesses and we specialise in Scala, Kafka, Akka and Cassandra, among other technologies. Our areas of expertise include distributed systems, big data, blockchain, machine learning and data analytics. If you’re curious about how our collaboration could look like, check out our website!