Financial technology, commonly referred to as FinTech, has been one of the fastest growing areas in technology innovation, and has recently become a favourite industry for venture capitalists to pour their investments in.
Over time, Fintech has evolved and disrupted almost all aspects of financial services, including payments, investments, consumer finance, insurance, securities settlement, and cryptocurrencies, among others.
One thing that Fintechs have heavily relied on in order to achieve this is machine learning, artificial intelligence, predictive analytics and data science to simplify financial decision making and provide superior solutions.
At the core of that is data.
Fintechs are drowning in data, and they must implement contemporary data tooling, facilitate data access, and democratise data skills in order to achieve the full value of their data. If data does not reveal anything, it is of little to no value in and of itself. The value comes from what is done with the data, not from the data itself.
Why is it important for fintechs to use data?
A fintech organisation that bases its decisions on facts, figures, statistical trends, analytical patterns, and forecasts is said to be data-driven.
Being data-driven refers to a strategy method of utilising data insights to find new business possibilities, provide better customer service, increase sales, and more. This enables firms to carefully prepare and make decisions based on facts in order to fulfil their business objectives.
With data and data science they can enable their business to have the following:
Fraud Detection
Data science is very useful in fraud detection.
Fintechs use Big data and data analytics techniques to analyse vast amounts of online fraudulent transactions data that is used and modelled in a way that can help Fintechs and other authorities flag or predict fraud in future transactions.
This is done using data science and machine learning techniques such as Deep Neural Networks (DNNs).
Data science enables monitoring transactions in real-time and flagging the ones that fall outside of the average. Data science not only enables the early detection of anomalies but also the prevention of any possible cyber attack.
Payment and Transactions
Fintechs have access to customers historical payment data and that is important data for their business.
With a customers payment and transaction data Fintechs can make an evaluation of the customer’s payment and purchases history at granular level making classification of payment records and allowing Fintechs to customise additional services to their client’s needs, creating customised seamless experiences for their users.
Revenue and Debt Collection
Historical customer data, like individual credit risk evaluations make it possible to evaluate who is worth giving money to. For a lending business this information allows for them to reduce the risk of the business.
Data science helps with accurately assessing the payment schedule of a person which also makes a smooth revenue and debt collection.
These are only a few examples of how data is used in Fintechs and how important it is for them.
What about other organisations?
Other organisations, aside from Fintechs, can also be financially data driven. It may seem like a daunting task for organisations to become more financially data-driven, but with proper focus, planning and leadership, things eventually fall into place. In the next issue, we will explore how an organisation can use its financial data to make better driven decisions.