How Data Science Helps Empower Fintech
Our world has become increasingly connected thanks to the internet. The same connectivity has also changed user behaviour on how they consume information or react towards marketing content, which affects brands and industries alike. This emergence of unique consumer behaviour coupled with the advancement of technology has disrupted the financial world.
Banks and financial institutes are left with no choice but to adopt innovative technology to cater to their customers. In this article, we see how data science empowers the Fintech industry and is the core element of the current transformation of the financial sector.
Payment and Transactions
Predictive analysis of transaction volume is key to enhance the financial product value for customers. Data science enables better record management and data classification of payment records, which help the banks to meet their clients’ needs. These records also allow the banks to develop additional services or recommendations for their clients’. In short, data science helps facilitate the analysis of customer behaviour across various channels of engagement.
Fraud Detection and Prevention
With evolving business models and the rise of e-commerce, fraud detection remains as one of the top priorities in the financial industry. Real-time assessment and detection of fraudulent payments are far more superior now thanks to data science. Fraud identification requires real-time actions and immediate responses from the authorities to control losses.
AI Financial Advisors
While we can argue whether or not artificial intelligence can replace an actual financial advisor, there is no denying how quickly AI learns from a bunch of humans that the gap in understanding those unique human nuances is closing fast. We already have the software and mobile apps that are capable of recommending wealth planning and investment plans based on a person’s financial capacity.
Customer Journey and Acquisition
Customer Acquisition Costs and Customer Lifetime-Value are – as in most business models – key metrics for banks and financial service providers. Thus, minimizing churn rates and optimizing conversion rates are crucial activities within most financial organizations. Data science enables the structured understanding of all kinds of interaction data – from unstructured text, social network activity to direct feedback rankings – throughout the entire customer journey. Thus, enabling the efficient spotting of customers, which are likely to quit the service or identify those, which could be targeted for upselling activities.
The financial industry sits on a huge pile of data that continues to grow every second. These data range from various consumer activities and sources, ranging from financial transactions to online reviews. With this tremendous growth in data, a team of data scientists will be able to do more in-depth and ‘profitable’ analysis for financial institutes.
Descriptive, Predictive and Prescriptive Analytics in the Fintech industry
Mass amounts of data provide a competitive business advantage for financial institutes. These data will be transformed into practical and useful information through a combination of descriptive, predictive and prescriptive analytics.
Descriptive analytics in a Fintech context provides a summary view of an institution’s financial health based on past financial data.
Predictive analytics focuses on predicting the future with available data.
Prescriptive analytics goes beyond predictive analytics and evaluates new ways to conduct business.
Fintech creates a competitive advantage for financial institutions through the combination of data science and AI. These factors will continue to evolve and revolutionize the Fintech industry and will drive the creation of new financial services and products for consumers. Failing to embrace or adapt these advancements will negatively affect financial institutions, especially with the market hold and presence.