Data Science and Machine Learning in FinTech
The use of data is not new. It can be tracked to ancient history, as early as C 18,000 BCE, then Palaeolithic tribespeople was using marks on sticks to track trading activities. Advance data analytics and data science logics like regression, fuzzy logic, Neuro-fuzz, etc. are also not new. Advancement in cloud technology has immensely improved our ability to store a large amount of data and process them at scale. This advancement enabled engineers and data scientists to use data and algorithms together for decision making like never before.
Almost all business sectors, including banking & finance, are going through a digital transformation since the late nineties. This digitalization trend has resulted in various kinds of data generation ranging from IoT events, transactions, customer relationship, customer behavior, feedback, and many more varieties of data, enabling multiple opportunities for decision-making using statistical, machine learning, and artificial intelligence techniques.
Here are some of the ways Big Data & Data Analytics are helping the BFSI sector:
For operational efficiency:
Financial Institutions (FIs) are analyzing IoT and transaction data to improve system and service uptime. Some examples are proactive ATM maintenance, better cash management, virtual teller workload management, ATM location & service identification, proactive cloud services operation monitoring, proactive issue resolution, and more.
For customer experience improvement:
Customer experience starts with understanding your customer, and data science allows you to understand your customer better. FIs are using transaction data, CRM data, social media data, and other behavioral data to gain a much more accurate understanding of their customers. AI chatbots are providing personalized assistance to customers.
For growth opportunities:
While data analytics is used in finding hidden revenue opportunities from operational data, it is also getting traction in creating new differentiated product offerings based on predictive behavior analysis, recommendation & personalization engine. New solutions offering highly personalized services based on real-time decisions powered by sophisticated personalization and recommendation engine are now far and wide. AI and machine learning are also in use for targeted marketing, sales, and renewal conversation.
Digital transactions require risk analysis and fraud detection. FIs are relying on simple logistic regression techniques to complex machine learning techniques like the neural network, Recurrent Neural Network, Long Short Term Memory Network, and Naive Bayes to achieve this.
Data science and machine learning flourish on a diverse set of data and specialized skillsets. The fintech sector has both the data and the appetite to invest, so we expect wide adoption of AI & ML in this sector.