FinTech is one of the biggest sectors which has witnessed tremendous growth in terms of technological advancement. As the competition in the industry keeps advancing, key players have no choice but to upgrade to the latest technologies to provide better and faster service to its customers.

While technologies like the Internet of Everything (IoE) and blockchain have made substantial inroads in the sector and have disrupted the way banks and other financial organisation offer their services, the next wave of change will be driven by emerging technologies like artificial intelligence, big data and machine learning.

Why Is Hypersonalisation In Banking Important?

With the advancements in technology, customers are looking for more nuanced and personalised banking experience which can be attained at the comfort of their home.

Moreover, banks have realised the economic advantage of individualisation which can bring in considerable cost reduction. By digitising omnichannel, payments and by providing diverse cutting-edge products with AI, ML and big data capabilities, banks are now trying to cater to customer individualisation while meeting the larger organisation goal.  

According to a recent study, the only way that banks can achieve hyper-personalisation is by utilising digital technologies like cloud-based applications, platforms and infrastructure, mobile devices and data analytics.

For the study, which compiled the responses of top banking executives from North America, 66% of the respondents believe the ability to offer more highly individualised experiences to customers ranks as one of the top three priorities for their organisation.

While only 31% of consumers surveyed believe their financial institution knows their needs well, and only 34% thought their bank had their best interests in mind.

How Is AI Enabling Hyper-personalisation?

According to a 2018 study by Analytics India Magazine, 38% of total analytics revenue in India ($1 billion, approximately) comes from banking and finance at a 31% growth over 2017.

With Robotic Process Automation and AI considered to be the cornerstone for the future of banking, AI is believed to drive the second wave of automation in banking by increasing capacity and free employees to focus on higher-value projects.

Speaking about the role of AI and ML in driving personalisation in banking, Sankat Chauhan, who leads Data Science and Products department at Goals101, a leading FinTech startup said, “While technologies such as Big Data, AI and ML are disrupting the BFSI industry, hyper-personalisationisation is the next big thing, as it will lead to  more resilient, customer-focused bank of the future that incorporates the virtues of non-banking rivals.”

With AI and ML emerging as a powerful disruptor in the finance industry, Chauhan points out the key ways in which AI will enable hyperpersonalisation:

Will make banks more customer-centric: Hyper-personalisation is core to any initiative that banks undertake to become more customer-centric. They understand that everyone’s needs are unique and want to customize products accordingly.

“ML and AI will help banks achieve this at a scale capable of managing their volumes and more,” Chauhan says about the key functionality.

By adopting technologies like chatbots to cater to the burgeoning demands of customers, banks can easily handle a large pool of customers thus driving personalisation.

Will drive operational and cost efficiencies, driving profitability: With the help of ML/AI, banks are able to partially or fully automate their processes, increasing operational and cost efficiency, which is translating into greater profitability.

Will help with fraud detection and risk management: Financial service providers are inundated with big data, especially unstructured data. With the rise of AI-based systems, it is now possible to analyse huge volumes of business data and find out how well the internal control systems are operating.

“Financial organisations are increasingly adopting a machine learning-based approach to augment their algorithmic rule-based approach towards surveillance and risk management. Machine learning techniques are evolving in nature and can keep a few steps ahead of human and rule-based fraud detection systems,” Chauhan adds.

Gain customer insight: By looking beyond traditional data set point and by tapping into information like customer behaviour, their social interaction and even getting minute information like health and important event dates can enable a higher degree of personalisation and customisation.