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Role of Data Analytics in the Fintech Industry’s Growth

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By Maninder Singh Grewal, Chief Growth Officer, mPokket

Be it chatbots, internet cookies or diverse types of social media posts, all these are assisting companies in analysing consumer habits, actions and transactions to provide personalised experiences. Among financial entities, fintech firms have been at the forefront of leveraging the power of data and analytics to get ahead.

The Four Vs
What is it that makes data analytics so useful for fintechs and other BFSI companies?

As per IBM data scientists, the power of big data can be encapsulated in four Vs: volume, variety, velocity and veracity.

Volume is mirrored by the mammoth mounds of consumer data generated daily. With the help of sophisticated algorithms, companies can collect, process, analyse and filter the enormous troves of data to infer meaningful insights about customer behaviour and preferences.

Variety comes in the form of millions of audios, videos, text messages, online activities, GPS data and more. Advanced analytics tools then collate this fragmented information into structured bytes via a highly organised, predetermined format that is easier to use.

Velocity refers to the exponential speed at which huge data dumps are generated and processed through advanced analytics systems, making them usable.

Veracity (or accurate facts) concerns both the availability and quality of data. In the case of big data, variety or massive mounds of information can raise doubts about the data quality, which makes the data sources seem dubious. For conventional business analytics, the data sources could be smaller in quantity and variety. As a result, organisations can have more control over such data, leading to greater veracity or accuracy.

The Criticality of Big Data for Fintech
New age fintech companies have been exploiting the benefits of big data for predicting customer behaviour and then developing complex risk assessments that differentiate them from legacy financial players. The velocity of real-time information allows fintechs to disrupt traditional lending services while simultaneously adapting to the changing marketplace.

The ability of fintechs to dispense loans and other services at the click of a button has meant legacy players have no option but to deploy digital tools and keep pace with the changing market dynamics. By processing vast data sets at lightning speed, fintechs are well-placed for faster decision-making and creating bespoke customer experiences.

They are consistently able to spot and solve a new problem area for the customer to incrementally grow the differential between them and the legacy players.

Rather than using the conservative risk assessment tools, fintech firms utilise big data for knowing and engaging with the customers individually. This provides multiple advantages such as:

Assessing credit scores: Given the unsecured nature of most fintech loans, applicants’ credit history or score helps in assessing the possibility of default by prospective borrowers. In tier 2 cities and beyond, however, some people do not operate bank accounts or use cards, due to which they lack credit history. Yet, customer profiling is possible without traditional banking history, thanks to data science tools such as psychographic segmentation, analyses of SMS messages and geocoding, among others. These tools help in predicting possible defaulters. Consequently, machine learning and its allied technologies have become instrumental in offering loans to people who are outside the purview of conventional banking.

Gauging customer lifetime value: For businesses to scale operations, they must sell more through new customer acquisitions. Considering the highly competitive market, this needs to be done with reduced costs. With the rapidly evolving market dynamics, companies must know their customer’s lifetime value (CLV), which allows them to focus their efforts on the best clients. Through better CLV understanding, fintechs can employ more pinpointed strategies for retaining the most profitable customers. For calculating CLV models, machine learning is an extremely efficient option.

Augmenting security: As frauds keep rising with an increase in digital banking activities, fintechs are using big data to create robust fraud detection systems that can sense unusual activities or transactions before fraud occurs. Fintech apps also offer a smooth means of communication to warn customers of potential security risks, thereby protecting their money.

Offering innovative products and services: By using data and analytics, fintech firms have uncovered new opportunities for hyper-personalised services, upselling, cross-selling, optimising market outreach and creating new synergies between different products while managing strategic risks. One example of innovative products is ‘buy now pay later’. BNPL loan offers are available interest-free if customers make payments within a predetermined period. Through BNPL, consumers can conveniently purchase products they would otherwise have not bought. Backed by real-time automated underwriting, this payment system is powered by data analytics to verify customer information and creditworthiness, thereafter, processing the line of credit almost immediately.

Driven by the above benefits, merchants can ensure existing customers display more loyalty. Through data science and ML tools, loyalty programmes, product offers and dealer discounts can be utilised optimally. Many of these innovative products would not be possible on a mass scale without the benefits of big data in uncovering behavioural insights about customers and even creating profiles of unbanked cohorts.

Nonetheless, what is seen today remains just the tip of the fintech offerings’ iceberg. In the coming years, more innovative products and services will accelerate the growth of fintech firms, transforming India’s lending landscape.

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