FinTechDecoding Data: Fintech’s Powerplay With Big Data Analytics

May 17, 20240


In recent years, the financial sector has undergone significant changes due to the integration of technology and finance, which is commonly referred to as financial technology (hereinafter referred to as “Fintech”). Fintech encompasses various innovations altering the delivery, accessibility, and management of financial services. This transformation has led to a growing need to comprehend the evolving fintech landscape and the influence of emerging regulations on its trajectory.

Fintech encompasses a wide array of applications, such as mobile banking, peer-to-peer lending, robo-advisors, blockchain-based payments, and algorithmic trading. These technologies leverage advanced digital tools to disrupt traditional financial services, often providing faster, cost-effective, and user-centric alternatives to conventional offerings.

Technological advancements like blockchain, big data analytics, and cloud computing are central to driving innovation within the fintech sphere. Despite their potential benefits, fintech companies encounter regulatory hurdles as they navigate the intricate legal frameworks governing financial services. The dynamic nature of fintech and its disruptive potential have spurred regulators worldwide to develop novel approaches to oversight, aiming to strike a balance between fostering innovation and safeguarding consumer interests and financial stability.

In this era, where the digital transformation of financial services is both a boon and a battleground, this blog meticulously navigates the intricate relationship between Fintech and the evolving landscape of data privacy laws.


Big data analytics is revolutionizing the fintech landscape, empowering companies to tap into extensive datasets to uncover valuable insights into customer behaviour, market trends, and risk management strategies. By employing sophisticated analytical techniques like machine learning and predictive modelling, fintech firms can customize their services, fine-tune pricing structures, and strengthen fraud detection methods with unprecedented accuracy. This entails integrating existing customer knowledge with data sourced from various channels to gain deeper insights into individual preferences and market segments.

The sheer scale of data generated on a daily basis, is estimated at around 1.1 trillion megabytes by nearly 4.7 billion internet users, underscores the indispensable role of big data in the financial sector. Big data analytics acts as a cornerstone for transforming vast datasets into actionable intelligence, equipping financial institutions with a competitive edge and driving operational improvements.


In modern times, the traditional approach of evaluating an individual’s financial status exclusively through bank account records has become outdated. The depiction of a present-day consumer is notably complex and multifaceted, incorporating not merely transactional data but also a wide spectrum of personal characteristics, digital interactions, spending patterns, media consumption,  social media activities, travel behaviours, geographic locations, topical preferences, and assorted additional facets.

This evolving scenario has spurred a growing number of businesses to incorporate big data analytics into their financial activities, capitalizing on the extensive pool of data accessible to extract refined and nuanced insights.

1. Big data in online payments

Big data analytics have been used in the industry for security and fraud detection purposes for quite a while. These systems combine big data to instantly assess risk levels and the amount of credit available to the user. A US-based company uses data analytics to target young professionals and help them pay, save, invest, and borrow money most effectively and securely.

2. Big data in Insurtech

Modern insurance companies take full advantage of big data and actively use machine learning to create highly customized, low-risk insurance offers that address the specific needs of particular categories of users.

One of the Swedish companies is using massive amounts of the ground information and road accident statistics and their business skilfully applies machine learning to aid insurance companies in estimating risks and adjusting pricing levels.

3. Big data and financial technology in Real Estate

Real estate fintech companies focus on capturing data from various sources and applying data analytics to make the right offers to the right people at the right time. With detailed information constantly transmitted to the back office, they can optimize rental and maintenance offers dynamically and keep their customers happy for years

4. Big data in trading and Investing

The adoption of real-time big data analytics in finance and the application of self-learning algorithms now define the varied landscape of online trading platforms.

Leading global investment banking company, Goldman Sachs uses quantitative analysis of investment candidates, to also assess the media context and tonality of the public discourse about them, thus achieving a higher accuracy and efficacy of investments. The same mechanisms is being applied by smaller businesses for brand monitoring and adjustment of trading strategies.


India continues to grow as one of the largest Fintech markets globally. India is the third largest Fintech market, following the US and the UK, with several Fintech unicorns, startups, and funding.

“The trends of change Trends shaping India’s Fintech Sector” published in September 2022 by Payment Council of India (hereinafter referred to as “PCI”), Fintech Convergance Council (FCC), and EY along with the “$ 1 Tn India Fintech Opportunity: Chiratae Ventures-EY FinTech Report” released in 8th August, 2022 by EY and Chiratae Ventures shows that the next decade will record a 10x growth in the Indian FinTech market to achieve $200 billion in revenues.

Due to these recent trends in the Indian Fintech sector the question of “How you use data is more important than how much data you have” is much important. The primary regulatory agencies in charge of this sector are the Reserve Bank of India (hereinafter referred to as “RBI”), the Insurance Regulatory & Development Authority of India, the Securities Exchange Board of India (hereinafter referred to as “SEBI”), the Ministry of Corporate Affairs, and the Ministry of Electronics and Information Technology (hereinafter referred to as “MEITY”).

In India, the Fintech regulatory structure is significantly fragmented, with no uniform body of rules or norms governing all Fintech services. The sections that follow go through 2 important legislations which regulates the data protection from Fintech companies in India.

The Digital Personal Data Protection Act, 2023 (“DPDP Act”)

The DPDP Act is India’s first dedicated data protection legislation, and it establishes a framework for the processing of personal data in India. Fintech companies are now required to implement enhanced data security measures to protect sensitive personal data.

Section 6 of the DPDP Act discusses the concept of “Consent” which mandates the consent of the Data Principals or data user for the purpose of processing of her personal data for the specified purpose. Along with various rights it imposes duty on the fintech and other companies under  Section 8 which states “General obligations of Data Fiduciary” to protect personal data of the data principal in its possession or under its control. The DPDP Act prescribes penalties for non-compliance, with a maximum penalty of INR 250 crore (approximately $31 million) for certain violations.

This legislation is designed to bolster data protection and accountability for entities such as internet companies, mobile apps, and Fintechs that handle citizen’s data. Its key provisions, such as informed consent and data minimization, will have a significant impact on how fintech companies collect, store, and use customer’s data.

The Consumer Protection Act, 2019

The Consumer Protection Act, 2019 is a comprehensive legislation aimed at protecting the interests of consumers in India. While it does not specifically focus on fintech or data analytics, its provisions have implications for these sectors.

According to Section 2(47)(ix) of the Consumer Protection Act, unfair commercial practice are defined as the “publication of consumer’s personal information submitted in confidence unless required by law or in the public interest,”. The broader framework of the Consumer Protection Act establishes how the companies handle the consumer data and the redressal mechanism for the consumer’s grievances.


In the realm of Fintech, where data is the lifeblood of innovation and customer service, the regulatory landscape in India is evolving to ensure both dynamism and protection. The emergence of legislations like the DPDP Act and the Consumer Protection Act, 2019 underscores a concerted effort to balance the power dynamics inherent in data-driven financial technologies. These regulations highlight that it is imperative for Fintech companies to prioritize data protection, consent, and accountability while leveraging consumer information.

As India propels towards a projected 10x growth in its Fintech market, the effective decoding of data becomes pivotal. Fintech’s powerplay with big data analysis must navigate not only technological advancements but also the ethical and legal dimensions of data utilization. Ultimately, the success of Fintech in India hinges on striking a delicate balance between innovation and safeguarding consumer privacy, ensuring a sustainable and inclusive digital financial future.

– Team AMLEGALS assisted by Ms. Harshita Mewade (Intern)

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