With advancements in technology, the amount of data available for analysis has increased, providing businesses with opportunities to gain insights that were once unattainable. Data is essential for fintech companies to provide personalized and relevant communication, gain insights into customer behavior and preferences, detect and prevent fraud, and assess, monitor and mitigate risk. Data analytics is not a luxury; it’s a necessity for fintech companies looking to stay competitive in an ever-evolving landscape.
The financial services industry has seen a remarkable evolution in the use of technology, from the days of the calculator to the era of Artificial Intelligence. The intersection of Finance and Technology has resulted in the coinage of the term ‘Fintech’. According to Columbia University, “FinTech (Financial Technology) is a catch-all term referring to software, mobile applications, and other technologies created to improve and automate traditional forms of finance for businesses and consumers alike.”
We now live in the digital age of finance, where data is king and fintech reigns supreme. In today’s fast-paced world, where everything is just a click away, financial services have had to evolve rapidly to meet the needs of modern customers. This journey has been fuelled by exponential growth in computing ability that allows fintech companies to harness the power of huge volumes of structured and unstructured data that were hitherto untapped.
To understand the importance of data in fintech, we need to look at how the fintech industry operates. At its core, fintech is all about leveraging technology to make financial services more accessible, efficient, and personalized. To achieve these goals, fintech companies need to collect vast amounts of structured and unstructured data and analyze it to generate actionable insights.
Data Analytics for Fintech: The Magic Wand
Data analytics is the magic wand that transforms raw data into actionable insights and opportunities for fintechs.
Customer Orientation
According to a research report titled ‘The Challenge of Customer-Centric Banking’ by Genesys, 61% of banking executives say that expectations for customer experience are continuing to rise and 45% of them acknowledge that they are finding it difficult to match them.
However, we live in what is the ‘experience economy’. Banks cannot afford not to meet these expectations, and the only way to do so is by using Big Data. According to Accenture, if banks focus on cultivating deeper personal connections with their customers, they could potentially increase their revenue from primary customers by as much as 20%.
Thankfully, data analytics provide fintechs with the ability to gain insights into customer behavior and preferences. By analyzing data, both structured and unstructured, from various sources, such as transaction data and customer profiles, fintech companies can understand their customers much better and offer hyper-personalized services and products.
Many banks and fintechs now send out marketing material and promotions based on the specific spending habits of their customers. For instance, in the case of a customer who has large spends on dining out, the bank might send her/him a dining card with special discounts at various restaurants.
Risk Management
Another benefit of using data analytics in fintech is the ability to identify potential risks. This is corroborated by the EY Global Board Risk Survey 2021, which concludes that leading companies take a data-driven approach to risk management. Financial institutions have long recognized that sound credit decision are the outcome of data-driven risk management, especially when the institution is acquiring customers at a rapid pace.
According to a McKinsey and International Association of Credit Portfolio Managers (IACPM) global survey, financial institutions are increasingly utilizing machine-learning models for risk scoring of SMEs (>70% respondents) and developing early warning systems (>50% respondents). This is significant because traditional financial metrics to evaluate the creditworthiness of SMEs fall short as data about proprietorship and partnership firms may not be available from structured data sources (corporate registries etc).
Data gathering tools can mine unstructured data about a business and its owners from social media and a variety of other sources. Individual data elements may not be useful but when all the data about an entity is combined and mined using data analytics tools, it throws up interesting risk insights.
Another advantage is quicker credit decisioning. In fact, 37% of the respondents in the McKinsey IACPM survey felt that innovative data sources and advanced analytics have sped up credit decisions for SMEs. Such results are being applied to retail finance as well to reach the underserved population that has traditionally never been considered a target for credit products. The rapid proliferation of Buy Now Pay Later (BNPL) financing is a testimony to the successful use of data analytics in making credit accessible to such customers.
Fraud Prevention and Control
The Association of Certified Fraud Examiners (ACFE) reports that an average fraud incident results in a median loss of $117,000 and typically remains undetected for 12 months. Thankfully, with the emergence of data science-based technology and fraud analytics tools, the time taken to detect fraud can be significantly reduced, resulting in lesser losses and lower risk exposure. Organizations all over the world have begun to acknowledge this and are actively investing in data-backed tools.
In the ACFE 2022 Anti-Fraud Technology Benchmarking Report survey, more than 40% of respondents said they have accelerated their use of data analytics amid the pandemic.
Almost 60% of respondents were positive about increasing their anti-fraud tech budgets in the forthcoming years, with most of them adopting advanced analytics particularly Artificial Intelligence (AI) and Machine Learning and predictive analytics/modeling. Anomaly detection, behavioral analysis, clustering analysis, gap analysis, and transaction analysis can be efficiently done by these tools to detect fraud. As fraudsters find newer ways to beat the system, banks and fintechs have little choice but to invest in intelligent systems that leverage data to prevent these actors from succeeding.
Innovation and Growth
Data and analytics enable fintech companies to identify trends, uncover new opportunities, and drive innovation. By staying ahead of the curve, fintech firms can develop new products and services that cater to evolving customer needs and preferences. One way to do this is through Application Program Interfaces (APIs).
McKinsey’s State of APIs in Global Transaction Banking (GTB) 2021 survey reports that more than 90% of respondents had plans to use APIs to generate additional revenue from existing customers and 75% wanted to deploy APIs to generate revenue streams from new customers. Almost 75% of the respondents also felt that APIs give them the ability to innovate and integrate with third-party capabilities. This is significant because the increasing adoption of open banking regulations is allowing third-party fintech companies to access customer data from banks and financial institutions.
India’s Unified Payments Interface (UPI) is a prime example of open banking. By providing an open platform that enables third-party developers to build innovative solutions on top of its infrastructure, UPI is driving innovation and promoting financial inclusion for 1.4 billion Indians. The JAM Trinity (Jan-Dhan Bank Account, Aadhar identification number, and Mobile No) has become the backbone of India’s digital economy and is the foundation of the world’s largest direct benefits transfer scheme. In fact, Mastercard’s 2022 New Payments Index reveals that Indians are the most willing consumers in the Asia-Pacific region to use emerging cashless payment methods—a whopping 93% have possibly used such a method in the past year.
Regulatory Compliance
In a recent survey by RegTech firm SteelEye, 50% of respondents stated that at least half of their compliance staff undertake administrative or repetitive tasks. Herein lies one of the most important but less-glamorous benefits of data-driven tools—their ability to digitize the compliance function in banks and fintech companies.
Given the highly regulated nature of the BFSI sector, it is imperative that players ensure compliance with the relevant laws and regulations to reduce the risk of penalties and help maintain a positive reputation in the industry. The SteelEye survey says that 31% of the respondents have fully deployed AI and ML into their compliance processes and an additional 25% are investing in the technology and still implementing it. In fact, 100% of those who have implemented the technology reported a significant improvement in the quality of their management information.
A Comprehensive Approach to Data
Much has been written and said about the use of structured data, but companies are now realizing that harnessing the power of unstructured data is the key to unleashing the true potential of fintech. Unstructured data, which could be from various formats such as audio, video, email files, call centre transcripts, online reviews, chatbot conversations, news articles, research reports, and social media posts, offers significant analytical potential and can be integrated with structured data to provide a comprehensive view of customer behavior and financial trends.
By leveraging both structured and unstructured data, fintech companies can drive personalized marketing, risk assessment and monitoring, cash flow management and offer risk-based pricing to customers.
To fully harness the power of unstructured data, fintech companies must break down data silos and adopt scalable data hubs that can store, analyze, and report data from diverse sources. By embracing unstructured data and investing in the right tools and technologies, fintech companies can unlock new opportunities while better managing risk.
The Future of Fintech
The future possibilities of data in fintech are vast and exciting. As technology continues to advance, and more data becomes available, fintech companies will have new opportunities to leverage data to improve their products and services. As the volume of unstructured data generated and collected grows, the importance of language models will increase significantly. Natural language processing (NLP) is now becoming more prominent in our daily lives, particularly in customer engagement through conversational AI such as chatbots.
As AI advances, newer models such as the third-generation Generative Pre-trained Transformer (GPT-3) developed by OpenAI are able to perform tasks like writing articles and poetry, coding, and generating reports in a more natural, human-like manner. GPT-3 has 175 billion machine learning parameters; GPT-4 is expected to be several times larger and it will have huge implications not just in finance but in every field of human endeavor.
I would say, the evolution of data in finance is like a computer game; every time you think you’ve conquered a level, there’s a new challenge waiting around the corner. But with each new level come new opportunities, and those who are able to adapt and innovate are the ones who will come out on top.
