Does your FinTech Business Need Data Analytics Consulting?

6 min read
Apr 16, 2022 4:00:00 PM

"Data is everything," as the adage goes, in the FinTech and Financial Services industries and every sector. The ability to transform raw data into meaningful, actionable insights resides where the true power lies.

Businesses of all shapes and sizes are discovering the value of data, whether leveraging internet cookies to create tailored online experiences or analyzing bank activities to provide budgeting advice. Fintechs, in particular, are looking at new ways to use big data to challenge existing financial institutions with a customer-centric approach. So, what are the big data applications in finance, and how can your company personalize consumer experiences?

Businesses have effectively used insights derived from data analytics reports to stop consumer churn, avoid accidents, and detect and halt financial fraud and errors since FinTech observed the digital transition. In addition, FinTech has increased its operations thanks to big data and related quality assurance services, utilizing current technologies such as the Internet of Things (IoT), Blockchain, Artificial Intelligence/ Machine Learning (AI/ML), and intelligent data discovery. These are the primary reasons SMBs and enterprises prefer to outsource data analytics.

So let's take a look at the potential of big data fintech firms and provide some tips on how to execute effective customer-focused marketing campaigns.

What Makes Big Data BIG?

The concept of gathering data to improve consumer experiences isn't new. From small-town greengrocers to big-city bankers, morsels of data have long been used to build a better image of their consumers. The BIG in 'big data,' on the other hand, supplies firms with a treasure trove of consumer information that has the potential to turn the financial world on its head.

The three Vs. can be used to define 'big data':

  • Volume

By 2020, the entire digital universe is predicted to have grown to 44 zettabytes (21 zeros). Sophisticated algorithms can process, analyze, and filter massive data volumes to extract relevant insights about individual consumers.

  • Variety

Excel spreadsheets and brutal disc photographs are no longer the only ways to save data. Big data gathers data from various sources, including video, audio, internet activity, GPS data, sensors, text messages, and more. Moreover, structured data is well-organized and follows a specified format, whereas unstructured data requires some sorting and processing.

  • Velocity

Businesses can obtain real-time data because of the "always-on" attitude and the rising interconnection of the digital realm. Advanced analytic systems can collect, analyze, and act on data in the blink of an eye, rather than having it sit around collecting dust.

The Role of Big Data in FinTech

The FinTech sector is anticipated to reach $28529.29 billion by 2025, increasing at a CAGR of 6%. The growing use of mobile devices has resulted in a rise in clients utilizing FinTech goods and services. In addition, users have exponential growth in data created per second as these interactions and use habits are stored over time.

Most FinTech companies face both obstacles and possibilities as a result of this.

You're putting yourself up for failure if your FinTech company can't leverage this massive quantity of data to serve your clients better. Quality assurance services like advanced data analytics and intelligent data discovery skills, on the other hand, may work wonders for your FinTech companies.

How big data helps fintech companies to serve their customers better

There are various areas where financial technology and big data come together to generate a perfect synergy that benefits both service providers and customers.

Big Data Processing

  • The chore of tracking users' activity in online and mobile applications for later analysis is made easier with real-time data processing.
  • Data mining enables a deeper dive into vast amounts of unstructured data stored in data lakes, allowing for the extraction of relevant insights.
  • Data visualization on customizable dashboards allows you to see the most important aspects of key business processes clearly and concisely.
  • Stream computing systems combine data streams from apps and IoT networks, analyze them in real-time, and deliver clean, usable data sets.

Data analytics

  • Artificial intelligence and machine learning approaches are used in advanced risk analysis algorithms to improve accuracy.
  • Big data analysis and AI/ML algorithms are used in data-driven decision-making to advise the best course of action in complicated scenarios.
  • Service delays are reduced, and the customer experience is improved when business processes are automated and intelligent decision-making is used.
  • Continuous social analytics helps marketing teams maintain a high CSAT (customer satisfaction) score by monitoring brand image in social networks and detecting concerns early on.

Data security

  • Big data assists in the creation of highly comprehensive customer profiles that are used to ensure the security of users' identities and personal information, which is particularly crucial for financial institutions.
  • Artificial intelligence (AI) and big data are increasingly being utilized to detect suspicious activity and prevent fraud and intricate hacking efforts under quality assurance services.

In theory, each contemporary digital bank or financial technology company's data management framework comprises these and other related practices and techniques. This is because extensive data collecting and 360-degree data analysis are no longer considered a passing novelty in computer science. They are, on the contrary, critical components of every organization dealing with money and attempting to be effective in a highly competitive environment.

Do you want to know how AI and big data may help you enforce your financial company idea? Then, TransformHub can help you realize the full potential under the blend of this revolution.

Applications and Use Cases of (Big) Data Analytics in FinTech 

Big data applications in online banks, FinTech startups, and existing financial institutions are not confined to transforming data into useful information. In the FinTech industry, big data analytics may be used for various applications. Let's look at a few examples of combining big data analytics with contemporary technology that might assist your FinTech company.

  • 1. Big data in insurance 

Insurance firms have traditionally relied on statistical and demographic data that is no longer relevant. As a result, they were forced to price their insurance manually, resulting in significant financial losses. Modern insurance companies make extensive use of big data to generate low-risk insurance packages.

For example, many vehicle insurance firms analyze risk and change policy offerings based on on-the-ground data and road accident statistics.

  • 2. Big data analytics in digital payments 

Big data and machine learning are widely used in FinTech for fraud detection and security control. The merging of payment processing and sales, where customers may acquire loans in seconds, is the most current digital transformation trend in online payments. The online method integrates machine learning algorithms and big data to assess risk and the amount of credit accessible to the consumer. Various FinTech businesses have seen improved conversion rates as a result of this.

Various financial payment firms, for example, target young professionals and assist them in successfully and securely investing, saving, and managing their finances. This move is part of quality assurance services. 

  • 3. Big data technology in real estate 

Real estate requires constant market monitoring, dynamic pricing, and detailed property information to sell more with a more significant profit. This entails gathering data from various sources and analyzing it to provide the best possible offerings to the relevant clients. As a result, big data analytics may assist you in reducing the likelihood of consumers defaulting on payments.

  • 4. Big data in lending 

Microfinance and other lending organizations may employ AI models and Big data analytics to make credit loans more accessible to a broader audience. This provides you with two advantages. First, your FinTech company would encourage financial inclusion among your target population, and second, loan adoption would increase your FinTech company's income.

Furthermore, the increased availability of rapid loans stimulates the economy and aids the dynamic growth of other enterprises.

  • 5. Predictive data analytics in wealth management

Predictive data analytics might be utilized in wealth management to bridge the gap between corporate expectations and client expectations. The valuable client information may be used to generate more detailed customer profiles. Data also aids customer retention; for example, you may create personalized offers for various demographics ranging from student loans to retirement plans.

Not only that, but predictive data analytics under quality assurance services helps with consumer segmentation, delivery of more customer-centric goods and services, and operational optimization. This is most likely why businesses of all sizes and scales use data analytics services to improve their complete client experience.

Let's look at why you should outsource data management services now that we know how significant data analytics insights may be to your FinTech organization.

Big Data in Fintech is The "New Big Thing"

The importance of client data analysis in the financial services business cannot be emphasized. Only outsourcing big data analytics solutions can keep FinTech companies "in the game" as the industry becomes increasingly competitive.

Many business owners are outsourcing their business analytics to acquire the data and advice they want while focusing on other aspects of their organization. Having experts undertake the heavy work of statistical studies and then develop ideas and suggestions is less expensive and more efficient.

These business use examples demonstrate how incorporating data analytics into customer service solutions may help your FinTech product improve.

How can TransformHub Help You with FinTech Data Analytics Solutions?

With the growing popularity of extensive data analytics outsourcing in FinTech, it's reasonable to assume that it will soon become the lifeblood of major companies and businesses. As a result, your FinTech company will be more competent at offering clients seamless service across all channels using big data. TransformHub has successfully provided data management and quality assurance services and solutions after several years of development.

We can provide tailored data analytic services for your FinTech company, from analytical consultancy to data visualization. To learn more, So give us a call at +65 31384660 or send us an email at