Big Data Fintech and Healthtech Applications in the Real World

Big Data Fintech and Healthtech Applications in the Real World
Reading Time: 6 minutes

In this article:

  • A look at how ethnographic data is applied in the financial services industry to predict market behavior
  • Exploring data security challenges that have arisen in the fintech industry due to the pandemic
  • Examples of using software for healthcare and real-time electronic health records to deliver better patient care
  • Examples of AI and machine learning driving innovation and combating bias in healthtech

How Big Data help Fintech and Healthtech

Today, the amount of data being created on a daily basis is so vast it almost eludes comprehension. The numbers are truly astonishing. Just take a look: 

  • 1.7MB of data is created every second by every person during 2020
  • In the last two years alone, 90% of the world’s data has been created
  • 2.5 quintillion bytes of data are produced by humans every day
  • 95 million photos and videos are shared every day on Instagram
  • By the end of 2020, 44 zettabytes will make up the entire digital universe
  • Every day, 306.4 billion emails are sent, and 5 million Tweets are made

Source: TechJury

This trend shows no signs of slowing down, especially as we’ve come to increasingly rely on digital platforms during the pandemic. This will continue to affect industry and society, as vast quantities of data are leveraged to change the way we do business and make decisions. In this article, we’ll explore the tangible effects that Big Data has had in the real world, through the lens of two specific industries: fintech and healthtech. Though Big Data has brought about changes in almost every industry, fintech and healthtech have seen a particularly dramatic effect, given the changes associated with COVID-19. Let’s take a look at some of these changes, and how players within these industries are using Big Data to adapt in the face of unprecedented flux.



Big Data Fintech Analytics

The financial services sector generates vast quantities of data daily through customer transactions, global trade and stock market data, leveraging it to derive increasingly accurate insights and predictive models for consumer and market behavior. Fintech was one of the earliest adopters of Big Data analytics. IBMs Institute for Business Value along with Oxford University’s Saïd Business School conducted a study back in 2012 in which 71% of financial firms reported that leveraging Big Data and Analytics created a competitive edge for their organizations. 

Financial firms have also become experts at using ethnographic data from their customers to study and predict behavior. Their data scientists have established some remarkable and unexpected correlations, like using education levels of a customer’s social media contacts, and their patterns of mobile activity, to determine how likely that customer is to default on a loan. In another trial, one of the main credit scorers, FICO, established a connection between statuses users shared on Facebook and their creditworthiness (Economist).

Regulators have taken note of such activities.  In the UK, the FCA (Financial Conduct Authority) said it was worried that specific clients could be outpriced of insurance through these practices. However, the finance and insurance industry maintains that the more data it has about its customers, the better it can tailor products and services to different individuals, and offer better protection to their customers. For example, banks can protect customers against potential fraud if they track their location through mobile phones.

One of the biggest challenges faced by fintech during the pandemic has been data security. The industry holds a lot of sensitive data, like incomes, investment information and credit card details, and as such, it has historically been susceptible to data breaches and hacks. Moving its IT infrastructure from physical office to remote, and even moving to the cloud without taking time to properly focus on security, has created additional vulnerabilities around data. Fortunately, there are some basic measures that fintech companies can implement to prevent future data breaches, and most have taken serious steps to address the threat. The measures include improving cloud security by adding a cloud data loss prevention (DLP) to reduce the risk of data exfiltration, encrypting sensitive data, adding multi-factor authentication, and implementing internal education protocols within their organizations about data security.

Along with security challenges, however, the pandemic has delivered multiple opportunities for innovation, by exponentially increasing the demand for touchless and remote services. In response, big data fintech companies have developed a whole host of products and services, racing to develop new tools that allow customers to complete a variety of actions remotely, like open new accounts, apply for loans, and use digital signatures, removing the need for people to attend branches in person. These industry-changing innovations are here to stay, and will continue to shape our behavior beyond the pandemic.


Healthtech and software for healthcare

The applications for Big Data in the software for healthcare sector are many.  By leveraging historical data sets found in Big Data, predictive analytics and modeling can be applied to Electronic Patient Health Records (EPHR), improving the likelihood of catching life-threatening diseases sooner, something that has become more crucial than ever over the past year. By using real-time EPHR data such as heart rate, respiratory rate, temperature, and white blood cell count, diseases can be identified earlier, and care can shift further from management and treatment, to prevention. 

The healthtech industry was arguably the most affected by the pandemic, seeing demand for its services skyrocket almost overnight, and having to adapt quickly, both in terms of responding to increased volume, and doing so in a manner that’s still secure and compliant with the industry’s strict regulations.

One of the big success stories to come out of software for healthcare over the last several months is how it successfully leveraged AI in using Big Data during the pandemic. A recent notable example is MIT developing an AI model that accurately identifies asymptomatic COVID patients just by the sound of their cough. But beyond that catchy and undoubtedly cool story, healthtech has taken some significant and meaningful strides to advance Big Data in its practice.

One area they’ve worked on improving is training data. Obtaining high quality training data is difficult and time-intensive, making it a challenge to build effective models. Without it, however, algorithms will be trained on erroneous or incomplete data, and yield bad results that culminate in poor quality care. Researchers across the industry have been aware of this issue for a long time, and have been working on a number of solutions to overcome the bias challenge. Last year, MIT researchers developed an automated system that gathers more data from images used to train machine learning models, synthesizing a massive dataset of distinct training examples. The dataset can be used to improve the training of machine learning models, enabling them to detect anatomical structures in new scans.

The industry has also been working hard to eliminate bias in data. With current inequities faced by underserved populations when it comes to accessing and receiving care, developing bias-free algorithms out of existing datasets has been challenging. We know that racial bias is prevalent in predictive analytics platforms, which was confirmed by this University of California Berkeley study. To remove bias from analytics tools, developers must work with experts and end-users to understand what clinical metrics are important to providers. For example, researchers at Columbia University have developed a machine learning algorithm that identifies and predicts differences in adverse drug side effects between men and women by analyzing 50 years’ worth of reports in an FDA database. Acknowledging that adverse side effects can differ by gender is an example of actively removing bias from Big Data sets that will be used to teach future algorithms and improve patient care for all.



The pandemic has brought about a number of rapid systemic changes to several industries working with Big Data. But pandemic or not, as Big Data grows and data scientists identify new uses for datasets, industries will continue to be further disrupted, and innovative solutions to complex business problems will continue to be developed. 

At Growth Acceleration Partners, we have extensive expertise in many verticals. We can provide your organization with resources in the following areas:

  • Software for healthcare
  • Software development for cloud and mobile applications
  • Data analytics and data science
  • Information systems
  • Machine learning and artificial intelligence
  • Predictive modeling
  • QA Automation

If you have any further questions regarding our services, please reach out to us.

About Paul Brownell

Paul Brownell

As Chief Technology Officer, Paul is responsible for GAP’s software lifecycle delivery processes, from concept through execution and maintenance. Paul is an Agile evangelist, focused on Agile transformation, Agile environments, and scaling distributed teams in accordance with the Agile SAFe framework. Paul’s favorite pastimes are skydiving and building high-performance PCs. You can connect with Paul on LinkedIn, or you can send him an email.

GAP offers a whole host of technology solutions including software for healthcare, cloud migration and application modernization services. For more information contact us.