Big Data for FinTech and InsureTech

InsureTech – This post came out of experience from my visit to a large retail store situated in Harare on last week Sunday. The marketplace was bustling with excitement as the end of the month approached, which is when people usually get paid. While I was grocery shopping, I obtained some necessary items for my own consumption. As I waited in line to pay for and pick up my stuff, I observed that the vast majority of customers preferred using either credit cards or their mobile wallets to complete their payments.

The digital payment line was progressing swiftly in contrast to the cash payment line, which had only a few individuals carrying one or two minor purchases. The notion that crossed my mind upon observing the entire scenario was, “What other occurrences are taking place beside the utilization of mobile and plastic payments?” The generation of significant amounts of data, often referred to as “big data, was apparent.

Introduction – InsureTech

Big data poses a significant challenge unless appropriate security and encryption measures are implemented. Let me provide a concise explanation of what “big data” means before we proceed any further.

Undoubtedly, many of us are familiar with the definition of “big data,” an expression that refers to an immense quantity of electronic information. Big data factories should use a reliable and privacy-protected approach to manage and maintain expensive, confidential, and critical assets, i.e., customer data. This data is unorganized and unstructured because it was captured from different sources. So it is difficult to analyze. For instance, cardholder data should be managed in a highly secured data vault using multiple encryption keys with split knowledge and dual or triple control.

In today’s time, with the help of artificial intelligence, data security has taken another angle where actual data is mapped to dummy data and actual data never gets into the internet black hole as this data store cannot be connected to or via the internet but remains in the background.

A data thief would not be able to make use of information stolen from a database without also having multiple levels of keys. Big data presents a tremendous opportunity for enterprises across multiple industries, especially in the tsunami-like data flow industry of “payments.”. FinTech, InsureTech, and MedTech are major data-generating industries, i.e., massive groups of factories. According to some data from Google, it shows technology-based innovative insurance companies pay $0.60–$0.65 of each dollar in claims, with the rest covering costs of administration, marketing, and reinsurance.

InsureTech Data Security – Data Intelligence

The next questions were “Who owns this data?”, “What is the use of this data?” and “How secure is this data?”. My payment data, with all my sensitive information, is it secured and in safe hands? What about the privacy of my sensitive information? Thousands of questions started spinning in my head. There is massive scope for data security. This presents a significant opportunity for disruption. Improvements in technology, which are anyway happening every day without demand, will bring a reduction in each of these cost items.

More startups are coming in to disrupt this massive and antiquated industry. Artificial intelligence helps reduce underwriting risk using big data and machine learning; it also offers secure data migration to the secured data vaults.

Automating policy administration and claims pay-out to bring a big smile to customers’ faces and improve distribution via marketplaces The wide variety of data volumes generated by FinTech, InsureTech, and MedTech is inspiring for data scientists (I simply love this and would feel very happy to play with it if I ever get access to it), executives, product managers, and marketers.

Leveraging on data from different platforms, i.e., CRM platforms, spreadsheets, enterprise planning systems, social media feeds like Facebook, Twitter, Instagram, LinkedIn, the company website feed section, any video file, and any other source Thanks to mobile devices, tracking systems, RFID, sensor networks, Internet searches, automated record keeping, video archives, e-commerce, etc., coupled with the additional information derived by analyzing all this information, which on its own creates another enormous data sets.

Data Sources

Novel sources of information generate additional data that necessitates fresh procedures for management, which subsequently introduces novel security weaknesses. The primary and most notable opportunity for malicious actors to exploit and abuse client information stems from the remote activation of payment credentials and apps, commonly referred to as over-the-air provisioning.

Generally, it pertains to the way individuals and their surroundings interact in a contagious manner. As the world moves towards advanced modes of digital payments and integrated financial services, it is imperative for all businesses to adopt progressive changes to ensure their expansion and sustainability. This goes beyond a solitary data source emanating from a solitary payment system.

In order to fully reap the benefits of big data, corporations must utilize diverse types of data, encompassing both structured data from a variety of heterogeneous applications and databases as well as unstructured data in multiple file formats.

The data warehouse can help in making more informed decisions, planning for growth, discovering and designing a roadmap for new opportunities, optimizing existing ones, and delivering breakthrough innovations. Today, we don’t know where new data sources may come from tomorrow, but we can have some certainty that there will be more to contend with and more diversity to accommodate.

Big Data Factories and Data Intelligence

In today’s times, there has been an expansion in the quantity of large-scale data companies that are leveraging data intelligence and advanced analytics to identify novel business trends, optimize research efficacy, and acquire insights in diverse sectors such as FinTech, InfoTech, InsureTech, MedTech, and law enforcement. The aforementioned setting offers a sensory experience characterized by invigorating gusts of wind, a stimulating atmosphere, an extensive topography, and a feeling of heightened elevation atop the Data lake.

In Big data frameworks powered by Hadoop, Teradata, MongoDB, NoSQL, or another system, massive amounts of sensitive data may be managed at any given time.

As per AILabPage – The term “Big Data” pertains to a collection of data sets that are characterized by their massive volume and complex structure, leading to difficulties in their management and processing through conventional database management software and data processing applications.

Sensitive assets don’t just reside on big data nodes; they can come in the form of system logs, configuration files, error logs, and more. within its own environment itself—whether the challenges include capture, curation, storage, search, sharing, transfer, analysis, and visualization.

There exists a diverse array of potential sources of credible and impactful information, comprising identifiable personal information, credit card data, intellectual property, medical records, and other comparable data.

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Conclusion – In short, big data has transformed artificial intelligence to an almost unreasonable level. Blockchain technology could transform AI too, in its own particular ways, but for now, that’s for my next post. I guess my analysis is reasonable, but my conclusion at this time might be a bit premature. The clearing houses get real-time payment data to apply their expertise and also have a vision beyond their current rails and the pockets to support it.

Further, more data sources are added all the time. Start and grow businesses and prevent fraud by putting security before innovation. Transactions and data generated out of them will then be safe, quick, and easy.

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